presentations of covid 19

• Patient Care & Health Information
• Diseases & Conditions
• Coronavirus disease 2019 (COVID-19)

COVID-19, also called coronavirus disease 2019, is an illness caused by a virus. The virus is called severe acute respiratory syndrome coronavirus 2, or more commonly, SARS-CoV-2. It started spreading at the end of 2019 and became a pandemic disease in 2020.

• Coronavirus

Coronaviruses are a family of viruses. These viruses cause illnesses such as the common cold, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and coronavirus disease 2019 (COVID-19).

The virus that causes COVID-19 spreads most commonly through the air in tiny droplets of fluid between people in close contact. Many people with COVID-19 have no symptoms or mild illness. But for older adults and people with certain medical conditions, COVID-19 can lead to the need for care in the hospital or death.

Staying up to date on your COVID-19 vaccine helps prevent serious illness, the need for hospital care due to COVID-19 and death from COVID-19 . Other ways that may help prevent the spread of this coronavirus includes good indoor air flow, physical distancing, wearing a mask in the right setting and good hygiene.

Medicine can limit the seriousness of the viral infection. Most people recover without long-term effects, but some people have symptoms that continue for months.

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Typical COVID-19 symptoms often show up 2 to 14 days after contact with the virus.

Symptoms can include:

• Shortness of breath.
• Loss of taste or smell.
• Extreme tiredness, called fatigue.
• Digestive symptoms such as upset stomach, vomiting or loose stools, called diarrhea.
• Pain, such as headaches and body or muscle aches.
• Fever or chills.
• Cold-like symptoms such as congestion, runny nose or sore throat.

People may only have a few symptoms or none. People who have no symptoms but test positive for COVID-19 are called asymptomatic. For example, many children who test positive don't have symptoms of COVID-19 illness. People who go on to have symptoms are considered presymptomatic. Both groups can still spread COVID-19 to others.

Some people may have symptoms that get worse about 7 to 14 days after symptoms start.

Most people with COVID-19 have mild to moderate symptoms. But COVID-19 can cause serious medical complications and lead to death. Older adults or people who already have medical conditions are at greater risk of serious illness.

COVID-19 may be a mild, moderate, severe or critical illness.

• In broad terms, mild COVID-19 doesn't affect the ability of the lungs to get oxygen to the body.
• In moderate COVID-19 illness, the lungs also work properly but there are signs that the infection is deep in the lungs.
• Severe COVID-19 means that the lungs don't work correctly, and the person needs oxygen and other medical help in the hospital.
• Critical COVID-19 illness means the lung and breathing system, called the respiratory system, has failed and there is damage throughout the body.

Rarely, people who catch the coronavirus can develop a group of symptoms linked to inflamed organs or tissues. The illness is called multisystem inflammatory syndrome. When children have this illness, it is called multisystem inflammatory syndrome in children, shortened to MIS -C. In adults, the name is MIS -A.

When to see a doctor

Contact a healthcare professional if you test positive for COVID-19 . If you have symptoms and need to test for COVID-19 , or you've been exposed to someone with COVID-19 , a healthcare professional can help.

People who are at high risk of serious illness may get medicine to block the spread of the COVID-19 virus in the body. Or your healthcare team may plan regular checks to monitor your health.

Get emergency help right away for any of these symptoms:

• Can't catch your breath or have problems breathing.
• Skin, lips or nail beds that are pale, gray or blue.
• New confusion.
• Trouble staying awake or waking up.
• Chest pain or pressure that is constant.

This list doesn't include every emergency symptom. If you or a person you're taking care of has symptoms that worry you, get help. Let the healthcare team know about a positive test for COVID-19 or symptoms of the illness.

More Information

• COVID-19 vs. flu: Similarities and differences
• COVID-19, cold, allergies and the flu
• Unusual symptoms of coronavirus

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COVID-19 is caused by infection with the severe acute respiratory syndrome coronavirus 2, also called SARS-CoV-2.

The coronavirus spreads mainly from person to person, even from someone who is infected but has no symptoms. When people with COVID-19 cough, sneeze, breathe, sing or talk, their breath may be infected with the COVID-19 virus.

The coronavirus carried by a person's breath can land directly on the face of a nearby person, after a sneeze or cough, for example. The droplets or particles the infected person breathes out could possibly be breathed in by other people if they are close together or in areas with low air flow. And a person may touch a surface that has respiratory droplets and then touch their face with hands that have the coronavirus on them.

It's possible to get COVID-19 more than once.

• Over time, the body's defense against the COVID-19 virus can fade.
• A person may be exposed to so much of the virus that it breaks through their immune defense.
• As a virus infects a group of people, the virus copies itself. During this process, the genetic code can randomly change in each copy. The changes are called mutations. If the coronavirus that causes COVID-19 changes in ways that make previous infections or vaccination less effective at preventing infection, people can get sick again.

The virus that causes COVID-19 can infect some pets. Cats, dogs, hamsters and ferrets have caught this coronavirus and had symptoms. It's rare for a person to get COVID-19 from a pet.

Risk factors

The main risk factors for COVID-19 are:

• If someone you live with has COVID-19 .
• If you spend time in places with poor air flow and a higher number of people when the virus is spreading.
• If you spend more than 30 minutes in close contact with someone who has COVID-19 .

Many factors affect your risk of catching the virus that causes COVID-19 . How long you are in contact, if the space has good air flow and your activities all affect the risk. Also, if you or others wear masks, if someone has COVID-19 symptoms and how close you are affects your risk. Close contact includes sitting and talking next to one another, for example, or sharing a car or bedroom.

It seems to be rare for people to catch the virus that causes COVID-19 from an infected surface. While the virus is shed in waste, called stool, COVID-19 infection from places such as a public bathroom is not common.

Serious COVID-19 illness risk factors

Some people are at a higher risk of serious COVID-19 illness than others. This includes people age 65 and older as well as babies younger than 6 months. Those age groups have the highest risk of needing hospital care for COVID-19 .

Not every risk factor for serious COVID-19 illness is known. People of all ages who have no other medical issues have needed hospital care for COVID-19 .

Known risk factors for serious illness include people who have not gotten a COVID-19 vaccine. Serious illness also is a higher risk for people who have:

• Sickle cell disease or thalassemia.
• Serious heart diseases and possibly high blood pressure.
• Chronic kidney, liver or lung diseases.

People with dementia or Alzheimer's also are at higher risk, as are people with brain and nervous system conditions such as stroke. Smoking increases the risk of serious COVID-19 illness. And people with a body mass index in the overweight category or obese category may have a higher risk as well.

Other medical conditions that may raise the risk of serious illness from COVID-19 include:

• Cancer or a history of cancer.
• Type 1 or type 2 diabetes.
• Weakened immune system from solid organ transplants or bone marrow transplants, some medicines, or HIV .

This list is not complete. Factors linked to a health issue may raise the risk of serious COVID-19 illness too. Examples are a medical condition where people live in a group home, or lack of access to medical care. Also, people with more than one health issue, or people of older age who also have health issues have a higher chance of severe illness.

Related information

• COVID-19: Who's at higher risk of serious symptoms? - Related information COVID-19: Who's at higher risk of serious symptoms?

Complications

Complications of COVID-19 include long-term loss of taste and smell, skin rashes, and sores. The illness can cause trouble breathing or pneumonia. Medical issues a person already manages may get worse.

Complications of severe COVID-19 illness can include:

• Acute respiratory distress syndrome, when the body's organs do not get enough oxygen.
• Shock caused by the infection or heart problems.
• Overreaction of the immune system, called the inflammatory response.
• Blood clots.
• Kidney injury.

Post-COVID-19 syndrome

After a COVID-19 infection, some people report that symptoms continue for months, or they develop new symptoms. This syndrome has often been called long COVID, or post- COVID-19 . You might hear it called long haul COVID-19 , post-COVID conditions or PASC. That's short for post-acute sequelae of SARS -CoV-2.

Other infections, such as the flu and polio, can lead to long-term illness. But the virus that causes COVID-19 has only been studied since it began to spread in 2019. So, research into the specific effects of long-term COVID-19 symptoms continues.

Researchers do think that post- COVID-19 syndrome can happen after an illness of any severity.

Getting a COVID-19 vaccine may help prevent post- COVID-19 syndrome.

• Long-term effects of COVID-19

The Centers for Disease Control and Prevention (CDC) recommends a COVID-19 vaccine for everyone age 6 months and older. The COVID-19 vaccine can lower the risk of death or serious illness caused by COVID-19.

The COVID-19 vaccines available in the United States are:

2023-2024 Pfizer-BioNTech COVID-19 vaccine. This vaccine is available for people age 6 months and older.

Among people with a typical immune system:

• Children age 6 months up to age 4 years are up to date after three doses of a Pfizer-BioNTech COVID-19 vaccine.
• People age 5 and older are up to date after one Pfizer-BioNTech COVID-19 vaccine.
• For people who have not had a 2023-2024 COVID-19 vaccination, the CDC recommends getting an additional shot of that updated vaccine.

2023-2024 Moderna COVID-19 vaccine. This vaccine is available for people age 6 months and older.

• Children ages 6 months up to age 4 are up to date if they've had two doses of a Moderna COVID-19 vaccine.
• People age 5 and older are up to date with one Moderna COVID-19 vaccine.

2023-2024 Novavax COVID-19 vaccine. This vaccine is available for people age 12 years and older.

• People age 12 years and older are up to date if they've had two doses of a Novavax COVID-19 vaccine.

In general, people age 5 and older with typical immune systems can get any vaccine approved or authorized for their age. They usually don't need to get the same vaccine each time.

Some people should get all their vaccine doses from the same vaccine maker, including:

• Children ages 6 months to 4 years.
• People age 5 years and older with weakened immune systems.
• People age 12 and older who have had one shot of the Novavax vaccine should get the second Novavax shot in the two-dose series.

Talk to your healthcare professional if you have any questions about the vaccines for you or your child. Your healthcare team can help you if:

• The vaccine you or your child got earlier isn't available.
• You don't know which vaccine you or your child received.
• You or your child started a vaccine series but couldn't finish it due to side effects.

People with weakened immune systems

Your healthcare team may suggest added doses of COVID-19 vaccine if you have a moderately or seriously weakened immune system. The FDA has also authorized the monoclonal antibody pemivibart (Pemgarda) to prevent COVID-19 in some people with weakened immune systems.

Control the spread of infection

In addition to vaccination, there are other ways to stop the spread of the virus that causes COVID-19 .

If you are at a higher risk of serious illness, talk to your healthcare professional about how best to protect yourself. Know what to do if you get sick so you can quickly start treatment.

If you feel ill or have COVID-19 , stay home and away from others, including pets, if possible. Avoid sharing household items such as dishes or towels if you're sick.

In general, make it a habit to:

• Test for COVID-19 . If you have symptoms of COVID-19 test for the infection. Or test five days after you came in contact with the virus.
• Help from afar. Avoid close contact with anyone who is sick or has symptoms, if possible.
• Wash your hands. Wash your hands well and often with soap and water for at least 20 seconds. Or use an alcohol-based hand sanitizer with at least 60% alcohol.
• Cover your coughs and sneezes. Cough or sneeze into a tissue or your elbow. Then wash your hands.
• Clean and disinfect high-touch surfaces. For example, clean doorknobs, light switches, electronics and counters regularly.

Try to spread out in crowded public areas, especially in places with poor airflow. This is important if you have a higher risk of serious illness.

The CDC recommends that people wear a mask in indoor public spaces if you're in an area with a high number of people with COVID-19 in the hospital. They suggest wearing the most protective mask possible that you'll wear regularly, that fits well and is comfortable.

• COVID-19 vaccines: Get the facts - Related information COVID-19 vaccines: Get the facts
• Comparing the differences between COVID-19 vaccines - Related information Comparing the differences between COVID-19 vaccines
• Different types of COVID-19 vaccines: How they work - Related information Different types of COVID-19 vaccines: How they work
• Debunking COVID-19 myths - Related information Debunking COVID-19 myths

Travel and COVID-19

Travel brings people together from areas where illnesses may be at higher levels. Masks can help slow the spread of respiratory diseases in general, including COVID-19 . Masks help the most in places with low air flow and where you are in close contact with other people. Also, masks can help if the places you travel to or through have a high level of illness.

Masking is especially important if you or a companion have a high risk of serious illness from COVID-19 .

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• Goldman L, et al., eds. COVID-19: Epidemiology, clinical manifestations, diagnosis, community prevention, and prognosis. In: Goldman-Cecil Medicine. 27th ed. Elsevier; 2024. https://www.clinicalkey.com. Accessed Dec. 17, 2023.
• Coronavirus disease 2019 (COVID-19) treatment guidelines. National Institutes of Health. https://www.covid19treatmentguidelines.nih.gov/. Accessed Dec. 18, 2023.
• AskMayoExpert. COVID-19: Testing, symptoms. Mayo Clinic; Nov. 2, 2023.
• Symptoms of COVID-19. Centers for Disease Control and Preventions. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html. Accessed Dec. 20, 2023.
• AskMayoExpert. COVID-19: Outpatient management. Mayo Clinic; Oct. 10, 2023.
• Morris SB, et al. Case series of multisystem inflammatory syndrome in adults associated with SARS-CoV-2 infection — United Kingdom and United States, March-August 2020. MMWR. Morbidity and Mortality Weekly Report 2020;69:1450. DOI: http://dx.doi.org/10.15585/mmwr.mm6940e1external icon.
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• People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed Jan. 10, 2024.
• Factors that affect your risk of getting very sick from COVID-19. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/your-health/risks-getting-very-sick.html. Accessed Jan. 10, 2024.
• Regan JJ, et al. Use of Updated COVID-19 Vaccines 2023-2024 Formula for Persons Aged ≥6 Months: Recommendations of the Advisory Committee on Immunization Practices—United States, September 2023. MMWR. Morbidity and Mortality Weekly Report 2023; 72:1140–1146. DOI: http://dx.doi.org/10.15585/mmwr.mm7242e1.
• Long COVID or post-COVID conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html. Accessed Jan. 10, 2024.
• Stay up to date with your vaccines. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html. Accessed Jan. 10, 2024.
• Interim clinical considerations for use of COVID-19 vaccines currently approved or authorized in the United States. Centers for Disease Control and Prevention. https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html#CoV-19-vaccination. Accessed Jan. 10, 2024.
• Use and care of masks. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/about-face-coverings.html. Accessed Jan. 10, 2024.
• How to protect yourself and others. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html. Accessed Jan. 10, 2024.
• People who are immunocompromised. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-who-are-immunocompromised.html. Accessed Jan. 10, 2024.
• Masking during travel. Centers for Disease Control and Prevention. https://wwwnc.cdc.gov/travel/page/masks. Accessed Jan. 10, 2024.
• AskMayoExpert. COVID-19: Testing. Mayo Clinic. 2023.
• COVID-19 test basics. U.S. Food and Drug Administration. https://www.fda.gov/consumers/consumer-updates/covid-19-test-basics. Accessed Jan. 11, 2024.
• At-home COVID-19 antigen tests — Take steps to reduce your risk of false negative results: FDA safety communication. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/safety-communications/home-covid-19-antigen-tests-take-steps-reduce-your-risk-false-negative-results-fda-safety. Accessed Jan. 11, 2024.
• Interim clinical considerations for COVID-19 treatment in outpatients. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/outpatient-treatment-overview.html. Accessed Jan. 11, 2024.
• Know your treatment options for COVID-19. U.S. Food and Drug Administration. https://www.fda.gov/consumers/consumer-updates/know-your-treatment-options-covid-19. Accessed Jan. 11, 2024.
• AskMayoExpert. COVID:19 Drug regimens and other treatment options. Mayo Clinic. 2023.
• Preventing spread of respiratory viruses when you're sick. Centers for Disease Control and Prevention. https://www.cdc.gov/respiratory-viruses/prevention/precautions-when-sick.html. Accessed March 5, 2024.
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INTRODUCTION

This topic will discuss the clinical features of COVID-19. The epidemiology, virology, prevention, and diagnosis of COVID-19 are discussed elsewhere. (See "COVID-19: Epidemiology, virology, and prevention" and "COVID-19: Diagnosis" .)

The evaluation and management of COVID-19 is also discussed in detail elsewhere:

● (See "COVID-19: Evaluation of adults with acute illness in the outpatient setting" and "COVID-19: Management of adults with acute illness in the outpatient setting" .)

● (See "COVID-19: Management in hospitalized adults" .)

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Coronaviruses are a large family of viruses that are known to cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS).

A novel coronavirus (COVID-19) was identified in 2019 in Wuhan, China. This is a new coronavirus that has not been previously identified in humans.

This course provides a general introduction to COVID-19 and emerging respiratory viruses and is intended for public health professionals, incident managers and personnel working for the United Nations, international organizations and NGOs.

As the official disease name was established after material creation, any mention of nCoV refers to COVID-19, the infectious disease caused by the most recently discovered coronavirus.

Please note that the content of this course is currently being revised to reflect the most recent guidance. You can find updated information on certain COVID-19-related topics in the following courses: Vaccination: COVID-19 vaccines channel IPC measures: IPC for COVID-19 Antigen rapid diagnostic testing: 1) SARS-CoV-2 antigen rapid diagnostic testing ; 2) Key considerations for SARS-CoV-2 antigen RDT implementation

Please note: These materials were last updated on 16/12/2020.

Course contents

Emerging respiratory viruses, including covid-19: introduction:, module 1: introduction to emerging respiratory viruses, including covid-19:, module 2: detecting emerging respiratory viruses, including covid-19: surveillance:, module 3: detecting emerging respiratory viruses, including covid-19: laboratory investigations:, module 4: risk communication :, module 5 : community engagement:, module 6: preventing and responding to an emerging respiratory virus, including covid-19:, enroll me for this course, certificate requirements.

• Gain a Record of Achievement by earning at least 80% of the maximum number of points from all graded assignments.

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• Published: 13 November 2020

Clinical presentations, laboratory and radiological findings, and treatments for 11,028 COVID-19 patients: a systematic review and meta-analysis

• Carlos K. H. Wong 1 , 2   na1 ,
• Janet Y. H. Wong 3   na1 ,
• Eric H. M. Tang 1 ,
• C. H. Au 1 &
• Abraham K. C. Wai 4  

Scientific Reports volume  10 , Article number:  19765 ( 2020 ) Cite this article

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This systematic review and meta-analysis investigated the comorbidities, symptoms, clinical characteristics and treatment of COVID-19 patients. Epidemiological studies published in 2020 (from January–March) on the clinical presentation, laboratory findings and treatments of COVID-19 patients were identified from PubMed/MEDLINE and Embase databases. Studies published in English by 27th March, 2020 with original data were included. Primary outcomes included comorbidities of COVID-19 patients, their symptoms presented on hospital admission, laboratory results, radiological outcomes, and pharmacological and in-patient treatments. 76 studies were included in this meta-analysis, accounting for a total of 11,028 COVID-19 patients in multiple countries. A random-effects model was used to aggregate estimates across eligible studies and produce meta-analytic estimates. The most common comorbidities were hypertension (18.1%, 95% CI 15.4–20.8%). The most frequently identified symptoms were fever (72.4%, 95% CI 67.2–77.7%) and cough (55.5%, 95% CI 50.7–60.3%). For pharmacological treatment, 63.9% (95% CI 52.5–75.3%), 62.4% (95% CI 47.9–76.8%) and 29.7% (95% CI 21.8–37.6%) of patients were given antibiotics, antiviral, and corticosteroid, respectively. Notably, 62.6% (95% CI 39.9–85.4%) and 20.2% (95% CI 14.6–25.9%) of in-patients received oxygen therapy and non-invasive mechanical ventilation, respectively. This meta-analysis informed healthcare providers about the timely status of characteristics and treatments of COVID-19 patients across different countries.

PROSPERO Registration Number: CRD42020176589

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Introduction.

Following the possible patient zero of coronavirus infection identified in early December 2019 1 , the Coronavirus Disease 2019 (COVID-19) has been recognized as a pandemic in mid-March 2020 2 , after the increasing global attention to the exponential growth of confirmed cases 3 . As on 29th March, 2020, around 690 thousand persons were confirmed infected, affecting 199 countries and territories around the world, in addition to 2 international conveyances: the Diamond Princess cruise ship harbored in Yokohama, Japan, and the Holland America's MS Zaandam cruise ship. Overall, more than 32 thousand died and about 146 thousand have recovered 4 .

A novel bat-origin virus, 2019 novel coronavirus, was identified by means of deep sequencing analysis. SARS-CoV-2 was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%) 5 , both of which were respectively responsible for two zoonotic human coronavirus epidemics in the early twenty-first century. Following a few initial human infections 6 , the disease could easily be transmitted to a substantial number of individuals with increased social gathering 7 and population mobility during holidays in December and January 8 . An early report has described its high infectivity 9 even before the infected becomes symptomatic 10 . These natural and social factors have potentially influenced the general progression and trajectory of the COVID-19 epidemiology.

By the end of March 2020, there have been approximately 3000 reports about COVID-19 11 . The number of COVID-19-related reports keeps growing everyday, yet it is still far from a clear picture on the spectrum of clinical conditions, transmissibility and mortality, alongside the limitation of medical reports associated with reporting in real time the evolution of an emerging pathogen in its early phase. Previous reports covered mostly the COVID-19 patients in China. With the spread of the virus to other continents, there is an imminent need to review the current knowledge on the clinical features and outcomes of the early patients, so that further research and measures on epidemic control could be developed in this epoch of the pandemic.

Search strategy and selection criteria

The systematic review was conducted according to the protocol registered in the PROSPERO database (CRD42020176589). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline throughout this review, data were identified by searches of MEDLINE, Embase and references from relevant articles using the search terms "COVID", “SARS-CoV-2”, and “novel coronavirus” (Supplementary material 1 ). Articles published in English up to 27th March, 2020 were included. National containment measures have been implemented at many countries, irrespective of lockdown, curfew, or stay-at-home orders, since the mid of March 2020 12 , except for China where imposed Hubei province lockdown at 23th January 2020, Studies with original data including original articles, short and brief communication, letters, correspondences were included. Editorials, viewpoints, infographics, commentaries, reviews, or studies without original data were excluded. Studies were also excluded if they were animal studies, modelling studies, or did not measure symptoms presentation, laboratory findings, treatment and therapeutics during hospitalization.

After the removal of duplicate records, two reviewers (CW and CHA) independently screened the eligibility criteria of study titles, abstracts and full-texts, and reference lists of the studies retrieved by the literature search. Disagreements regarding the procedures of database search, study selection and eligibility were resolved by discussion. The second and the last authors (JW and AW) verified the eligibility of included studies.

Outcomes definitions

Signs and symptoms were defined as the presentation of fever, cough, sore throat, headache, dyspnea, muscle pain, diarrhea, rhinorrhea, anosmia, and ageusia at the hospital admission 13 .

Laboratory findings included a complete blood count (white blood count, neutrophil, lymphocyte, platelet count), procalcitonin, prothrombin time, urea, and serum biochemical measurements (including electrolytes, renal-function and liver-function values, creatine kinase, lactate dehydrogenase, C-reactive protein, Erythrocyte sedimentation rate), and treatment measures (i.e. antiviral therapy, antibiotics, corticosteroid therapy, mechanical ventilation, intubation, respiratory support, and renal replacement therapy). Radiological outcomes included bilateral involvement identified and pneumonia identified by chest radiograph.

Comorbidities of patients evaluated in this study were hypertension, diabetes, chronic obstructive pulmonary disease (COPD), cardiovascular disease, chronic kidney disease, liver disease and cancer.

In-patient treatment included intensive care unit admission, oxygen therapy, non-invasive ventilation, mechanical ventilation, Extracorporeal membrane oxygenation (ECMO), renal replacement therapy, and pharmacological treatment. Use of antiviral and interferon drugs (Lopinavir/ritonavir, Ribavirin, Umifenovir, Interferon-alpha, or Interferon-beta), antibiotic drugs, corticosteroid, and inotropes (Nor-adrenaline, Adrenaline, Vasopressin, Phenylephrine, Dopamine, or Dobutamine) were considered.

Data analysis

Three authors (CW, EHMT and CHA) extracted data using a standardized spreadsheet to record the article type, country of origin, surname of first author, year of publications, sample size, demographics, comorbidities, symptoms, laboratory and radiology results, pharmacological and non-pharmacological treatments.

We aggregated estimates across 90 eligible studies to produce meta-analytic estimates using a random-effects model. For dichotomous outcomes, we estimated the proportion and its respective 95% confidence interval. For laboratory parameters as continuous outcomes, we estimated the mean and standard deviation from the median and interquartile range if the mean and standard deviation were not available from the study 14 , and calculated the mean and its respective 95% confidence intervals. Random-effect models on DerSimonian and Laird method were adopted due to the significant heterogeneity, checked by the I 2 statistics and the p values. I 2 statistic of < 25%, 25–75% and ≥ 75% is considered as low, moderate, high likelihood of heterogeneity. Pooled estimates were calculated and presented by using forest plots. Publication bias was estimated by Egger’s regression test. Funnel plots of outcomes were also presented to assess publication bias.

All statistical analyses were conducted using the STATA Version 13.0 (Statacorp, College Station, TX). The random effects model was generated by the Stata packages ‘Metaprop’ for proportions 15 and ‘Metan’ for continuous variables 16 .

The selection and screen process are presented in Fig.  1 . A total of 241 studies were found by our searching strategy (71 in PubMed and 170 in Embase). 46 records were excluded due to duplication. After screening the abstracts and titles, 100 English studies were with original data and included in full-text screening. By further excluding 10 studies with not reporting symptoms presentation, laboratory findings, treatment and therapeutics, 90 studies 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 and 76 studies with more than one COVID-19 case 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 39 , 42 , 43 , 44 , 45 , 49 , 50 , 51 , 53 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 67 , 69 , 70 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 98 , 100 , 101 , 102 , 103 , 104 , 105 were included in the current systematic review and meta-analysis respectively. 73.3% 66 studies were conducted in China. Newcastle–Ottawa Quality Assessment Scale has been used to assess study quality of each included cohort study 107 . 30% (27/90) of included studies had satisfactory or good quality. The summary of the included study is shown in Table 1 .

PRISMA flowchart reporting identification, searching and selection processes.

Of those 90 eligible studies, 11,028 COVID-19 patients were identified and included in the systematic review. More than half of patients (6336, 57.5%) were from mainland China. The pooled mean age was 45.8 (95% CI 38.6–52.5) years and 49.3% (pooled 95% CI 45.6–53.0%) of them were male.

For specific comorbidity status, the most prevalent comorbidity was hypertension (18.1%, 95% CI 15.4–20.8%), followed by cardiovascular disease (11.8%, 95% CI 9.4–14.2%) and diabetes (10.4%, 95% CI 8.7–12.1%). The pooled prevalence (95% CI) of COPD, chronic kidney disease, liver disease and cancer were 2.0% (1.3–2.7%), 5.2% (1.7–8.8%), 2.5% (1.7–3.4%) and 2.1% (1.3–2.8%) respectively. Moderate to substantial heterogeneity between reviewed studies were found, with I 2 statistics ranging from 39.4 to 95.9% ( p values between < 0.001–0.041), except for liver disease (I 2 statistics: 1.7%, p  = 0.433). Detailed results for comorbidity status are displayed in Fig.  2 .

Random-effects meta-analytic estimates for comorbidities. ( A ) Diabetes mellitus, ( B ) Hypertension, ( C ) Cardiovascular disease, ( D ) Chronic obstructive pulmonary disease, ( E ) Chronic kidney disease, ( F ) Cancer.

Regarding the symptoms presented at hospital admission, the most frequent symptoms were fever (pooled prevalence: 72.4%, 95% CI 67.2–77.7%) and cough (pooled prevalence: 55.5%, 95% CI 50.7–60.3%). Sore throat (pooled prevalence: 16.2%, 95% CI 12.7–19.7%), dyspnoea (pooled prevalence: 18.8%, 95% CI 14.7–22.8%) and muscle pain (pooled prevalence: 22.1%, 95% CI 18.6–25.5%) were also common symptoms found in COVID-19 patients, but headache (pooled prevalence: 10.5%, 95% CI 8.7–12.4%), diarrhoea (pooled prevalence: 7.9%, 95% CI 6.3–9.6%), rhinorrhoea (pooled prevalence: 9.2%, 95% CI 5.6–12.8%) were less common. However, none of the included papers reported prevalence of anosmia and ageusia. The I 2 statistics varied from 68.5 to 97.1% (all p values < 0.001), indicating a high heterogeneity exists across studies. Figure  3 shows the pooled proportion of symptoms of patients presented at hospital.

Random-effects meta-analytic estimates for presenting symptoms. ( A ) Fever, ( B ) Cough, ( C ) Dyspnoea, ( D ) Sore throat, ( E ) Muscle pain, ( F ) Headache.

For laboratory parameters, white blood cell (pooled mean: 5.31 × 10 9 /L, 95% CI 5.03–5.58 × 10 9 /L), neutrophil (pooled mean: 3.60 × 10 9 /L, 95% CI 3.31–3.89 × 10 9 /L), lymphocyte (pooled mean: 1.11 × 10 9 /L, 95% CI 1.04–1.17 × 10 9 /L), platelet count (pooled mean: 179.5 U/L, 95% CI 172.6–186.3 U/L), aspartate aminotransferase (pooled mean: 30.3 U/L, 95% CI 27.9–32.7 U/L), alanine aminotransferase (pooled mean: 27.0 U/L, 95% CI 24.4–29.6 U/L) and C-reactive protein (CRP) (pooled mean: 22.0 mg/L, 95% CI 18.3–25.8 mg/L) and D-dimer (0.93 mg/L, 95% CI 0.68–1.18 mg/L) were the common laboratory test taken for COVID-19 patients. Above results and other clinical factors are depicted in Fig.  4 . Same with the comorbidity status and symptoms, high likelihood of heterogeneity was detected by I 2 statistics for a majority of clinical parameters.

Random-effects meta-analytic estimates for laboratory parameters. ( A ) White blood cell, ( B ) Lymphocyte, ( C ) Neutrophil, ( D ) C-creative protein, ( E ) D-dimer, ( F ) Lactate dehydrogenase.

Figure  5 presents the distribution of the pharmacological treatments received for COVID-19 patients. 10.6% of patients admitted to intensive care units (pooled 95% CI 8.1–13.2%). For drug treatment, 63.9% (pooled 95% CI 52.5–75.3%), 62.4% (pooled 95% CI 47.9–76.8%) and 29.7% (pooled 95% CI 21.8–37.6%) patients used antibiotics, antiviral, and corticosteroid, respectively. 41.3% (pooled 95% CI 14.3–68.3%) and 50.7% (pooled 95% CI 9.2–92.3%) reported using Lopinavir/Ritonavir and interferon-alpha as antiviral drug treatment, respectively. Among 14 studies reporting proportion of corticosteroid used, 7 studies (50%) specified the formulation of corticosteroid as systemic corticosteroid. The remaining one specified the use of methylprednisolone. No reviewed studies reported the proportion of patients receiving Ribavirin, Interferon-beta, or inotropes.

Random-effects meta-analytic estimates for pharmacological treatments and intensive unit care at hospital. ( A ) Antiviral or interferon drugs, ( B ) Lopinavir/Ritonavir, ( C ) Interferon alpha (IFN-α), ( D ) Antibiotic drugs, ( E ) Corticosteroid, ( F ) Admission to Intensive care unit.

The prevalence of radiological outcomes and non-pharmacological treatments were presented in Fig.  6 . Radiology findings detected chest X-ray abnormalities, with 74.4% (95% CI 67.6–81.1%) of patients with bilateral involvement and 74.9% (95% CI 68.0–81.8%) of patients with viral pneumonia. 62.6% (pooled 95% CI 39.9–85.4%), 20.2% (pooled 95% CI 14.6–25.9%), 15.3% (pooled 95% CI 11.0–19.7%), 1.1% (pooled 95% CI 0.4–1.8%) and 4.7% (pooled 95% CI 2.1–7.4%) took oxygen therapy, non-invasive ventilation, mechanical ventilation, ECMO and dialysis respectively.

Random-effects meta-analytic estimates for radiological findings and non-pharmacological treatments at hospital. ( A ) Bilateral involvement, ( B ) Pneumonia, ( C ) Oxygen therapy, ( D ) Non-invasive ventilation, ( E ) Extracorporeal membrane oxygenation (ECMO), ( F ) Dialysis.

The funnel plots and results Egger’s test of comorbidity status, symptoms presented, laboratory test and treatment were presented in eFigure 1 – S5 in the Supplement. 63% (19/30) of the funnel plots (eFigure 1 – S5 ) showed significance in the Egger’s test for asymmetry, suggesting the possibility of publication bias or small-study effects caused by clinical heterogeneity.

This meta-analysis reveals the condition of global medical community responding to COVID-19 in the early phase. During the past 4 months, a new major epidemic focus of COVID-19, some without traceable origin, has been identified. Following its first identification in Wuhan, China, the virus has been rapidly spreading to Europe, North America, Asia, and the Middle East, in addition to African and Latin American countries. Three months since Wuhan CDC admitted that there was a cluster of unknown pneumonia cases related to Huanan Seafood Market and a new coronavirus was identified as the cause of the pneumonia 108 , as on 1 April, 2020, there have been 858,371 persons confirmed infected with COVID-19, affecting 202 countries and territories around the world. Although this rapid review is limited by the domination of reports from patients in China, and the patient population is of relative male dominance reflecting the gender imbalance of the Chinese population 109 , it provides essential information.

In this review, the pooled mean age was 45.8 years. Similar to the MERS-CoV pandemic 110 , middle-aged adults were the at-risk group for COVID-19 infections in the initial phase, which was different from the H1N1 influenza pandemic where children and adolescents were more frequently affected 111 . Biological differences may affect the clinical presentations of infections; however, in this review, studies examining the asymptomatic COVID-19 infections or reporting any previous infections were not included. It is suggested that another systematic review should be conducted to compare the age-specific incidence rates between the pre-pandemic and post-pandemic periods, so as to understand the pattern and spread of the disease, and tailor specific strategies in infection control.

Both sexes exhibited clinical presentations similar in symptomatology and frequency to those noted in other severe acute respiratory infections, namely influenza A H1N1 112 and SARS 113 , 114 . These generally included fever, new onset or exacerbation of cough, breathing difficulty, sore throat and muscle pain. Among critically ill patients usually presented with dyspnoea and chest tightness 22 , 29 , 39 , 72 , 141 (4.6%) of them with persistent or progressive hypoxia resulted in the requirement of intubation and mechanical ventilation 115 , while 194 (6.4%) of them required non-invasive ventilation, yielding a total of 11% of patients requiring ventilatory support, which was similar to SARS 116 .

The major comorbidities identified in this review included hypertension, cardiovascular diseases and diabetes mellitus. Meanwhile, the percentages of patients with chronic renal diseases and cancer were relatively low. These chronic conditions influencing the severity of COVID-19 had also been noted to have similar effects in other respiratory illnesses such as SARS, MERS-CoV and influenza 117 , 118 . Higher mortality had been observed among older patients and those with comorbidities.

Early diagnosis of COVID-19 was based on recognition of epidemiological linkages; the presence of typical clinical, laboratory, and radiographic features; and the exclusion of other respiratory pathogens. The case definition had initially been narrow, but was gradually broadened to allow for the detection of more cases, as milder cases and those without epidemiological links to Wuhan or other known cases had been identified 119 , 120 . Laboratory investigations among COVID-19 patients did not reveal specific characteristics—lymphopenia and elevated inflammatory markers such as CRP are some of the most common haematological and biochemical abnormalities, which had also been noticed in SARS 121 . None of these features were specific to COVID-19. Therefore, diagnosis should be confirmed by SARS-CoV–2 specific microbiological and serological studies, although initial management will continue to be based on a clinical and epidemiological assessment of the likelihood of a COVID-19 infection.

Radiology imaging often plays an important role in evaluating patients with acute respiratory distress; however, in this review, radiological findings of SARS-CoV-2 pneumonia were non-specific. Despite chest radiograph usually revealed bilateral involvement and Computed Tomography usually showed bilateral multiple ground-glass opacities or consolidation, there were also patients with normal chest radiograph, implying that chest radiograph might not have high specificity to rule out pneumonia in COVID-19.

Limited clinical data were available for asymptomatic COVID-19 infected persons. Nevertheless, asymptomatic infection could be unknowingly contagious 122 . From some of the official figures, 6.4% of 150 non-travel-related COVID-19 infections in Singapore 123 , 39.9% of cases from the Diamond Princess cruise ship in Japan 124 , and up to 78% of cases in China as extracted on April 1st, 2020, were found to be asymptomatic 122 . 76% (68/90) studies based on hospital setting which provided care and disease management to symptomatic patients had limited number of asymptomatic cases of COVID-19 infection. This review calls for further studies about clinical data of asymptomatic cases. Asymptomatic infection intensifies the challenges of isolation measures. More global reports are crucially needed to give a better picture of the spectrum of presentations among all COVID-19 infected persons. Also, public health policies including social and physical distancing, monitoring and surveillance, as well as contact tracing, are necessary to reduce the spread of COVID-19.

Concerning potential treatment regime, 62.4% of patients received antivirals or interferons (including oseltamivir, lopinavir-ritonavir, interferon alfa), while 63.9% received antibiotics (such as moxifloxacin, and ceftriaxone). In this review, around one-third of patients were given steroid, suggestive as an adjunct to IFN, or sepsis management. Interferon and antiviral agents such as ribavirin, and lopinavir-ritonavir were used during SARS, and the initial uncontrolled reports then noted resolution of fever and improvement in oxygenation and radiographic appearance 113 , 125 , 126 , without further evidence on its effectiveness. At the time of manuscript preparation, there has been no clear evidence guiding the use of antivirals 127 . Further research is needed to inform clinicians of the appropriate use of antivirals for specific groups of infected patients.

Limitations of this meta-analysis should be considered. First, a high statistical heterogeneity was found, which could be related to the highly varied sample sizes (9 to 4226 patients) and study designs. Second, variations of follow-up period may miss the event leading to heterogeneity. In fact, some patients were still hospitalized in the included studies. Third, since only a few studies had compared the comorbidities of severe and non-severe patients, sensitivity analysis and subgroup analysis were not conducted. Fourthly, the frequency and severity of signs and symptoms reported in included studies, primarily based on hospitalized COVID-19 patients were over-estimated. Moreover, different cutoffs for abnormal laboratory findings were applied across countries, and counties within the same countries. Lastly, this meta-analysis reviewed only a limited number of reports written in English, with a predominant patient population from China. This review is expected to inform clinicians of the epidemiology of COVID-19 at this early stage. A recent report estimated the number of confirmed cases in China could reach as high as 232,000 (95% CI 161,000, 359,000) with the case definition adopted in 5th Edition. In this connection, further evidence on the epidemiology is in imminent need.

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These authors contributed equally: Carlos K. H. Wong and Janet Y. H. Wong.

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Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

Carlos K. H. Wong, Eric H. M. Tang & C. H. Au

Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

Carlos K. H. Wong

School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

Janet Y. H. Wong

Emergency Medicine Unit, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China

Abraham K. C. Wai

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C.W., J.W. and A.W. contributed equally to all aspects of study design, conduct, data interpretation, and the writing of the manuscript. C.W., E.T. and C.H.A. contributed to eligibility screening, data extraction from eligible studies, and data analysis and interpretation.

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Wong, C.K.H., Wong, J.Y., Tang, E.H.M. et al. Clinical presentations, laboratory and radiological findings, and treatments for 11,028 COVID-19 patients: a systematic review and meta-analysis. Sci Rep 10 , 19765 (2020). https://doi.org/10.1038/s41598-020-74988-9

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DOI : https://doi.org/10.1038/s41598-020-74988-9

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Clinical Presentation of COVID-19: Case Series and Review of the Literature

COVID-19 infection has a broad spectrum of severity ranging from an asymptomatic form to a severe acute respiratory syndrome that requires mechanical ventilation. Starting with the description of our case series, we evaluated the clinical presentation and evolution of COVID-19. This article is addressed particularly to physicians caring for patients with COVID-19 in their clinical practice. The intent is to identify the subjects in whom the infection is most likely to evolve and the best methods of management in the early phase of infection to determine which patients should be hospitalized and which could be monitored at home. Asymptomatic patients should be followed to evaluate the appearance of symptoms. Patients with mild symptoms lasting more than a week, and without evidence of pneumonia, can be managed at home. Patients with evidence of pulmonary involvement, especially in patients over 60 years of age, and/or with a comorbidity, and/or with the presence of severe extrapulmonary manifestations, should be admitted to a hospital for careful clinical-laboratory monitoring.

1. Introduction

Coronaviruses are enveloped viruses with a positive-sense single-stranded RNA genome belonging to the Coronaviridae family, the Nidovirales order, and broadly distributed in humans and other mammals [ 1 ]. Although most human coronavirus infections are mild, the epidemics of the two beta-coronaviruses, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) [ 2 ], caused more than 10,000 cumulative cases in the past two decades, respectively in 2002 and 2012, with mortality rates of 10% for SARS-CoV and 37% for MERS-CoV [ 3 ].

Since December 2019, a new zoonotic beta-corona virus (SARS-CoV-2) has spread all over the world from Wuhan, China [ 4 ], causing a disease known as coronavirus disease (COVID-19). On 30 January 2020, the World Health Organization (WHO) declared a public health emergency [ 5 ], and the epidemic rapidly evolved into a pandemic by March 2020 [ 6 ], with a high number of cases in the European Region, especially in Italy [ 7 ].

SARS-CoV-2 is able to enter host cells through the binding between the viral structural spike (S) protein and the angiotensin-converting enzyme 2 (ACE2) receptor, present in the lung and in other tissues [ 8 ]. Viral entry is facilitated by a type 2 transmembrane serine protease, TMPRSS2, via the S protein [ 9 ]. Once binding between the S protein and receptor is established, the virus particles enter the host cell through membrane fusion and endocytosis. Inside the cell, the viral genome is released and translated into viral polypeptides, which are then cleaved into small products by proteases. The following stages include RNA synthesis by RNA-dependent RNA polymerase (RdRp), structural protein synthesis, exocytosis, and the release of the new assembled virions [ 8 ]. COVID-19 infection has a broad spectrum of severity ranging from an asymptomatic form to a severe acute respiratory syndrome that requires mechanical ventilation. The early presentation of COVID-19 infection is typically non-specific. Among symptomatic patients, about 80% showed a mild clinical course [ 10 ] characterized by a dry cough, sore throat, low-grade fever, or malaise; in 20% of cases, the general condition worsened in about seven days from the beginning of the symptoms, culminating in respiratory failure [ 11 , 12 ].

Given the wide spectrum of clinical presentation, demographic, clinical, and biochemical criteria are needed to identify the individuals most likely to progress to a severe illness. Starting from the description of our case series, we evaluated the clinical presentation and evolution of COVID-19. This article is addressed particularly to physicians caring for patients with COVID-19 in their clinical practice. The intent is to identify the subjects in whom the infection is most likely to evolve and the best methods of management in the early phase of infection to determine which patients should be hospitalized and which could be monitored at home.

We conducted comprehensive computerized literature research to identify studies analyzing diagnostic tests for COVID-19 using MEDLINE and EMBASE from January 2020 to 15 May 2020, involving both medical subject heading (MeSH) terminology and relevant keywords for search strings. The following items were used to search for the studies: “clinical characteristics,” “natural history,” “COVID-19,” and “SARS-CoV-2.” We performed this research to further the knowledge of the clinical presentation and natural history of COVID-19.

3. Statistical Analysis

In case series analysis, continuous variables were expressed as median (IQR) and compared with the Mann–Whitney U test; categorical variables were expressed as a number (%) and compared by χ 2 test or Fisher’s exact test. A p -value of <0.05 was considered to be statistically significant.

4. Case Series

We described the first 40 subjects with SARS-CoV-2 rt-PCR positive based on nasopharyngeal swabs observed from 8 March 2020 to 31 March 2020 at the Vanvitelli Covid Unit in Naples, southern Italy. Table 1 shows the demographic and clinical characteristics of the patients enrolled. The median age of patients was 52 years (IQR, 41.25–65.75), and 20 were males. Of the 40 patients, 22 (55%) had one or more coexisting medical conditions: hypertension in 17 (42%), cardiovascular disease in 8 (20%), diabetes mellitus in 4 (10%), malignancy in 4 (10%), and chronic respiratory disease in 4 (10%).

Demographic and clinical characteristics of the patients enrolled.

Of the 40 patients enrolled, 3 (7.5%) were asymptomatic, and 37 patients (92.5%) were symptomatic. All the symptoms were reported by the patients and confirmed by the physicians. Among the symptomatic patients, the most common symptoms at the onset of illness were fever (in 31 (77%)), defined as an axillary temperature of 37.5 °C or higher, fatigue (in 24 (60%)), myalgia (in 23 (58%)), lack of appetite (in 23 (58%)), and dry cough (in 15 (37%)). Other symptoms were diarrhea (in eight (20%)), anosmia (in 12 (30%)), dysgeusia/ageusia (in 13 (33%)), nausea (in three (8%)), rhinorrhea (in 2 (5%)), conjunctivitis (in 2 (5%)), and skin lesions (in 2 (5%)).

Of the 40 patients enrolled, 24 (60%) were in home isolation and 16 (40%) hospitalized. The decision of home isolation was made by the physician. The median duration from the first symptoms to hospital admission was 8.5 days (IQR 6.5–11). Table 1 shows the demographic characteristics of the two groups of patients. Compared with the 24 patients in home isolation, the 16 hospitalized patients were significantly older (median age, 69 years (IQR, 48.5–80.25) vs. 43.5 years (IQR, 39.75–55.25); p = 0.001) and had more probable underlying comorbidities (75% vs. 42%; p = 0.05). Hypertension and malignancy were more frequently detected in hospitalized patients (75% vs. 21%, p = 0.001; 25% vs. 0, p = 0.02, respectively).

Of the 24 patients in home isolation, 21 (88%) were symptomatic, while all hospitalized patients were symptomatic. In the 24 patients in home isolation, the most frequent symptoms were fever (in 17 (71%)), asthenia (in 15 (63%)), loss of appetite (in 15 (63%)), myalgia (in 15 (63%)), ageusia/dysgeusia (in 8 (33%)), and cough (in 6 (25%)). In the 16 hospitalized patients, the same symptoms were observed, but cough (in 9 (56%)), dyspnea (in 5 (33%)), and diarrhea (in 6 (38%)) were more frequently observed as clinical manifestations of SARS-CoV-2 infection. Clinical or imaging signs of pulmonary involvement were observed in 14 (88%) hospitalized patients and in none in home isolation.

To date, all patients in home isolation recovered within day 30 from the onset of symptoms, and 20 patients cleared the virus, as demonstrated by the rt-PCR negativity for SARS-CoV-2 in two nasopharyngeal swabs; among the hospitalized patients, 14 recovered and cleared the virus, while two patients died. The median time that elapsed from the first positive swab to a negative swab was 22 days (IQR, 12.25–32) for patients in home isolation and 22.5 days (IQR, 17.5–32.75) for hospitalized patients.

5. Review of Literature

5.1. clinical presentation of covid-19, 5.1.1. typical clinical manifestations.

The incubation period for SARS-CoV-2 was estimated as 2–14 days, according to publicly available data; 14 days has been chosen as the cut-off for self-quarantine [ 13 , 14 ]. Guan et al. demonstrated that the median incubation period was four days and that 95% of the 1099 hospitalized patients enrolled (median age was 47 years; 41.9% were female) developed the symptoms within 10 days [ 15 ].

Another study of 72,314 Chinese patients, conducted by the Chinese Center for Disease Control and Prevention, reported that 1% were asymptomatic cases [ 16 ], while a study with a mathematical model estimated that the percentage of subjects infected but not confirmed was 86% (95% CI: 81.5–89.8%) [ 17 ]. The transmission of COVID-19 through patients who have not yet developed symptoms was observed in many reports, although the symptoms were absent [ 18 , 19 , 20 ]. In the symptomatic subjects, early-phase fever was present in 45%, and constitutional symptoms, such as muscle or bone aches, chills, headache, sore throat, and nasal congestion, were observed [ 21 ]. The symptomatic patients may have shown a mild clinical evolution or the development of pulmonary involvement [ 22 ].

In the first group of patients with mild symptoms, nasal congestion and sputum were the most common (34.3% and 39.5% respectively), while fever was observed only in 11.6% [ 23 ]. Radiological abnormalities on computer tomography (CT) were usually not observed [ 21 , 22 , 24 , 25 , 26 ]. However, some patients who had initially mild symptoms subsequently showed a precipitous clinical deterioration that occurred approximately one week after onset of symptoms [ 26 , 27 ].

When there was lung involvement, respiratory symptoms, such as dyspnea or cough and sputum, were present [ 21 ]. In these patients, CT showed a range of features including ground-glass opacities, interstitial infiltration, crazy-paving pattern, and multiple patchy consolidations in both lung fields; in addition, vessel enlargement, thick interlobar septa, and air bronchograms were observed [ 22 ]. Clinically, in severe pneumonia, a respiratory rate of at least 30/min, SpO 2 93%, or PaO 2 /FiO 2 300 mmHg was observed [ 28 ].

As regards the biochemical data in COVID-19 patients, leuco-lymphopenia, thrombocytopenia, hypoalbuminemia, and elevated lactate dehydrogenase were observed. Most of the patients also had elevated levels of C-reactive protein; less common were elevated levels of alanine aminotransferase, aspartate aminotransferase, creatine kinase, and D-dimer [ 25 , 29 , 30 ].

5.1.2. Atypical Clinical Manifestations

The ability of the virus to bind the ubiquitous ACE2 receptors allows SARS-CoV-2 to target organs other than the lungs. ACE2 is highly expressed in absorptive intestinal epithelial cells, in the ileum and colon, as well as in cholangiocytes, hepatocytes, and esophageal cells. This explains the presence of gastrointestinal symptoms, such as diarrhea, nausea, and vomiting, and elevated liver function test results. Considering the 1602 patients enrolled in 10 different case series, 55 had diarrhea (average 5.6%, range 2–33.98%), and 72 had nausea or vomiting symptoms (average 4.49%, range 1–10%). All of these patients were predominantly male and were hospitalized [ 21 , 26 , 31 ]. A recent study found that almost half of the 99 hospitalized patients infected with COVID-19 showed liver involvement; the cause of elevated aminotransferase serum levels remains unclear, but it may be due to liver damage by COVID-19 or by antiviral drugs [ 25 ] ( Table 2 ). In our case series, eight (20%) patients had diarrhea, but only one (3%) patient had increased aminotransferase serum levels.

Studies reporting the atypical clinical presentation of COVID-19.

The cardiovascular system may also be involved in COVID-19, as ACE-2 receptors play an important role in its neuro-humoral regulation. In fact, acute cardiac injury, as demonstrated by a significant elevation of cardiac troponins, occurred in approximately 8–12% of COVID-19 patients [ 32 ], probably due to virus-related damage and/or the effect of systemic inflammation [ 33 ]. Another life-threatening cardiac involvement is fulminant myocarditis, as suggested by case reports [ 34 , 35 , 36 ]. Moreover, in a Chinese study on 138 COVID-19 patients, a prevalence of arrhythmia in 16.7% was reported [ 37 ] ( Table 2 ). In our case series, an increase in cardiac troponins was observed in four (10%) patients, arrhythmias in four (10%) patients, while no patient experienced fulminant myocarditis.

Elevated D-dimer levels, which may suggest pulmonary embolism, were observed in 36–46.4% of patients with COVID-19 [ 38 ]. However, we noted that pulmonary embolism should be confirmed by a pulmonary angio-CT. A viral infection with subsequent systemic inflammatory response probably leads to an imbalance between pro-coagulative and anti-coagulant mechanisms [ 39 ] ( Table 2 ). In our case series, elevated D-dimer levels were observed in 7 (17%) patients.

Recently, dermatological manifestations were also observed in COVID-19 patients. In a study by Recalcati et al., 20.4% of the 88 COVID-19 patients developed cutaneous manifestations during the disease [ 40 ]; it was found that most cutaneous presentations were erythematous rash (77.8%) with a few cases of urticaria (16.7%) and vesicle formation (5.6%). Although the pathogenetic mechanisms are still unclear, they may be due to a secondary consequence of infection or a primary infection of the skin itself ( Table 2 ). In our case series, only two (5%) patients had cutaneous manifestations: specifically, maculo-papular exanthema in both patients. In one patient, this extended to the trunk, root of the limbs, and scalp.

The evidence of central nervous system (CNS) involvement of COVID-19 is scanty. However, some reports suggest that SARS-CoV-2 may present neurological manifestations, such as the loss of smell and taste, ataxia, confusion, and headache [ 41 , 42 , 43 ]. A few patients showed seizure or cerebrovascular disease [ 44 ]. The hematogenous route appears to be the most likely pathway for SARS-CoV2 to reach the brain, but other routes, such as across the cribriform plate of the ethmoid bone in proximity to the olfactory bulb, should be taken into consideration in patients who exhibit loss of smell and taste [ 45 , 46 ]. In our case series, 12 (30%) patients reported hyposmia, while 13 (32.5%) reported ageusia. None of the patients complained of confusion, headache, ataxia, or convulsions ( Table 2 ).

5.2. Correlation between Clinical Presentation and Clinical Evolution

According to WHO reports, the overall fatality rate for COVID-19 is estimated at 2.3% [ 47 ], but the fatality rate has varied among studies from 1.4% to 4.3% [ 21 , 37 ]. In our case series, the overall mortality rate was 2.5%. The differences in the results among different studies may be due to the study population (symptomatic and asymptomatic, hospitalized or home isolation) as well as the differences among the studies in terms of underlying chronic diseases and median age of subjects enrolled.

Although the risk factors of COVID-19 remain unclear, many studies reported that a significant proportion of patients had underlying conditions [ 21 , 37 ]. Chen et al. showed that 50.5% of 51 COVID-19 patients had a chronic disease, namely cardiovascular and cerebrovascular (40.4%) [ 25 ]. Of 1099 patients with SARS-CoV-2 infection, Guan et al. showed that 23.2% had at least one underlying disease; hypertension was the most common (14.9%), followed by diabetes mellitus (7.4%) [ 15 ]. Another large study of COVID-19 cases of varying degrees of severity showed that hypertension was the most common underlying disease (2608, 12.8%), followed by diabetes mellitus (1102 patients, 5.3%) and cardiovascular disease (873 patients, 4.2%). All patients were predominantly male [ 47 ] ( Table 3 ).

Studies evaluating the severe clinical forms of COVID-19.

Moreover, patients with severe COVID-19 were more likely to have comorbidities than patients with non-severe diseases (37.6% vs. 20.5%) [ 21 ]. A similar trend was observed in another study of 138 hospitalized patients with SARS-CoV-2 pneumonia, in which 46.4% had comorbidities, and intensive care unit (ICU) patients were more likely to have underlying diseases compared to non-ICU patients (72.2% vs. 37.3%, p < 0.001) [ 37 ] ( Table 3 ).

Other factors associated with an elevated case fatality rate included male sex, higher age, baseline diagnosis of severe pneumonia, and delay in diagnosis [ 47 ]. The China CDC reported that patients aged over 80 years had the highest case fatality rate (14.8%) [ 47 ]. As regards the biochemical data associated with severe forms, the data are not conclusive. A procalcitonin value of more than 0.5 ng/mL was associated with a higher risk of progression to a critical illness, such as an increase during the disease in total white blood cells compared to the baseline value [ 48 , 49 ]. In our case series, the hospitalized patients were significantly older and more likely to have underlying comorbidities, especially hypertension and malignancy, than those in home isolation.

6. Conclusions

COVID-19 may present a varied clinical picture, such as asymptomatic carriage, with or without associated pneumonia, and with or without several extrapulmonary manifestations [ 50 , 51 , 52 ]. Figure 1 shows a possible management plan for patients according to their clinical presentation. Asymptomatic patients with nasopharyngeal swabs positive for SARS-CoV-2 rt-PCR should be followed for 14 days to evaluate the appearance of symptoms. Similarly, patients with mild symptoms arising after more than 10 days, and without evidence of pneumonia, can be managed at home with periodic telephone evaluation. However, patients with evidence of pulmonary involvement, especially in patients over 60 years of age, and/or with a comorbidity, and/or the presence of severe extrapulmonary manifestations, should be admitted to a hospital for careful clinical-laboratory monitoring with periodic blood gas analysis, blood count, liver and kidney function evaluation, dosage of procalcitonin, reactive protein C, and D-dimer. In these patients, it is also important to do a radiological follow-up with lung CT [ 53 ].

Management of COVID-19 patients according to the clinical presentation.

In conclusion, other studies on the natural history of COVID-19 are needed to identify the correct management of COVID-19 patients and differentiate patients with a favorable or unfavorable clinical course according to the initial clinical presentation.

Vanvitelli COVID-19 Group: Nicola Coppola, Caterina Sagnelli, Stefania De Pascalis, Maria Stanzione, Gianfranca Stornaiuolo, Angela Cascone, Salvatore Martini, Margherita Macera, Caterina Monari, Federica Calò, Andrea Bianco, Antonio Russo, Valeria Gentile, Clarissa Camaioni, Giulia De Angelis, Giulia Marino, Roberta Astorri, Ilario De Sio, Marco Niosi, Serena Borrelli, Vincenzo Carfora, Benito Celia, Maria Ceparano, Salvatore Cirillo, Maria De Luca, Marco Di Mauro, Grazia Mazzeo, Marco Migliaccio, Filiberto Fausto Mottola, Giorgio Paoli, Riccardo Ricciolino, Giorgio Spiniello, Nicoletta Verde.

Author Contributions

M.M. and N.C. were involved in review concept, design, and critical revision for important intellectual content. M.M., G.D.A., C.S., and the Vanvitelli COVID Group performed the literature search and drafted the manuscript. M.M. and N.C. were involved in the critical revision of the manuscript. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

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HEALTH COMMUNICATIONS MATTER: A COMPARATIVE CASE STUDY OF BEST PRACTICES TO COMBAT MISINFORMATION AND DISINFORMATION DURING THE COVID-19 PANDEMIC

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• Affiliation: Gillings School of Global Public Health, Department of Health Policy and Management
• On January 10, 2020, the World Health Organization (WHO) provided guidance to participating countries about outbreak management of a new disease.(1–3) On March 11, 2021, the WHO cited SARS-CoV2—the virus that causes COVID-19—as the cause of a global pandemic declaring “a public health emergency of international concern.”(1,3) The WHO Director-General would later discuss fighting dual-pandemics: COVID-19 and an “infodemic”(4) of “false or misleading information in digital and physical.(5) The purpose of this research was to understand the key elements of health communication practices in countries that managed to keep COVID-19 transmission and death rates relatively low per capita in the early stages of the pandemic. Methods: A mixed methods approach was used to identify two English-speaking, WHO member countries with populations of at least 5 million people that successfully managed COVID-19 case fatality rates of four percent or lower per capita during the first year of the pandemic. Ten key informants (KIs) from Uganda (n=5) and Singapore (n=5) were interviewed from August 2023 through January 2024. The interviews aimed to uncover key messages, communication channels, strategies, and best practices with a focus on effectively managing misinformation and disinformation and reaching disenfranchised populations. Findings: Results of key informant interviews (KIIs) revealed three themes as integral to communication success in both Uganda and Singapore during the early stages of the COVID-19 pandemic: 1) communication planning and public health preparedness should be informed by past emergencies; 2) leaning into technology through innovation and utilization of non-traditional tools and channels serves to improve country-level communication practices; and, 3) managing misinformation and disinformation through evidence-based science is essential to improving communication effectiveness and health outcomes. Implications: The United States should build upon lessons learned from the COVID-19 pandemic and other public health emergencies. The opportunity exists to engage a national evidence-based strategy. Additionally, public health practitioners should be well-versed in communication modalities to effectively reach communities including vulnerable populations. Recommendations are made to embed health communication as a foundational and core competency in public health programs. A review of how the United States can improve national health communication practices is discussed.
• Communication
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• https://doi.org/10.17615/ybr2-h487
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• Weisman, John
• Greene, Sandra B.
• Umble, Karl
• Watson-Grant, Stephanie
• Alvarez Martin, Barbara
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• University of North Carolina at Chapel Hill Graduate School

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Research Article

A simplified pneumonia severity index (PSI) for clinical outcome prediction in COVID-19

Contributed equally to this work with: Shu-Ching Chang, Gary L. Grunkemeier, Jason D. Goldman

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Current address: The Society of Thoracic Surgeons (STS), Chicago, IL, United States of America

Affiliation Providence St. Joseph Health, Portland, Oregon, United States of America

Roles Conceptualization, Investigation, Methodology, Writing – review & editing

Affiliation Division of Cardiothoracic Surgery, Oregon Health & Science University, Portland, OR, United States of America

Affiliations Division of Infectious Diseases, Swedish Medical Center, Seattle, WA, United States of America, Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA, United States of America, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, United States of America

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation ClinChoice, Portland, OR, United States of America

Roles Conceptualization, Data curation, Writing – review & editing

Affiliation Providence Heart Institute, Providence St. Joseph Health, Portland, Oregon, United States of America

Roles Conceptualization, Writing – review & editing

Affiliation Institute for Systems Biology, Seattle, Washington, United States of America

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliations Division of Medicine, Section of Infectious Diseases, Providence Regional Medical Center Everett, Everett, WA, United States of America, Washington State University Elson S. Floyd College of Medicine, Spokane, WA, United States of America, Providence Research Network, Renton, WA, United States of America

• Shu-Ching Chang, 
• Gary L. Grunkemeier, 
• Jason D. Goldman, 
• Mansen Wang, 
• Paul A. McKelvey, 
• Jennifer Hadlock, 
• Qi Wei, 
• George A. Diaz

• Published: May 21, 2024
• https://doi.org/10.1371/journal.pone.0303899
• Peer Review
• Reader Comments

The Pneumonia Score Index (PSI) was developed to estimate the risk of dying within 30 days of presentation for community-acquired pneumonia patients and is a strong predictor of 30-day mortality after COVID-19. However, three of its required 20 variables (skilled nursing home, altered mental status and pleural effusion) are not discreetly available in the electronic medical record (EMR), resulting in manual chart review for these 3 factors. The goal of this study is to compare a simplified 17-factor version (PSI-17) to the original (denoted PSI-20) in terms of prediction of 30-day mortality in COVID-19.

In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20–5/28/20 were identified to compare the predictive performance between PSI-17 and PSI-20. Correlation was assessed between PSI-17 and PSI-20, and logistic regressions were performed for 30-day mortality. The predictive abilities were compared by discrimination, calibration, and overall performance.

Based on 1,138 COVID-19 patients, the correlation between PSI-17 and PSI-20 was 0.95. Univariate logistic regression showed that PSI-17 had performance similar to PSI-20, based on AUC, ICI and Brier Score. After adjusting for confounding variables by multivariable logistic regression, PSI-17 and PSI-20 had AUCs (95% CI) of 0.85 (0.83–0.88) and 0.86 (0.84–0.89), respectively, indicating no significant difference in AUC at significance level of 0.05.

PSI-17 and PSI-20 are equally effective predictors of 30-day mortality in terms of several performance metrics. PSI-17 can be obtained without the manual chart review, which allows for automated risk calculations within an EMR. PSI-17 can be easily obtained and may be a comparable alternative to PSI-20.

Citation: Chang S-C, Grunkemeier GL, Goldman JD, Wang M, McKelvey PA, Hadlock J, et al. (2024) A simplified pneumonia severity index (PSI) for clinical outcome prediction in COVID-19. PLoS ONE 19(5): e0303899. https://doi.org/10.1371/journal.pone.0303899

Editor: Kuo-Cherh Huang, Taipei Medical University, TAIWAN

Received: January 29, 2024; Accepted: May 2, 2024; Published: May 21, 2024

Copyright: © 2024 Chang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: G.A.D reported receipt of clinical trial research support from Gilead Sciences, Regeneron, Roche, Boehringer Ingelheim, Edesa Biotech and NeuroBo Pharmaceuticals. J.D.G reports contracted research from Helix, Gilead, Eli Lilly, and Regeneron, grants from Merck (BARDA) and Gilead, and collaborative services agreements with Adaptive Biotechnologies, Monogram Biosciences and Labcorp; and serving as a consultant, speaker or advisory board member for Gilead, and Eli Lilly. All other authors have nothing to disclose.

Introduction

The Pneumonia Severity Index (PSI), developed to predict mortality in patients with bacterial pneumonia, risk stratifies which patients can safely discharge to home [ 1 ]. Recently, PSI has also been validated in viral pneumonia [ 2 ]. We and others have validated the PSI for hospitalized patients with COVID-19 pneumonia as a predictor of mortality [ 3 , 4 ]. While the prediction of mortality is important, the PSI score is most helpful for deciding if a patient can safely return home and has been utilized for this purpose extensively in Emergency Department settings. During the COVID-19 pandemic, hospitals in the US resorted to crisis standards of patient care [ 5 ] due to lack of hospital beds and other critical resources. Current standard of care for admitted patients with COVID-19 requiring low flow oxygen is a combination of remdesivir and dexamethasone [ 6 , 7 ]. Current practice for patients presenting without hypoxemia is discharge to home, possibly with enrollment into a telehealth home monitoring program [ 8 ], and provision of antiviral therapy if presenting early in disease.

The PSI score contains 20 variables; 3 of them–pleural effusion (PE), altered mental status (AMS), and skilled nursing facility (SNF)–are not easily extracted from discrete fields in the electronic medical record (EMR). In this study, we define PSI-17, the PSI score without these 3 variables, and use a data set of 1,138 COVID-19 patients from 13 hospitals in the Providence St. Joseph Health system to compare its performance to the full PSI score (PSI-20).

Since PSI-17 can be abstracted and calculated easily from the EMR, it can provide to front line caregivers the ability to identify which hypoxemic patients with COVID-19 pneumonia can safely be treated in an outpatient setting. This study aims to compare the simplified 17-factor version (PSI-17) to the original PSI-20 in terms of prediction of 30-day mortality based on 1,138 patients who were hospitalized from in the first pandemic wave with confirmed SARS-CoV-2 infection [ 3 ].

Study population

In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20–5/28/20 that met the same inclusion and exclusion criteria as in our previous study (Diaz et al., 2022) were used to evaluate the predictive performance between PSI-17 and PSI-20. Data was accessed for research purposes from the electronic medical records via the PSJH electronic data warehouse or by manual record review from July 12 th , 2020 to June 21 th , 2021, and reviewed by the study team prior to analysis. Authors had access to limited identifiable information during data collection. Detailed information about data collection and study population can be found in Diaz et al. (2022).

Statistical analysis

The components and scores of PSI-17 and PSI-20 are summarized in S1 Table . Correlation of PSI-17 and PSI-20 was assessed using Spearman’s correlation coefficient. The predictive values of the PSI indices were evaluated by logistic regression models of 30-day mortality with each index as the predictor variable. The estimates of the models were given by odds ratio (OR) with 95% confidence intervals (95% CI) for each 10 points of the PSI score. We assessed the prediction performance of PSI-17 versus PSI-20 based on discrimination, calibration, and overall performance [ 9 ].

For model discrimination, areas under the receiver operating characteristic (ROC) curve (AUC) were estimated. In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding [ 10 ]. Calibration plots with integrated calibration index (ICI), which was estimated by the weighted average absolute difference between observed and predicted probabilities [ 11 ] were used for the assessment of the agreement between observed outcomes and predictions. For overall performance, reflecting discrimination and calibration simultaneously, the Akaike Information Criterion (AIC), the Brier Score and the net reclassification index (NRI) [ 12 ] were also calculated, with 95% CI constructed using 1,000 bootstrap replications. All tests were two-sided and statistical significance was set at p < 0.05. Statistical analyses were performed using the R software, version 4.1.3 (R Core Team, 2022).

Patient consent statement

Waiver of consent for this study was approved by the Providence St. Joseph institutional review board (STUDY2020000143).

In total, 1,138 patients who were hospitalized from 2/28/20–5/28/20 with confirmed SARS-CoV-2 infection were included in the study cohort ( S2 Table ) [ 3 ]. All 17 PSI components were extracted from the EMR. SNF, AMS and PE were collected from manual chart review by study investigators. S1 Fig shows the number of patients for the 3 chart-review variables and their combinations. The median (Q1 –Q3) values for PSI-17, and PSI-20 were 72 (54–90) and 77 (55–102), respectively with a correlation coefficient of 0.95. Univariate logistic regression analyses showed that PSI-17 and PSI-20 were significant predictors of 30-day mortality, with similar effect sizes ( Table 1 ).

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https://doi.org/10.1371/journal.pone.0303899.t001

Prediction performance of PSI-17 versus PSI-20

Predictive performance was similar between PSI-17 and PSI-20 based on different metrics and measures ( Table 1 ). The OR (95% CI) was 1.51 (1.40–1.63) and 1.43 (1.35–1.52) for each 10-points of the score for PSI-17 and PSI-20, respectively. The AUC for 30-day mortality was 0.79 (0.76–0.82) for PSI-17 and was 0.81 (0.78–0.84) for PSI-20, which are acceptable and similar ( Fig 1A ), with Brier scores (scaled) for both models reflecting good overall prediction ( Table 1 ). The relationship between observed and predicted probabilities for 30-day mortality in calibration plots showed good agreement with predicted probabilities over the entire empirical range of the predicted probabilities ( Fig 2A and 2B ). The 30-day mortality by PORT Class [ 1 ] further demonstrates both PSI scores as predictors of 30-day mortality with COVID-19, with no significant differences in 30-day mortality between PSI-17 versus PSI-20 within each PORT Class (Class I: 1.2% vs. 1.3%, p = 0.99; Class II: 5.7% vs. 3.4%, p = 0.20; Class III: 19.0% vs. 14.8%, p = 0.23; Class IV: 34.5% vs. 28.2%, p = 0.12; Class V: 42.1% vs. 47.1%, p = 0.89) ( Fig 3 ).

The comparison of ROCs from (A) univariate logistic regression and (B) risk-adjusted multivariable logistic regression, adjusted for 11 risk factors for 30-day mortality.

https://doi.org/10.1371/journal.pone.0303899.g001

Calibration plots of mean observed rates versus predicted rates of 30-day mortality for univariate logistic regression on (A and B) and risk-adjusted multivariable logistic regression adjusted for 11 risk factors (C and D). Samples were equally divided into 8 groups, according to their predicted 30-day mortality probability. "Rug plot" below the main figures are histograms showing the number of observations for the corresponding predicted 30-day mortality probability.

https://doi.org/10.1371/journal.pone.0303899.g002

30-day mortality rates for PORT Class I are 1.2% and 1.3% for PSI-17 and PSI-20, respectively. Individual patient PSI scores who survived or died are shown in green or yellow, respectively.

https://doi.org/10.1371/journal.pone.0303899.g003

After including the 11 risk factors identified by Diaz et al. (2022) [ 3 ] (WHO-OSS, do-not-resuscitate status, race/ethnicity, body mass index, creatinine clearance <50 mL/min, dementia, hypertension, D-dimer level, absolute lymphocyte count, any corticosteroid use, and a term for temporal effect), the AUCs (95% CIs) of the models increased to 0.85 (0.83–0.88) for PSI-17 and 0.86 (0.84–0.89) for PSI-20 ( Fig 1B ). In addition, the calibration plots showed that both indices had excellent agreement between observed and predicted 30-day mortality probabilities ( Fig 2C and 2D ).

The net reclassification index (NRI), Fig 4 , gives an individual determination per patient for which score performed better, given the actual outcome [ 12 ]. The net proportion of patients who didn’t die within 30-days and were assigned a lower risk by PSI-20 was 0.5, while the net proportion of patients who died within 30-days and were assigned a higher risk by the PSI-20 risk model was -0.044. Thus, although PSI-20 had a 50% improvement in the predictions of survivors, it introduced a few more errors in the predictions of 30-day death.

Open circles and crosses indicate alive and death, respectively. Blue symbols mean that PSI-20 improved PSI-17 since it had a higher predicted probability of 30-day mortality for those who died, or a lower predicted probability for those who were alive. Red symbols mean that PSI-20 didn’t improve PSI-17 since it had a lower predicted probability of 30-day mortality for those who died, or a higher predicted probability for those who were alive.

https://doi.org/10.1371/journal.pone.0303899.g004

Community-Acquired Pneumonia (CAP) is an acute lung infection that causes 1.5 million hospitalizations in the United States each year [ 13 ]. A Recent study showed that the hospitalized COVID-19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death [ 14 ]. As the COVID pandemic continues to evolve, health systems could see subsequent waves with large numbers of patients admitted to the inpatient setting with COVID-19 pneumonia, consuming valuable resources. There are currently a variety of therapeutics available for patients with mild to moderate COVID-19 [ 15 ]. Once patients develop new hypoxemia, common practice is hospital admission for treatment with intravenous remdesivir and dexamethasone [ 16 ]. We have previously described that PSI-20 is a good predictor of mortality from COVID-19 pneumonia [ 3 ]. Using PSI-20 can assist physicians to determine if outpatient treatment is appropriate for community acquired pneumonia, based on predicted mortality [ 1 ]. However, using PSI-20 can be cumbersome as it requires manual data entry for 3 fields and therefore is used variably in clinical practice. In this study, we demonstrate that the PSI-17 can accurately predict mortality in COVID-19. PSI-17 includes data elements which are found discreetly in the EMR and is amenable to automatic calculation within the EMR.

It is likely that COVID-19 patients with low risk of mortality who are also hypoxemic may be safely managed in the outpatient setting, according to current treatment recommendations, particularly in combination with telehealth home monitoring and provision of antivirals. Absolute mortality differed in the PORT classes defined by Fine et al. (1997) for bacterial pneumonia, compared to this study for COVID-19 pneumonia. For instance, for bacterial pneumonia in Fine et al. (1997), PORT class II predicted 0.6–0.9% mortality where outpatient care is reasonable barring other factors affecting care and PORT class III predicts a 0.9–2.8% mortality where outpatient or inpatient care is reasonable, depending on clinical judgement. In this study, PORT class I predicted 1.2% and 1.3% mortality and PORT class II predicted 5.7% and 3.4% mortality, from PSI-17 and PSI-20, respectively. Given the high mortality seen in the first wave of the pandemic, triage recommendations could be shifted for PORT classes. For instance, PORT class I would be reasonable to triage to outpatient care, and PORT class II would be reasonable for outpatient or inpatient care, depending on clinical judgement. To determine whether PSI-17 is predictive of mortality in community acquired pneumonia [ 13 ], validation in an appropriate cohort would need to be performed.

Similarly, as the mortality has decreased over time through evolving SARS-CoV-2 variants [ 17 – 19 ], the application of a mortality prediction tool would need to be validated in different variant eras. Future study could determine different recommendations for use as mortality during the pandemic continues to evolve given changing virulence of variants and population level protection from vaccination and prior infection.

Additionally, PSI-17 may also potentially predict if a patient is likely to decline and become hypoxemic if they are initially on room air on presentation. Based on our COVID-19 patient cohort, we showed that PSI-17 is not only efficient to calculate but also remains as an effective predictor for 30-day mortality. Jones et al. [ 20 ] took this even further and decided that if the computer was going to create PSI-17 (they call this ePSI) then the computer might as well add more terms for 30-day mortality prediction for pneumonia patients. In this paper, we also augmented the PSI-17 with other clinical variables for 30-day mortality. After adjusting for the 11 risk factors, we identified previously [ 3 ], the AUC’s of the PSI-17 and PSI-20 models both increased to above 0.85. The PSI is not comprehensive and other model features could be added, such as diabetes, which is a known risk factor for poor outcomes in COVID-19 [ 14 ] and is not one of the listed co-morbidities in PSI ( S1 Table ). Pneumonia predictive scores have been studied in COVID-19 and PSI was shown as one of the better performing scores for 30-day COVID mortality along with CURB-65, and covid specific scores: 4C and COVID GRAM among 11 different scores for mortality assessment [ 21 ].

In this study, we have shown that PSI-17 may be an easily obtained and effective alternative to PSI-20 for mortality prediction in COVID-19 as determined in this early pandemic cohort. The simplification of PSI-17 to remove fields that require manual chart abstraction can serve for integration of scores into clinical interfaces of electronic health records to be accessed at the point of care, or for controlling of confounders in retrospective studies of COVID-19. However, use of PSI-17 may not be relevant in the individual assessment of a COVID-19 patient where these three variables are readily obtainable. External validation as well as updated assessment of clinical utility in subsequent calendar years (including in the era of additional therapies) merits future study to further evaluate the association of automated calculation of PSI-17, disposition, and outcomes.

Supporting information

S1 fig. proportional-area venn diagram..

Among 1,138 patients, 433 (38%) patients had at least one of the 3 PSI-20 variables (SNF, skilled nursing facility; AMS, altered mental status; PE, pleural effusion); 705 patients (62%) did not have any of the 3 variables.

https://doi.org/10.1371/journal.pone.0303899.s001

S1 Table. Points assigned for PSI-20 (Fine et al., 1997) and PSI-17.

https://doi.org/10.1371/journal.pone.0303899.s002

S2 Table. Patient demographics and baseline characteristics.

https://doi.org/10.1371/journal.pone.0303899.s003

https://doi.org/10.1371/journal.pone.0303899.s004

Acknowledgments

We acknowledge the patients included in this study and the caregiver teams across the Providence organization. SCC was affiliated with the Providence St. Joseph Health at the time of this study and is currently affiliated with the Society of Thoracic Surgeons (STS).

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• Open access
• Published: 21 May 2024

Effectively teaching cultural competence in a pre-professional healthcare curriculum

• Karen R. Bottenfield 1 ,
• Maura A. Kelley 2 ,
• Shelby Ferebee 3 ,
• Andrew N. Best 1 ,
• David Flynn 2 &
• Theresa A. Davies 1 , 2  

BMC Medical Education volume  24 , Article number:  553 ( 2024 ) Cite this article

Metrics details

There has been research documenting the rising numbers of racial and ethnic minority groups in the United States. With this rise, there is increasing concern over the health disparities that often affect these populations. Attention has turned to how clinicians can improve health outcomes and how the need exists to educate healthcare professionals on the practice of cultural competence. Here we present one successful approach for teaching cultural competence in the healthcare curriculum with the development of an educational session on cultural competence consisting of case-based, role-play exercises, class group discussions, online discussion boards, and a lecture PowerPoint presentation.

Cultural competence sessions were delivered in a pre-dental master’s program to 178 students between 2017 and 2020. From 2017 to 2019, the sessions were implemented as in-person, case-based, role-play exercises. In 2020, due to in-person limitations caused by the COVID-19 pandemic, students were asked to read the role-play cases and provide a reflection response using the online Blackboard Learn discussion board platform. Evaluation of each session was performed using post-session survey data.

Self-reported results from 2017 to 2020 revealed that the role-play exercises improved participant’s understanding of components of cultural competence such as communication in patient encounters (95%), building rapport with patients (94%), improving patient interview skills (95%), and recognition of students own cultural biases when working with patients (93%).

Conclusions

Students were able to expand their cultural awareness and humility after completion of both iterations of the course session from 2017 to 2019 and 2020. This session can be an effective method for training healthcare professionals on cultural competence.

Peer Review reports

It is projected that by the year 2050, racial and ethnic minority groups will make up over 50% of the United States population [ 1 ]. With a more multicultural society, growing concern has emerged over how to address the health disparities that effect these populations and the ways in which healthcare professionals can increase positive health outcomes. Continuing evidence suggests that many patients from racial and ethnic minority groups are not satisfied with the current state of healthcare which has been attributed to implicit bias on the part of physicians and current challenges faced by practitioners who feel underprepared to address these issues due to differences in language, financial status, and healthcare practice [ 2 , 3 , 4 ].

To contend with health disparities and the challenges faced by practitioners working with a more diverse population, healthcare educators have begun to emphasize the importance of educating healthcare workforce on the practice of cultural competence and developing a skilled-based set of behaviors, attitudes and policies that effectively provides care in the wake of cross-cultural situations and differences [ 4 , 5 , 6 ]. There are several curricular mandates from both medical and dental accreditation bodies to address this issue [ 7 , 8 , 9 ], and large amounts of resources, ideas, and frameworks that exist for implementing and training future and current healthcare providers on the inadequacies of the healthcare system and cultural competence [ 10 , 11 , 12 ]. These current institutional guidelines for accreditation and the numerous amounts of resources for training cultural competence, continue to evolve with work documenting the need for blended curriculum that is continuous throughout student education, starting early as we have done here with pre-dental students, including in-person didactic or online sessions, a service learning component, community engagement and a reflective component [ 4 , 5 , 13 , 14 ].

This study investigates teaching cultural competence in a healthcare curriculum. We hypothesized that early educational exposure to cultural competence through role playing case studies, can serve as an effective mechanism for training early pre-doctoral students the practice of cultural competence. Utilizing student self-reported survey data conducted in a predental master’s curriculum, in which two iterations of role-playing case studies were used to teach components of cultural competence, this study aims to evaluate and support research that suggests role-playing case studies as effective means for educating future clinical professionals on the practice of cultural competence.

This study was determined to be exempt by the Institutional Review Board of Boston University Medical Campus, Protocol # H-37,232. Informed consent was received from all subjects.

Data collection

The role-playing, case-based simulated patient encounter exercises were developed and administered at Boston University Chobanian & Avedisian School of Medicine to predental students in the Master of Science in Oral Health Sciences Program (see Table  1 ). From 2017 to 2020, we administered patient encounter cases [see Additional File 1 ] to students ( n  = 178) in the program as a portion of a case-based, role-playing exercise to teach the importance of cultural competence and cultural awareness during patient encounters. During years 2017–2019, real actors portrayed the patient and physician. In 2020, the session was conducted online via a discussion board through a Blackboard Course Site. The original case was published as part of a master’s students thesis work in 2021 [ 15 ].

Description of patient encounter cases 1 and 2

Patient Encounter Case 1 [see Additional file 1 ] is composed of two subsections, scenario 1 A and scenario 1B, and is centered around a patient/physician interaction in which a patient who is pregnant presents with pain upon urination. The physician in 1 A is short and terse with the patient, immediately looking at a urine sample, prescribing medication for a urinary tract infection, and telling the patient to return for a follow-up in 2 weeks. In scenario 1B, a similar situation ensues; however, in this scenario the physician takes more time with the patient providing similar care as the physician in 1 A, but asking for more information about the patients personal and medical history. At the conclusion of the scenario, the patient is offered resources for an obstetrician and a dentist based on the information that is provided about the patient’s background. The patient is then sent on their way and asked to follow-up in 2 weeks. The patient does not return.

Patient Encounter Case 2 [see Additional file 1 ] follows a similar format to the Patient Encounter Case 1. In scenario 2 A, the same patient from Case 1 returns with tooth pain after giving birth. The physician in 2 A, like 1 A, is short with the patient and quickly refers the patient to a dentist. In 2B, the physician again takes more time with the patient to receive background information on the patient, make a connection, and provides an antibiotic and dental referral.

Each Patient Encounter Case explored topics such as the importance of building a trusting physician/patient relationship, the importance of asking a patient for patient history, making a connection, and the importance of a physician taking all facets of a patient’s circumstances into consideration [ 15 ].

Session outline

The sessions conducted between 2017 and 2019 were composed of three parts: (1) enactment of an abridged patient encounter facilitated by session administrators, (2) group discussion and reflection during which time students were asked to critically reflect and discuss the theme and key take-aways from the role play exercise, and (3) a PowerPoint presentation emphasizing take-away points from the role-play exercise. At the conclusion of the cultural competence training sessions, students participated in a post-session Qualtrics generated survey administered electronically to assess each student’s feelings about the session [see Additional file 3 ].

Role-play enactment

Facilitators dressed-up in clothing to mimic both the physician and patient for all case scenarios in Patient Encounter Case 1 and Case 2. At the conclusion of the role play portion of each of the cases, the facilitators paused to lead students in a real-time class group discussion. After Case 1, students were asked questions such as: What did you think ? Were the patient’s needs met? Did you expect the patient to return? Following Case 2, similar questions were asked by the facilitators, including: What did you think ? Were the patient’s needs met? Did you expect the patient to accept help?

At the conclusion of this portion of the session, the facilitators led a larger general discussion about both cases and how they related to one another. Finally, the course session concluded with a PowerPoint presentation that reinforced the take-home points from the session [see Additional file 2 ] [ 15 ].

Change in session modality due to COVID-19 pandemic

In Fall 2020, due to the COVID-19 pandemic, the course modality moved to an online platform and consisted of three parts on a Blackboard Discussion Board (Blackboard, Inc.). Students were required to: (1) read each of the Patient Encounter Cases and add a brief reflection comparing the scenarios, (2) then comment on at least two peer’s posts in the discussion forum and (3) attend class to hear a PowerPoint presentation by a course session facilitator on the key take-aways from each scenario [ 15 ].

Student surveys

At the conclusion of the cultural competence training sessions, students participated in a post-session Qualtrics ( https://www.qualtrics.com ) generated survey administered electronically to assess each student’s feelings about the sessions [see Additional file 3 ]. The format of the survey included 5 questions with the following Likert scale response options: strongly agree, agree, disagree, strongly disagree. These post-session surveys were not required but rather optional [ 15 ].

A total of 178 students completed the cultural competence sessions between 2017 and 2020. Of these participants, 112 voluntarily completed a post-session survey on the effectiveness of the course in teaching cultural competence and cultural awareness during patient encounters. Between 2017 and 2019, 99 students completed post-session surveys following sessions with role play exercises. In 2020, 13 students completed post-session surveys following discussion board sessions.

Role-play exercises enhanced cultural competence

In responding to post-session survey questions following cultural competence sessions that included role-play exercises (2017–2019), 71% of students surveyed strongly agreed and 24% agreed that the role-play exercises helped them to identify the importance of communication in patient encounters. In asking participants if the role-play exercises made them more aware of different strategies to improve their patient interview skills, 72% strongly agreed and 23% agreed. Also, 68% of the students strongly agreed and 26% agreed that the exercises helped them to better identify the importance of building rapport and trust during patient encounters. When asked if the exercises helped the students to better understand their own bias and/or cultural awareness when working with patients, the results of the survey showed that 62% of students strongly agreed and 31% agreed with this statement. In addition, most students found the role-play exercises to be enjoyable (72% strongly agreed and 22% agreed). See results shown in Fig.  1 .

Cultural Competence Session Survey Data from the Year 2017–2019. Survey data from students at Boston University’s Oral Health Sciences Program for the years 2017–2019. Data is presented as percent of respondents ( n  = 99)

Discussion boards and reflections enhanced cultural competence

Cultural competence sessions held during 2020 did not include role-play exercises due to the Covid-19 pandemic. Instead, students participated in discussion boards and reflections on Blackboard. In response to the post-session survey question asking if the discussion board exercises were helpful in identifying the importance of communication during patient encounters, 67% of students strongly agreed and 25% agreed with this statement. Also, 75% of students strongly agreed and 17% agreed that the discussion board exercises helped them identify the importance of building rapport and trust during patient contact. When asked if the exercises helped the students to better understand their own bias and/or cultural awareness when working with patients, the results of the survey showed that 67% of students strongly agreed and 25% agreed with this statement. In addition, most students found the discussion board exercises to be enjoyable (67% strongly agreed and 22% agreed). See results shown in Fig.  2 .

Cultural competence session survey data from the Year 2020. Survey data from students at Boston University’s Oral Health Sciences Program for the year 2020. Data is presented as percent of respondents ( n  = 13)

Student responses to the reflection portion of the online cultural competency sessions were recorded and categorized. Five themes were selected and 441 reflection responses were coded using NVivo (Version 12). The results showed that 29% of reflections demonstrated student’s ability to understand a holistic approach to clinical care, 24.3% understood the importance of collecting a patient history, 6.8% recognized the socioeconomic factors during a patient encounter, 27.9% reflected on the importance of the patient clinical relationship, and 12% on the effects on improving health outcomes (Table  1 ). Representative student responses to these themes are shown in Table  1 .

There exists a need to develop novel and effective means for teaching and training the next generation of healthcare professionals the practice of cultural competence. Thus, two iterations of a course session using case-based patient centered encounters were developed to teach these skills to pre-professional dentals students. Overall, the results of this study demonstrated that participation in the course, subsequent group discussion sessions, and take-away PowerPoint sessions significantly improved the participant’s understanding of the importance of communication skills and understanding of socioeconomic, environmental, and cultural disparities that can affect a patient’s health outcome.

According to results from the course session implemented in-person from 2017 to 2019, the role-playing exercise significantly improved participants understanding of important components that can be used to improve health outcomes that may be affected due to health disparities. Students were strongly able to identify the importance of communication in patient encounters, to understand strategies such as communication and compassionate care in patient encounters, identify the importance of building a patient-physician relationship with patients, and were able to recognize their own cultural biases. Similarly, in 2020, even with a change in course modality to on-line learning due to COVID-19, students were able to understand the same key take-aways from the course session as demonstrated by reflections using the discussion board regarding the need for a holistic approach to care, importance of the patient clinician relationship, and importance of taking a patient history. Despite promising implications of both iterations of the session, students completing the session online did not find the same success in “understanding my own bias/and or cultural awareness when working with patients.” This decrease may be attributed to change in course modality and the strengths of the role-play enactment of the patient encounter. It is important to recognize that additional learning components, including video recordings of the role-play enactment, may be necessary if the discussion board is used as the primary learning method in the future.

In contrast to previous studies that attempted to determine the effectiveness of cultural competence training methods, this session had many unique characteristics. The simulated role-playing exercise enabled student participants to see first-hand an interactive patient scenario that could be used as an example for when students begin working with patients or communicating with patients who are culturally diverse. Additionally, the nature of the cases created for the course session which were divided into a part A in which the patient physician was more straightforward when diagnosing and treating the patient and a part B with a more comprehensive and nurturing approach to care, allowed the students to compare the scenarios and make their own assumptions and comments on the effectiveness of each portion of the case. Another strength of this training, was the faculty with cultural competence training were uniquely involved in case creation and facilitation of the course session. According to previous studies with similar aims, it was noted that direct observation and feedback from a faculty member who had cultural competence training and direct contact with patients can provide students with a more memorable and useful experience when educating students [ 12 ]. The facilitators of this session were able to emphasize from their own personal experiences how to work with culturally diverse populations.

An important aspect of the 2020 iteration of the course session in which a discussion board format was used, was that it allowed students who may feel uncomfortable with sharing their thoughts on a case and their own biases, the opportunity to share in a space that may feel safer than in person [ 4 ]. Previous studies have mentioned challenges with online discussion boards [ 4 ] but here we had robust participation, albeit required. Students often contributed more than the required number of comments and they were often lengthy and engaging when responding to peers. Finally, in contrast to previous studies, this course session took place in a pre-professional master’s program, the M.S. in Oral Health Sciences Program at Boston University Chobanian & Avedisian School of Medicine. This program, in which students are given the opportunity to enhance their credentials for professional school, provided students with early exposure to cultural competence training. Students that completed this session in their early pre-professional curriculum should be better prepared than peers who did not receive any cultural competence training until they entered their designated professional school. This session is part of an Evidence Based Dentistry course, which incorporates a larger component of personal reflection that serves to engage students in critical thinking as they begin to develop the skills to be future clinicians. Students that understand different cultures, society and themselves through self-assessments will grow and be best suited in time to treat future patients [ 4 , 16 , 17 ].

One limitation of the present study was the number of survey participants that competed the post-session surveys, as survey completion was not required. Thus, the number of student participants declined over the years, reaching its lowest number of participants in 2020 when the discussion board course session was implemented, and students may have been over surveyed due to the pandemic. Another limitation to this study, was the lack of both a pre and post survey that could be used to determine how student’s understanding of cultural competence had evolved from their entry into the course to the conclusion of the course as well as individual bias and self-reporting measures.

In the future, the course should implement both a role-playing format and subsequent discussion board reflections within the same course session. Studies have shown that alternatives ways of drawing students to reflect whether role play, personal narratives, etc. can be extremely advantageous in developing personal reflection and awareness building competency [ 4 , 16 , 17 , 18 ]. It is noted that role-playing exercises that allow students to provide feedback with student colleagues can provide students with more insight into their own behaviors. It has also been shown in previous studies that student writing and reflection activities can also facilitate student’s reflections on their own beliefs and biases [ 4 , 11 ]. Reflective writing skills are an important and effective means for students to continue to gauge their cultural competence throughout the remainder of their academic training and as future clinicians [ 4 , 17 , 19 ]. Further, students may experience emotional responses through the process of reflective writing as they recognize personal bias or stereotypes, creating a profound and impactful response resulting in enhanced understanding of cultural differences and beliefs [ 4 ]. By combining both learning techniques, students would be able to understand their own bias and their classmates and create a dialogue that could be more beneficial than just one learning method alone. Furthermore, by implementing the discussion board into the role-playing session, as stated previously, students that are more cautious about sharing their point of view or about their own implicit bias in a traditional classroom setting would be able to express their opinions and facilitate a more comprehensive discussion more thoroughly.

Here we show an effective means to utilize role-play of a multi-scenario case-based patient encounter to teach pre-professional healthcare student’s components of cultural competence, emphasizing the importance of provider-patient interactions, holistic patient care, and patient history and socioeconomic factors in provider care. This study contributes to the larger body of work that seeks to address this important aspect of education as it relates to enhancing patient health care outcomes.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge Boston University’s Chobanian & Avedisian School of Medicine’s Graduate Medical Science students and study participants.

No funding was used for the completion of this study.

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Karen R. Bottenfield, Andrew N. Best & Theresa A. Davies

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Maura A. Kelley, David Flynn & Theresa A. Davies

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Contributions

TAD designed the original study concept, taught the classes (roleplay), conducted the surveys, and collected data; MAK designed the original case and PowerPoint, and performed roleplay; DBF and SF evaluated data and drafted original figures; ANB assisted in drafting the manuscript; KRB finalized figures and the manuscript.

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Correspondence to Theresa A. Davies .

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This study was determined to be EXEMPT by the Institutional Review Board of Boston University Medical Campus, Protocol # H-37232.

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Bottenfield, K.R., Kelley, M.A., Ferebee, S. et al. Effectively teaching cultural competence in a pre-professional healthcare curriculum. BMC Med Educ 24 , 553 (2024). https://doi.org/10.1186/s12909-024-05507-x

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An infection occurs when germs enter the body, increase in number and the body reacts. Only a small portion of germs can cause infection.

Terms to know

• Sources : places where infectious agents (germs) live (e.g., sinks, surfaces, human skin). Sources are also called reservoirs.
• Susceptible person: someone who is not vaccinated or otherwise immune. For example, a person with a weakened immune system who has a way for the germs to enter the body.
• Transmission: a way germs move to the susceptible person. Germs depend on people, the environment and/or medical equipment to move in healthcare settings. Transmission is also called a pathway.
• Colonization: when someone has germs on or in their body but does not have symptoms of an infection. Colonized people can still transmit the germs they carry.

For an infection to occur, germs must transmit to a person from a source, enter their body, invade tissues, multiply and cause a reaction.

How it works in healthcare settings

Sources can be:.

• People such as patients, healthcare workers and visitors.
• Dry surfaces in patient care areas such as bed rails, medical equipment, countertops and tables).
• Wet surfaces, moist environments and biofilms (collections of microorganisms that stick to each other and surfaces in moist environments, like the insides of pipes).
• Cooling towers, faucets and sinks, and equipment such as ventilators.
• Indwelling medical devices such as catheters and IV lines.
• Dust or decaying debris such as construction dust or wet materials from water leaks.

Transmission can happen through activities such as:

• Physical contact, like when a healthcare provider touches medical equipment that has germs on it and then touches a patient before cleaning their hands.
• Sprays and splashes when an infected person coughs or sneezes. This creates droplets containing the germs, and the droplets land on a person's eyes, nose or mouth.
• Inhalation when infected patients cough or talk, or construction zones kick up dirt and dust containing germs, which another person breathes in.
• Sharps injuries such as when someone is accidentally stuck with a used needle.

A person can become more susceptible to infection when:

• They have underlying medical conditions such as diabetes, cancer or organ transplantation. These can decrease the immune system's ability to fight infection.
• They take medications such as antibiotics, steroids and certain cancer fighting medications. These can decrease the body's ability to fight infection.
• They receive treatments or procedures such as urinary catheters, tubes and surgery, which can provide additional ways for germs to enter the body.

Recommendations

Healthcare providers.

Healthcare providers can perform basic infection prevention measures to prevent infection.

There are 2 tiers of recommended precautions to prevent the spread of infections in healthcare settings:

• Standard Precautions , used for all patient care.
• Transmission-based Precautions , used for patients who may be infected or colonized with certain germs.

There are also transmission- and germ-specific guidelines providers can follow to prevent transmission and healthcare-associated infections from happening.

Learn more about how to protect yourself from infections in healthcare settings.

For healthcare providers and settings

• Project Firstline : infection control education for all frontline healthcare workers.
• Infection prevention, control and response resources for outbreak investigations, the infection control assessment and response (ICAR) tool and more.
• Infection control specifically for surfaces and water management programs in healthcare settings.
• Preventing multi-drug resistant organisms (MDROs).

Infection Control

CDC provides information on infection control and clinical safety to help reduce the risk of infections among healthcare workers, patients, and visitors.

For Everyone

Health care providers, public health.