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Story tips from Johns Hopkins experts on COVID-19

Johns Hopkins Medicine

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Study of Orthodox Jews May Help Guide COVID-19 Prevention in Culturally Bonded Groups
Media Contact: Michael E. Newman, mnewma25@jhmi.edu

The holiday of Purim is a festival of life, recalling how the Jewish people escaped the genocidal plot of an evil minister under an ancient Persian king. In 2021, Purim again marked the saving of Jewish lives, but this time from a different enemy: SARS-CoV-2, the virus that causes COVID-19. Leaders of the U.S. Orthodox Jewish community -- a group devastated by COVID-19 infections and deaths following Purim social gatherings in March 2020 before preventive measures such as masking and physical distancing became commonplace -- were able before this year's holiday to promote scientifically based safety guidelines for COVID-19-free celebrations. This was possible partly because of findings from a Johns Hopkins Medicine-led study evaluating just over 9,500 Orthodox Jews in 12 states that helped define the epidemiology of the Purim 2020 COVID-19 outbreak.

The study appears online March 10 in JAMA Network Open.

"Because Purim in 2020 caused hundreds of Orthodox Jews to become ill or hospitalized with COVID-19 in the earliest stages of the pandemic, we realized that these patients -- who were convalescing when others were just coming in contact with SARS-CoV-2 for the first time -- were an important population to study to better understand why and how the virus spreads through a culturally bonded community," says study co-senior author Avi Rosenberg, M.D., Ph.D., assistant professor of pathology at the Johns Hopkins University School of Medicine.

"We felt with that insight, health care practitioners could develop strategies based on scientific evidence to limit the spread of COVID-19 while still enabling important religious and other cultural practices to go on," he explains.

Rosenberg and his collaborators created the Multi-Institutional sTudy analyZing anti-coV-2 Antibodies Cohort, or MITZVA Cohort (the acronym is taken from the Hebrew word for "commandment" and often refers to a "good deed"), to explore the epidemiology of the Purim 2020 COVID-19 spread within the large Orthodox Jewish communities of Brooklyn, New York; Lakewood, New Jersey; Los Angeles, California; Nassau and Sullivan counties, New York; New Haven, Connecticut; and Detroit, Michigan. Also included were Orthodox Jews who resided in Colorado, Florida, Maryland, North Carolina, Ohio, Pennsylvania and Washington State.

Study participants were first asked to complete a survey to define their demographic characteristics; whether they had any symptoms of SARS-CoV-2 infection before, during or shortly after the 2020 Purim holiday; the onset of any symptoms experienced; and if they had already tested positive for the virus. Out of 12,626 people given the questionnaire, 9,507 completed it and were invited to undergo SARS-CoV-2 antibody testing in the second stage of the study. Of those participants, 6,665 (70.1%) were screened for immunoglobin G (IgG) antibodies to the nucleocapsid (outer covering) protein of SARS-CoV-2 between May 14 and 30, 2020.

The survey results defined the date range for possible COVID-19 symptom onset as from Dec. 1, 2019, to May 26, 2020. More than three-quarters, 77%, of the respondents reported their first symptoms between March 9 and April 1, with another 15% stating theirs began after April 1 -- indicating that they were likely exposed just before or during the Purim season.

Rosenberg says the Purim link to the outbreak is further supported by the fact that the median (the midpoint date when dates were listed from earliest to latest) and mode (the date that occurred most often) for symptom onsets for study participants in all the states fell within the same period, March 17-21, 2020 (with Purim occurring March 10 and 11).

Among the study participants who tested positive for SARS-CoV-2 IgG antibodies, Rosenberg says that most (between 82% and 94% in the primary five communities examined) reported the onset of COVID-19 symptoms between March 9 and March 31, 2020.

The seroprevalence rates -- the percentage of people in a population with antibodies against, and indicating infection with, SARS-CoV-2 -- were consistently higher in the Orthodox Jewish communities than those in neighboring areas during the study time period. This is consistent, Rosenberg says, with the culturally bonded nature of these communities within a neighborhood or city, and even across state lines.

"Based on these findings from a large study population within culturally bonded communities, we identified parallel SARS-CoV-2 outbreaks occurring in multiple areas around the Jewish festival of Purim," Rosenberg says. "The risk to these communities was amplified by the fact that these outbreaks occurred in the early days of the pandemic prior to widespread adoption of mask-wearing and physical distancing procedures."

Rosenberg says that once COVID-19 prevention measures were established and promoted by public health authorities, local and national Orthodox Jewish leaders put forth mandates for their communities to comply, and developed culturally sensitive policies to address how to safely engage in prayer services, family and communal gatherings and social support systems.

"This shows that preventing the spread of COVID-19 does not have to mean giving up or limiting religious and cultural practices that are vital to the lives of so many," Rosenberg says. "We believe that our study of the Purim 2020 outbreak, and the positive actions taken in part because of those findings, can provide guidance for safely celebrating many other religious and secular holidays in the United States, including Chinese New Year, Ramadan and Christmas."

Rosenberg is available for interviews.


Dynamic Tool Accurately Predicts Risk of COVID-19 Progressing to Severe Disease or Death
Media Contact: Michael E. Newman, mnewma25@jhmi.edu

Clinicians often learn how to recognize patterns in COVID-19 cases after they treat many patients with it. Machine-learning systems promise to enhance that ability, recognizing more complex patterns in large numbers of people with COVID-19 and using that insight to predict the course of an individual patient's case. However, physicians sworn to "do no harm" may be reluctant to base treatment and care strategies for their most seriously ill patients on difficult-to-use or hard-to-interpret machine-learning algorithms.

Now, Johns Hopkins Medicine researchers have developed an advanced machine-learning system that can accurately predict how a patient's bout with COVID-19 will go, and relay its findings back to the clinician in an easily understandable form. The new prognostic tool, known as the Severe COVID-19 Adaptive Risk Predictor (SCARP), can help define the one-day and seven-day risk of a patient hospitalized with COVID-19 developing a more severe form of the disease or dying from it.

SCARP asks for a minimal amount of input to give an accurate prediction, making it fast, simple to use and reliable for basing treatment and care decisions. The new tool is described in a paper first posted online March 2 in the Annals of Internal Medicine.

"SCARP was designed to provide clinicians with a predictive tool that is interactive and adaptive, enabling real-time clinical variables to be entered at a patient's bedside," says Matthew Robinson, M.D., assistant professor of medicine at the Johns Hopkins University School of Medicine and senior author of the paper. "By yielding a personalized clinical prediction of developing severe disease or death in the next day and week, and at any point in the first two weeks of hospitalization, SCARP will enable a medical team to make more informed decisions about how best to treat each patient with COVID-19."

The brains of SCARP is a predictive algorithm called Random Forests for Survival, Longitudinal and Multivariate Data (RF-SLAM), described in a 2019 paper by its creators, Johns Hopkins Medicine researchers Shannon Wongvibulsin, an M.D./Ph.D. student; Katherine Wu, M.D.; and Scott Zeger, Ph.D.

Unlike past clinical prediction methods that base a patient's risk score on their condition at the time they enter the hospital, RF-SLAM adapts to the latest available patient information and considers the changes in those measurements over time. To make this dynamic analysis possible, RF-SLAM divides a patient's hospital stay into six-hour windows. Data collected during those time spans are then evaluated by the algorithm's "random forests" of approximately 1,000 "decision trees" that operate as an ensemble. This enables SCARP to give a more accurate prediction of an outcome than each individual decision tree could do on its own.

"The same way that individual stocks and bonds perform better as a portfolio -- with the overall value staying strong as individual items balance each other's rises and falls in price -- the trees as a group create a flexible and adaptable forest that protect each other from individual errors," says Robinson. "So, even if some trees predict incorrectly, many others will get it right and move the group in the correct direction."

Robinson says that most machine-learning systems used to make clinical prediction can only consider static data at a single point in time. "RF-SLAM enables us to be nimble and predict the future at any point," he explains.

To demonstrate SCARP's ability to predict severe COVID-19 cases or deaths from the disease, Robinson and his colleagues used a clinical registry with data about patients hospitalized with COVID-19 between March and December 2020, at five centers within the Johns Hopkins Health System. The patient information available included demographics, other medical conditions and behavioral risk factors, along more than 100 variables over time, such as vital signs, blood counts, metabolic profiles, respiratory rates and the amount of supplemental oxygen needed.

Among 3,163 patients admitted with moderate COVID-19 during this time, 228 (7%) became severely ill or died within 24 hours; an additional 355 (11%) became severely ill or died within the first week. Data also were collected on the numbers who developed severe COVID-19 or died on any day within the 14 days following admission.

Overall, SCARP's one-day risk predictions for progression to severe COVID-19 or death were 89% accurate, while the seven-day risk predictions for both outcomes were 83% accurate.

Robinson says that further SCARP trials are planned to validate its performance on a large scale using national patient databases. Based on the results of the first study, Johns Hopkins Medicine has already incorporated a version of SCARP into the electronic medical record system at all five of its hospitals in the Maryland and Washington, D.C., area.

"Our successful demonstration shows that SCARP has the potential to be an easy-to-use, highly accurate and clinically meaningful risk calculator for patients hospitalized with COVID-19," says Robinson. "Having a solid grasp of a patient's real-time risk of progressing to severe disease or death within the next 24 hours and next week could help health care providers make more informed choices and treatment decisions for their patients with COVID-19 as they get sicker."

Robinson is available for interviews.

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