Since its opening in January 2016, the Johns Hopkins Capacity Command Center has helped Johns Hopkins Medicine manage hospital operations — notably the flow of patients. So when the COVID-19 pandemic and the first people with the illness came to the hospital, the capacity command center was ready to manage the influx of patients.
“We didn’t need a separate plan for patients with COVID-19 because this is what we do every day,” recalls Jim Scheulen, chief administrative officer, emergency medicine and capacity management at Johns Hopkins Medicine. “These were patients with emergency needs just like others, but they had COVID-19. They’re patients and they’re being transported, and we know how to do that.”
The Judy Reitz Capacity Command Center, as it is formally known, at The Johns Hopkins Hospital was designed and built in collaboration with GE Healthcare Partners. With its 12 digital, data-filled screens in the front of the room, the control center combines the latest in systems engineering, predictive analytics and innovative problem-solving to better manage patient safety, experience, volume and the movement of patients in and out of the hospital and around the health system, which enables greater access to Johns Hopkins’ world-class services. Between 25 and 30 staff members inside the command center control bed assignments for all patients admitted and discharged within The Johns Hopkins Hospital, as well as those transferred to and from four Johns Hopkins Medicine hospitals — Howard County General, Johns Hopkins Bayview Medical Center, Sibley Memorial and Suburban – along with those transferred from outside hospitals.
Since the first patient with COVID-19 was admitted within the Johns Hopkins Health System in March 2020, the capacity command center has managed the flow of patients into and out of The Johns Hopkins Hospital and other health system hospitals, allowing for patients to receive optimal and often lifesaving care during their hospital stay. Through March 1, 2021, 659 patients with COVID-19 were transferred to and from hospitals in the Johns Hopkins Health System, and 877 patients with COVID-19 were transferred internally by the Lifeline team at The Johns Hopkins Hospital. Importantly, of the team dispatched from the capacity command center to transport patients, not a single transmission of COVID-19 from patient to staff is known to have occurred during a transport. In the same time period, the Command Center managed a total of 7,529 patient transfers including and excluding patients with COVID-19.
The capacity command center’s impact before and during the COVID-19 pandemic resembles that of the airline industry. “The airline industry is trying to get the right plane, to the right gate, at the right time; we are trying to get the right patient, to the right bed, at the right time,” says Anna Ye, assistant administrator for the Office of Capacity Management in the command center. “Air traffic control is about getting planes and passengers safely from one airport to another, and we are focused on moving patients safely though our hospitals.”
Scheulen and Ye are available for interviews on how the Johns Hopkins Capacity Command Center has boosted health system operations during the COVID-19 pandemic.
For additional details, view a video highlighting the capacity command center’s fifth anniversary and impact during the COVID-19 pandemic.
With vaccinations and falling infection rates, many people are looking for ways to safely visit loved ones. However, evidence shows that COVID-19 tests are not the fast-pass to normalcy some may think. Panagis Galiatsatos explains how the different coronavirus tests detect the virus, when to get one and what they can tell us about your risk.
Galiatsatos is available for interviews.
Getting a COVID-19 vaccination may come with mild to moderate side effects. Some people notice pain or swelling where they got the vaccine. Others may have fever, muscle aches, fatigue, headaches or a combination of these symptoms. Now, experts say some also might experience swollen underarm lymph nodes, a typical immune response to a foreign source in the body, but one that may give a false positive mammogram result.
Breast-imaging radiologists have noticed that underarm lymph nodes can be larger than usual after getting a COVID-19 vaccine, on the same side where the vaccine was administered. “This is a normal and expected reaction related to the body’s immune response to the vaccine,” says Lisa Ann Mullen, M.D., assistant professor of radiology and radiological science at the Johns Hopkins University School of Medicine.
The experts advise that women schedule their mammogram screening before the first does of the vaccine or four to six weeks after the second dose to help rule out the possibility that the swollen lymph nodes are related to the vaccine, rather than cancer.
The new recommendations for routine screening mammograms come from the Society of Breast Imaging. “These recommendations are for women without any symptoms or problems with their breasts,” says Mullen. “If a woman has a new lump, breast pain, nipple discharge or a recent diagnosis of breast cancer, she shouldn’t wait; she should have her breast imaging performed as soon as possible.”
Lymph nodes are small, bean-shaped structures that are part of the body’s immune system. Most people can’t feel them unless they become swollen. Sometimes large lymph nodes can be a sign of other problems, such as breast cancer in the lymph nodes or another problem affecting the lymph nodes, such as lymphoma or leukemia.
“The COVID-19 vaccines cause a significant immune response, which is why we are noticing the large lymph nodes,” says Mullen. “Since we can’t tell why a lymph node is large on a screening mammogram, we will call the patient back for additional imaging, likely an ultrasound of the armpit. We are hoping that if women wait four to six weeks after their second dose of the vaccine, their nodes will be back to normal and they won’t risk being recalled for false positive results.”
Annual screening mammograms are used for the early detection of breast cancer and other breast health issues. They are recommended for women 40 years of age or older, or for younger women with specific risk factors for breast cancer. Mullen points out that the COVID-19 vaccine is not related to breast cancer. “Getting the vaccine is not a risk factor for breast cancer and does not cause breast cancer,” says Mullen.
Mullen is available for interviews.
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.
“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.
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.
Even as the pace of COVID-19 vaccinations picks up, we should still be wearing our protective masks, practicing physical distancing and avoiding large gatherings well into the year, health experts say. The mask-wearing could be a boon of sorts to seasonal allergy sufferers, as tree, grass and weed pollen are waiting to spring on them as the weather warms up.
A mask can help decrease the symptoms of seasonal allergies, says Sandra Y. Lin, M.D., a professor of otolaryngology and clinical vice director of the Johns Hopkins University School of Medicine’s Department of Otolaryngology–Head and Neck Surgery.
She says a study published in the December Journal of Allergy and Clinical Immunology found that during a two-week period last spring, nurses with allergy symptoms had improvement in their nasal allergy symptoms after wearing a surgical or N95 mask.
Masks filter out particles, including allergens, says Lin, who is an allergy and sinusitis expert. Most pollens, which are the primary cause of seasonal allergies, are 10–100 micrometers in size (a human hair is approximately 70 micrometers; a grain of fine beach sand, approximately 90 micrometers). Standard surgical masks, the pleated, usually light blue variety typically worn by health care personnel, will filter out anything larger than 3 micrometers, Lin says.
Cloth masks will offer some filtration of allergen particles, so they can also be of benefit, Lin says, but she adds that they likely would be somewhat less effective than a surgical mask.
Lin says that for people with pollen allergies who spend a lot of time outdoors in allergy season, masks can be especially helpful in alleviating their symptoms — as long as they provide enough filtration to block the pollen allergen particles.
Lin is available for interviews.
For information from Johns Hopkins Medicine about the coronavirus pandemic, visit hopkinsmedicine.org/coronavirus. For information on the coronavirus from throughout the Johns Hopkins enterprise, including the Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University, visit coronavirus.jhu.edu.