Predictive Analytics Model Can Determine COVID-19 Outcomes

Using a predictive analytics model, providers can better project COVID-19 outcomes for improved decision-making and resource allocation, according to a study published in Annals of Internal Medicine.

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Researchers from Johns Hopkins University School of Medicine studied data from COVID-19 patients admitted to five Johns Hopkins hospitals between March 4 and April 24, 2020. During that period, Johns Hopkins admitted a combined 827 people aged 18 or older who tested positive for coronavirus and presented with symptoms of the virus.

Using data from these patients, as well as a set of risk factors known to be associated with COVID-19, to develop a predictive analytics model that could forecast how likely a patient’s disease is to worsen while in the hospital. Among the risk factors included were a patient’s age, body mass index (BMI), lung health and chronic disease, vital signs, and the severity of a patient’s symptoms at the time of admission.

The model, called the COVID Inpatient Risk Calculator (CIRC), can help providers assess the risk of a patient’s condition worsening.

The study revealed that the disease can rapidly progress from mild or moderate to severe, particularly if a patient had all or some of the risk factors of the disease. Forty-five of the patients in the study had severe COVID-19 when they were admitted to the hospital, but 120 developed severe disease or died within 12 hours of being admitted.

Of the 302 patients in the study who developed severe disease or died, the median tine of disease progression was 1.1 days.

“Rapid progression of disease following admission to the hospital provides a narrow window to intervene,” said Brian Garibaldi, MD, associate professor of medicine at the Johns Hopkins University School of Medicine. “Different combinations of risk factors appear to predict severe disease or death, with probabilities ranging from over 90 percent to as little as five percent.”

Using CIRC, the researchers estimate that a 60-year-old white woman with a BMI of 28, no chronic disease, and no fever who is hospitalized for COVID-19 has a ten percent chance of her disease worsening by day two of her hospital stay. The longer she’s in the hospital, the greater that chance becomes – increasing to 15 percent after four days and 16 percent after a week.

In contrast, an 81-year-old black woman admitted to the hospital with COVID-19 – with a BMI of 35, diabetes, hypertension, and a fever – has an 89 percent likelihood of progressing to severe disease or even death by just the second day of her hospital stay. By days four and seven, that percentage increases to higher than 95 percent.

By June 24, 694 of the patients in the study had been discharged from the hospital, 131 had died, and seven were still hospitalized with severe illness.

“We identified a few readily measurable demographic and clinical factors that, when assessed admission to the hospital, can predict if someone has a five percent or a 90 percent risk of developing severe disease or dying from COVID-19,” said Amita Gupta, MD, professor of medicine at the Johns Hopkins University School of Medicine, who directs the Center for Clinical Global Health Education and is a co-author of the study.

“This is incredibly useful information to have when communicating with patients and their families, as well as for informing resource allocation in the hospital.”

The study took its data from a registry of all patients treated with COVID-19 at hospitals in the Johns Hopkins system. The registry, called JH-CROWN, offers demographics diagnoses, procedures, social histories, and other data points relevant to caring for COVID-19 patients.

“The JH-CROWN data registry embodies the same teamwork and dedication that went into the care of more than 3,000 COVID-19 patients admitted to Johns Hopkins hospitals since the start of the pandemic,” Garibaldi said. “We hope it can teach us more about the nature of COVID-19 and improve both patient care and research as we prepare for a second wave of infections in the fall.”

The team noted that their study and predictive analytics model offers critical insight into the disease progression of COVID-19 patients, allowing hospitals to better allocate resources and prioritize care.

“This is some of what we’ve learned in the months since we started seeing patients with COVID-19 at our hospitals,” said Garibaldi.

“As we continue to grapple with high numbers of COVID-19 infections across the United States, it’s important to share knowledge with our colleagues at other hospitals.”