“Acute Kidney Injury treated with dialysis initiation is a common complication of COVID-19 infection among hospitalized patients,” a study published recently in the Clinical Journal of the American Society of Nephrology has observed. And yet another complicating factor is a frequent shortage of the dialysis supplies and personnel necessary to treat it.
In an attempt to address that problem, researchers working the Mount Sinai Health System in Manhattan have developed a machine learning model that uses predictive analytics to detect a COVID-19 patient’s risk of death or dialysis treatment.
To help hospitals make informed decisions about where to allocate dialysis resources, the researchers studied EHR data of adult patients from five hospitals from the Mount Sinai Health System with COVID-19 who were admitted from March 10th and December 26th, 2020. They then developed and tested five machine learning models with the goal of developing the most effective means of predicting death or dialysis in patients.
“Machine learning allows us to discern complex patterns in large amounts of data,” said Akhil Vaid, MD, one of the study’s authors, in a press release. “For COVID-19 inpatients, this means being able to more easily identify incoming at-risk patients, while pinpointing the underlying factors that are making them better or worse. The underlying algorithm, XGBoost, excels in accuracy, speed, and other under-the-hood features that allow for easier deployment and understanding of model predictions.”
The specific elements of the EHR data assessed included demographics, comorbidities, laboratory results, and vital signs within 12 hour of hospital admission. The machine learning algorithms were able to predict dialysis or death at one, three, five, and seven days after hospital admission.
“As a nephrologist, we were overwhelmed with the increase in patients who had AKI (Acute Kidney Injury) during the initial surge of the COVID-19 pandemic,” said study co-author Lili Chan, MD, in the press release. “Prediction models like this enable us to identify, early on in the hospital course, those at risk of severe AKI (those that required dialysis) and death. This information will facilitate clinical care of patients and inform discussions with patients and their families.”
According to the report, widespread use of EHRs makes the deployment of prediction models such as this one possible.
“The near universal use of electronic health records has created a tremendous amount of data, which has enabled us to generate prediction models that can directly aid in the care of patients,” explained Mount Sinai's Dr. Girish Nadkarni. “A version of this model is currently deployed at Mount Sinai Hospital in patients who are admitted with COVID-19.
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