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Machine Learning Model Predicts Treatment with Dialysis or Death for Hospitalized COVID-19 Patients

By HospiMedica International staff writers
Posted on 31 May 2021
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A team of researchers have created a machine learning model to predict treatment with dialysis or death for hospitalized COVID-19 patients.

The study by researchers from the Mount Sinai Health System (New York, NY, USA) used a machine learning model to determine COVID-19 patients most at risk for treatment requiring dialysis or critical illness leading to death. SARS-CoV-2, the virus that causes COVID-19, has infected more than 103 million people worldwide. Acute kidney injury (AKI) treated with dialysis was a common complication in patients who were hospitalized with COVID-19. AKI is associated with increased risks for morbidity and mortality. Early prediction of which patients will need dialysis or experience critical illness, leading to mortality during hospital care can enhance appropriate monitoring, and better inform conversations with patients and their caretakers.

The Mount Sinai team developed and tested five different algorithms to predict patients requiring treatment with dialysis or critical illness leading to death on day 1, 3, 5, and 7 of the hospital stay, using data from the first 12 hours of admission to the Mount Sinai Health System. Assessed features included demographics, comorbidities, laboratory results, and vital signs within 12 hour of hospital admission. The five models created and tested were: the logistic regression, LASSO, random forest, and XGBoost with and without imputation. Out of the total model approaches used, XGBoost without imputation had the highest area under the receiver curve and area under the precision recall curve on internal validation for all time points. This model also had the highest test parameters on external validation across all time windows. Features including red cell distribution width, creatinine, and blood urea nitrogen were major drivers of model prediction.

While the Mount Sinai model requires further external review, such machine learning models can potentially be deployed throughout healthcare systems to help determine which COVID-19 patients are most at risk for adverse outcomes of the coronavirus. Early recognition of at-risk patients can enhance closer monitoring of patients and prompt earlier discussions regarding goals of care.

“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,” said Dr. Girish Nadkarni, MD, Associate Professor in the Department of Medicine (Nephrology), Clinical Director of the Hasso Plattner Institute for Digital Health, and Co-Chair of the Mount Sinai Clinical Intelligence Center at the Icahn School of Medicine at Mount Sinai. “A version of this model is currently deployed at Mount Sinai Hospital in patients who are admitted with COVID-19.”

“As a nephrologist, we were overwhelmed with the increase in patients who had AKI during the initial surge of the COVID-19 pandemic,” said Dr. Lili Chan, MD, Assistant Professor in the Department of Medicine (Nephrology) at the Icahn School of Medicine at Mount Sinai. “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.”

“Machine learning allows us to discern complex patterns in large amounts of data,” said Dr. Akhil Vaid, MD, postdoctoral fellow in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, and member of the Mount Sinai Clinical Intelligence Center and the Hasso Plattner Institute for Digital Health at Mount Sinai. “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.”

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