Acute Kidney Injury treated with dialysis initiation is a common complication of COVID-19 infection among hospitalized patients. However, dialysis supplies and personnel are often limited.
The development of an accurate machine learning model could enable hospitals to make informed decisions about where to allocate dialysis resources. Researchers studied EHR data of adult patients with COVID-19, all admitted to hospitals within the Mount Sinai Health System between March and December of 2020. The final count included 2,442 patients in the internal validation cohort and 3,651 patients in the external validation cohort.
Researchers developed and tested five machine learning models: LASSO, random forest, XGBoost with imputation, XGBoost without imputation, and logistic regression. The XGBoost model without imputation proved to be the most effective at predicting death or dialysis in patients.
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