Renalase Identified by Machine Learning Methods As A Novel Independent Predictor Of Mortality In Hospitalized Patients With COVID-19

Abstract

Abstract Low levels of renalase, a flavoprotein released by kidneys, has been linked with cytokine release syndrome and disease severity of viral infections. We sought to, 1) identify traditional and novel predictors of mortality for patients hospitalized with COVID-19; and 2) investigate whether renalase independently predicts mortality. In a retrospective cohort study, clinicopathologic data and blood samples were collected from hospitalized COVID-19 patients. Patients were excluded if < 18 years or opted out of research. Novel research markers – renalase, kidney injury molecule-1, interferon (α,δ,ι), interleukin (IL-1, IL6), and tumor necrosis factor were measured. The primary outcome was mortality within 180 days of index visit. Among 437 patients who provided 897 blood samples, mean age was 64 years (SD ± 17), 233 (53%) were males, and 48% were non-whites. Seventy-one patients (16%) died. Area under the curve (AUC) for mortality prediction was as follows: using logistic regression with a priori feature selection (AUC = 0.72; CI 0.62, 0.82), logistic regression with backward feature selection (0.70; CI 0.55, 0.77), and XGBoost (0.87; CI 0.77, 0.93)]. PR-AUC and calibration plots also showed best performance with XGBoost model. Elevated BNP, advanced age, oxygen saturation deviation, and low renalase were the leading predictors of mortality in XGBoost. Renalase emerged as an independent predictor of mortality for COVID-19 across all statistical models.