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

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.

SOFA score performs worse than age for predicting mortality in patients with COVID-19 (preprint)

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA scores in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. Among the COVID-19 cohort, age (area under receiver-operating characteristic curve (AU-ROC) 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.