Associations of weight-derived markers with mortality in patients with Corona virus disease 2019: evidence from hospitals and patients.

OBJECTIVES

This study investigated the relationship between weight-derived markers and in-hospital mortality in patients with Corona Virus Disease 2019 (COVID-19).

METHODS

Various body composition including Weight, Body Mass Index (BMI), Body Fat Percentage (BFP), Whole-Body Fat Mass (WBFM), Lean Body Mass (LBM), and Basal Metabolic Rate (BMR) were calculated based on height, weight, gender, and age. In-hospital mortality served as the primary clinical outcome. The associations between these indicators and patient prognosis were evaluated using a crude model, a logistic Model adjusted for confounders, and a Propensity Score Matching (PSM) model. The corresponding 95% confidence intervals (95% CI) and odds ratio (OR) values were calculated. Additionally, four machine-learning predictive models (Decision Tree Classifier, Random Forest, Gaussian Naive Bayes, Gradient Boosting Classifier) were developed to assess the clinical utility of weight-derived markers.

RESULTS

A total of 509 patients with COVID-19 were included in the study. Among the weight-derived markers, only BMI consistently demonstrated a protective effect against in-hospital mortality (crude model: OR (95% CI) = 0.84 (0.77-0.92); adjusted model 1: OR (95% CI) = 0.84 (0.77-0.92); PSM: OR (95% CI) = 0.85 (0.74-0.97), all P < 0.05). Restricted Cubic Spline regression indicated significant nonlinear associations between BMI, Weight, LBM, and WBFM with in-hospital mortality (P for overall < 0.05). Conversely, no significant nonlinear associations were observed between BFP, BMR, and in-hospital mortality. The BMI-based Random Forest prediction model effectively forecasted in-hospital mortality (ROC (95% CI) = 0.84 (0.76-0.92)).

CONCLUSIONS

Higher BMI was associated with reduced in-hospital mortality in patients with COVID-19, with the BMI-based predictive model demonstrating strong predictive capabilities.