# Prognostic value of red blood cell distribution width in traumatic brain injury: A mediation and deep learning analysis

## TL;DR

This study shows that red blood cell distribution width (RDW) is a useful predictor of short-term mortality in traumatic brain injury patients and partially explains how age affects outcomes.

## Contribution

The study introduces RDW as a partial mediator of age-related mortality in TBI and validates its use in deep learning survival models for risk stratification.

## Key findings

- RDW and age independently predict short-term mortality in TBI patients.
- RDW partially mediates age's effect on mortality, accounting for ~4.5% of the total effect.
- Deep learning models incorporating RDW and age achieve strong predictive performance (C-index: 0.759).

## Abstract

This study aimed to investigate the associations between age, red blood cell distribution width (RDW), and short-term mortality in patients with traumatic brain injury (TBI), with a particular focus on the role of RDW in mediating the impact of age on mortality. We conducted a retrospective cohort analysis of 1,203 ICU-admitted TBI patients from the MIMIC-IV database (v3.1). Cox proportional hazards regression, restricted cubic splines (RCS), and mediation analysis were employed to evaluate the relationships between age, RDW, and mortality outcomes. Both advanced age (adjusted hazard ratio [HR] = 1.022 for 28-day mortality; HRadj = 1.031 for in-hospital mortality) and RDW (HRadj = 1.085 for 28-day mortality; HRadj = 1.094 for in-hospital mortality) were found to predict mortality (all P < 0.05) independently. RDW demonstrated a dose–response relationship with mortality: the highest quartile (Q4) exhibited a 2.061-fold increased risk of 28-day mortality (P = 0.010) and a 2.086-fold increased risk of in-hospital mortality (P = 0.022) compared to the lowest quartile (Q1). RCS analysis revealed significant nonlinear associations between age and 28-day mortality (P < 0.05) and between RDW and in-hospital mortality (P < 0.05). The mediation analysis demonstrated that RDW played a partial mediating role in age-related mortality, accounting for 4.40% of the total effect on 28-day mortality and 4.62% on in-hospital mortality (both P < 0.05). Deep learning survival models (e.g., Deepsurv: C-index: 0.759; IBS: 0.113; AUC (95% CI): 0.824 (0.735–0.900)) that incorporate age, RDW, and other clinical variables achieved robust predictive performance. Age and RDW are independent predictors of short-term mortality in TBI. RDW partially mediates the effect of age on TBI prognosis and shows potential as a practical biomarker for clinical risk stratification.