# Association between the geriatric nutritional risk index and 28-day mortality in critically ill sepsis-associated pneumonia patients: retrospective study based on two cohorts

## TL;DR

This study shows that the Geriatric Nutritional Risk Index (GNRI) can predict 28-day mortality in critically ill patients with sepsis-associated pneumonia, helping identify those needing urgent care.

## Contribution

The study validates GNRI as a novel, practical tool for risk stratification in sepsis-associated pneumonia patients using two cohorts and a new prognostic nomogram.

## Key findings

- GNRI showed significant inverse associations with 28-day ICU and in-hospital mortality in both cohorts.
- A novel nomogram incorporating GNRI outperformed conventional severity scores in predicting mortality.
- The GNRI-based model achieved AUC values of 0.70–0.71, demonstrating strong predictive performance.

## Abstract

In recent years, the identification of reliable prognostic indicators for critically ill patients has become increasingly crucial. The Geriatric Nutritional Risk Index (GNRI), a simple and objective tool for assessing malnutrition risk, has demonstrated significant prognostic value across various disease conditions. This study aims to investigate and validate the clinical utility of GNRI in predicting 28-day mortality among critically ill patients with sepsis-associated pneumonia.

We conducted a retrospective analysis using two distinct cohorts. Critically ill elderly people with sepsis-associated pneumonia were included. The derivation cohort consisted of critically ill patients with sepsis-associated pneumonia extracted from the MIMIC-IV database, while the validation cohort comprised consecutively enrolled patients meeting identical criteria from Jinyang Hospital Affiliated to Guizhou Medical University between March 2023 and March 2025. Key baseline variables including demographics, comorbidities, and severity scores were analyzed. We employed restricted cubic spline regression, multivariable logistic and Cox regression, and Kaplan-Meier analysis to assess associations between GNRI and mortality. Using LASSO regression for variable selection coupled with multivariable Cox proportional hazards modeling, we developed a prognostic nomogram across three distinct risk strata. Model discrimination was evaluated using time-dependent receiver operating characteristic (ROC) analysis, with predictive performance quantified by the area under the curve (AUC).

The final analysis included 2,230 critically ill patients with sepsis-associated pneumonia, with observed 28-day ICU and in-hospital mortality rates of 26.64% and 26.59%, respectively. In fully adjusted models, both continuous GNRI and categorical GNRI showed significant associations with 28-day ICU mortality across cohorts. The hazard ratios were 0.99 (95% CI: 0.98–1.00) for continuous GNRI; 0.69 (0.51–0.93) for moderate vs. high nutritional risk; and 0.41 (0.25–0.69) for no vs. high nutritional risk. Similar associations were observed for 28-day in-hospital mortality (no vs. high nutritional risk: HR: 0.59, 95% CI: 0.36–0.98). Restricted cubic spline analysis revealed a nonlinear relationship between continuous GNRI and both 28-day ICU and in-hospital mortality. When combined with conventional critical illness severity scores, GNRI provided incremental predictive value for 28-day mortality. ROC curve analysis demonstrated that our risk model outperformed conventional ICU severity scores in identifying high-risk sepsis-associated pneumonia patients. Our novel nomogram demonstrated superior predictive performance for 28-day mortality, achieving area under the curve (AUC) values of 0.71 (training), 0.70 (internal validation), and 0.70 (external validation), consistently exceeding the performance of conventional ICU severity scores.

Our multicenter study demonstrates a consistent inverse association between GNRI and short-term mortality in critically ill patients with sepsis-associated pneumonia across both cohorts. These findings position GNRI as a practical, readily available risk-stratification tool that may assist clinicians in promptly identifying high-risk patients for targeted nutritional interventions and intensified monitoring.