European journal of clinical nutrition · 2026
Comparative study on the predictive value of PNI and GNRI for all-cause mortality rates of elderly patients in cardiac care unit and cardiovascular intensive care unit.
СтатьяНаблюдательное исследованиеСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Zhou Wanlu, Yu Jingjia, Zheng Min, Shi Ruizheng
Аннотация
BACKGROUND: The prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) are reliable alternative biomarkers of nutrition. However, the relationship between nutritional indicators and mortality of elderly patients in the cardiac care unit (CCU) and cardiovascular intensive care unit (CVICU) remains unknown.
METHODS: This retrospective observational study analyzed data from 811 elderly patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary outcome was 360-day all-cause mortality. PNI and GNRI were evaluated using restricted cubic spline (RCS), Cox proportional hazards model, Kaplan-Meier curve, and subgroup analyses. Predictive models were developed using machine learning (ML) algorithms, and the predictive values of feature variables were assessed using the SHapley Additive exPlanation (SHAP) algorithm.
RESULTS: RCS and Cox models showed that higher nutritional indices were associated with lower mortality risk. Kaplan-Meier curves further confirmed higher mortality in patients with lower indices. Four ML algorithms were constructed. Among these, the logistic regression (LR) algorithm demonstrated superior performance compared to the others, with PNI and GNRI identified as the key predictors. Subgroup analyses showed consistent PNI and GNRI effects across congestive heart failure (CHF), myocardial infarction (MI), acute kidney injury (AKI), and type 2 diabetes mellitus (T2DM), with no significant interactions.
CONCLUSION: Nutritional indices were significantly associated with the mortality risk of elderly patients in CCU and CVICU.
METHODS: This retrospective observational study analyzed data from 811 elderly patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary outcome was 360-day all-cause mortality. PNI and GNRI were evaluated using restricted cubic spline (RCS), Cox proportional hazards model, Kaplan-Meier curve, and subgroup analyses. Predictive models were developed using machine learning (ML) algorithms, and the predictive values of feature variables were assessed using the SHapley Additive exPlanation (SHAP) algorithm.
RESULTS: RCS and Cox models showed that higher nutritional indices were associated with lower mortality risk. Kaplan-Meier curves further confirmed higher mortality in patients with lower indices. Four ML algorithms were constructed. Among these, the logistic regression (LR) algorithm demonstrated superior performance compared to the others, with PNI and GNRI identified as the key predictors. Subgroup analyses showed consistent PNI and GNRI effects across congestive heart failure (CHF), myocardial infarction (MI), acute kidney injury (AKI), and type 2 diabetes mellitus (T2DM), with no significant interactions.
CONCLUSION: Nutritional indices were significantly associated with the mortality risk of elderly patients in CCU and CVICU.