What question did this study set out to answer?

Evaluate the effectiveness of the inflammation-adjusted prognostic nutritional index (iPNI) for predicting mortality in older ICU patients compared to traditional indices.

March 6, 2026

C-Reactive Protein–Adjusted Prognostic Nutritional Index for Mortality Prediction in Older Adults in Intensive Care: Comparative ROC, Calibration, and Decision-Curve Analyses

Key Points

Evaluate the effectiveness of the inflammation-adjusted prognostic nutritional index (iPNI) for predicting mortality in older ICU patients compared to traditional indices.
Single-center retrospective cohort study of adults aged ≥65 in ICU from 2018-2024.
Primary outcome assessed was ICU mortality; secondary endpoints included need for mechanical ventilation and other critical therapies.
Analysis involved DeLong tests, bootstrap validation, calibration, decision curve analysis, and logistic models.
iPNI showed superior discrimination for predicting ICU mortality with an AUC of 0.961 compared to PNI (0.901) and GNRI (0.681).
Statistical significance was noted in comparisons of iPNI with both PNI and GNRI.

Abstract

Background: In elderly intensive care patients, malnutrition and inflammation together shape the risk of mortality. Traditional indices (PNI, GNRI) are widely used but show variable predictive value. In this study, our primary objective is to evaluate whether iPNI = PNI/(1+lnCRP+1), an index adjusted for inflammation, improved the discrimination and clinical utility of early mortality compared to PNI and GNRI. Secondary factors are the length of stay in the intensive care unit (ICU) and the use of critical organ support therapies.Methods: A single-center retrospective cohort (2018-2024; n=673) of adults aged ≥65 years admitted to the ICU. The primary outcome was defined as ICU mortality. The secondary endpoint was the need for ICU admission and mechanical ventilation (MV), vasopressor (VP), or renal replacement therapy (RRT). We performed DeLong tests, bootstrap internal validation, calibration (intercept, slope, Brier), decision curve analysis (10–40% threshold values), age/sex-adjusted logistic models, Spearman correlations for LOS, and ROC with CRP stratified ROC for PNI and GNRI. Youden cutoff values were determined exploratory a priori.Results: iPNI demonstrated the highest discrimination (AUC 0.961, 95% CI 0.940-0.982), outperforming PNI (0.901, 0.876-0.927) and GNRI (0.681, 0.639-0.723) (both p

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