Evaluation of Novel Combined CBC-Derived Systemic Inflammatory Ratios and Their Dynamic Changes as ICU Mortality Predictors, a Retrospective Cohort

IF 2.1 Q2 MEDICINE, GENERAL & INTERNAL Health Science Reports Pub Date : 2025-03-05 DOI:10.1002/hsr2.70441

Helia Azmakan, Farshad Hashemian, Kaveh Kazemian

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Abstract

Background and Aims

Novel biomarkers, such as neutrophil lymphocyte ratio, monocyte lymphocyte ratio, neutrophil to lymphocyte platelet ratio, derived neutrophil to lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index, and aggregate index of systemic inflammation, have shown promising prognostic value, especially in ICU settings. We aimed to evaluate the potential of the mentioned factors as ICU mortality predictors in a heterogeneous ICU cohort.

Methods and Materials

We conducted a retrospective cohort study using data obtained from the intensive care unit (ICU) records of 311 patients. We evaluated the strength of the inflammatory parameters upon admission, 48 h later, and their dynamic changes within this period in predicting ICU mortality. We used multivariate logistic regression with backward elimination, which were further validated using ROC and calibration curves. Interaction terms were added to assess the possible modifications in predictive performance of ratios across various subgroups of patients.

Results

NLPR, 48 h post ICU admission (p < 0.001, OR: 7.3436, 95% CI: 3.2986–17.2619) and NLPR changes during the first 48 h of ICU admission (p = 0.018, OR: 2.3826, 95% CI: 1.2069–6.7112), were significant predictors of ICU mortality in the multivariate logistic regression models. The model, including 48-h NLPR, had the highest AUC of ROC, calibration slope, and lowest AIC (0.8671, 0.8622, and 229.12, respectively). Also, the predictive performance of NLPR dynamic changes decreases significantly among patients with a background of CVA.

Conclusions

NLPR level, 48 h post-ICU admission and its dynamic changes during the first 48 h of ICU stay, significantly predict ICU mortality among heterogeneous critically ill patients. These findings can serve as practical and accessible predictors of ICU mortality, particularly in settings, where traditional scoring systems may not be routinely available.

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