Predicting acute kidney injury in septic shock patients using inflammatory indices in the intensive care unit

Abstract

BACKGROUND

Acute kidney injury (AKI) is a prevalent and common complication in critically ill patients with septic shock, associated with increased morbidity, mortality, and healthcare resource utilization in the intensive care unit (ICU). While inflammatory indices derived from standard laboratory tests – such as the neutrophil-to-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil percentage to albumin ratio (NPAR) and aggregate index of systemic inflammation (AISI) – have emerged as promising biomarkers for systemic immune activation in critical illness, their direct value as predictors of AKI in large ICU cohorts remains uncertain.

AIM

To evaluate the predictive value of inflammatory indices derived from standard laboratory tests as predictors of AKI in ICU patients with septic shock.

METHODS

This retrospective cohort study utilized the eICU Collaborative Research Database, including adult patients with septic shock admitted to over 200 ICUs across the United States from 2014 to 2015. Patients with pre-existing end-stage renal disease, death within 24 hours, or insufficient data for inflammatory indices were excluded. Inflammatory markers (NLR, PLR, MLR, NPAR, SII, SIRI, AISI) and clinical variables were analyzed. Multivariable logistic regression, principal component analysis, and multilayer perceptron neural network modeling were employed to identify independent predictors of AKI, defined by Kidney Disease Global Outcomes criteria.

RESULTS

Among 12660 septic shock patients, 6552 (51.7%) developed AKI during their ICU stay. Patients with AKI were older, had higher body mass index and Sequential Organ Failure Assessment scores, and a greater burden of comorbidities such as chronic kidney disease and diabetes. Univariate analysis showed significantly higher levels of NLR, MLR, SII, NPAR, SIRI, and AISI in the AKI group, suggesting an association between systemic inflammation and kidney injury. However, these indices displayed strong multicollinearity with other clinical and laboratory variables. In logistic regression, traditional predictors such as baseline serum creatinine, blood urea nitrogen, Sequential Organ Failure Assessment score, chronic kidney disease, vasopressor use, and selected comorbidities remained independently associated with AKI, while most individual inflammatory indices did not retain independent significance due to multicollinearity. To address this, principal component analysis employed, which identified three major components – an inflammatory/hematological component, a metabolic/renal/inflammatory component, and an electrolyte/age component. Incorporating these composite dimensions into predictive models significantly improved discrimination for AKI risk. Neural network models further expounded the contribution of both clinical factors and the combined inflammatory/metabolic dimension to accurate AKI prediction, capturing complex interactions and non-linear relationships not evident in traditional regression models.

CONCLUSION

In ICU patients with septic shock, composite inflammatory indices are elevated in those who develop AKI and may serve as important markers of risk. However, after accounting for multicollinearity and confounding, these markers alone offer limited incremental predictive value over traditional clinical and laboratory risk factors.

Keywords: Acute kidney injury; Septic shock; Inflammatory indices; eICU Collaborative Research Database; Principal component analysis; Machine learning; Neutrophil-to-lymphocyte ratio; Systemic immune-inflammation index; Aggregate index of systemic inflammation; Monocyte-to-lymphocyte ratio

Core Tip: Composite inflammatory markers are elevated in patients who develop acute kidney injury. However, due to heterogeneity of septic shock, multicollinearity and nonlinear relationships, these markers alone offer limited incremental predictive value. Neural network models further expounded the contribution of both clinical factors and the combined inflammatory/metabolic dimension to accurate acute kidney injury prediction, capturing complex interactions and non-linear relationships not evident in traditional regression models. Implementation of supervised and unsupervised machine learning together may offer further insights.