ArticleBMC nephrology2025
Prediction model for postoperative acute kidney injury in non-cardiac surgical patients: a retrospective cohort study.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAcute kidney injury (AKI) is a prevalent and severe complication following non-cardiac surgery, often leading to poor outcomes. Despite the critical role of inflammation in AKI pathogenesis, reliable preoperative predictive models remain elusive. The pan-immune inflammation value (PIV), a novel index that integrates counts of neutrophils, platelets, lymphocytes, and monocytes, provides a comprehensive reflection of systemic inflammation. This study aimed to develop and validate a clinical prediction model for postoperative AKI (PO-AKI) in non-cardiac surgical patients.
methodsThis retrospective study included adult patients who underwent non-cardiac surgery under general anaesthesia. The objective was to construct a model to predict PO-AKI. The statistical analysis focused on model construction and validation. LASSO regression was employed for variable selection to identify the most parsimonious set of predictors. The model's performance was evaluated based on its discriminative ability (AUC), with calibration and decision curve analysis used to assess its clinical utility.
resultsThe cohort consisted of 1,164 adult patients. AKI was diagnosed in 8.4% of patients. The primary outcome, the performance of the prediction model, showed an AUC of 0.70. The model incorporated PIV and emergency surgery. The secondary outcome, the discriminative ability of PIV alone, yielded an AUC of 0.691. The model demonstrated good calibration and provided a clinical net benefit across a wide range of threshold probabilities.
conclusionWe developed and validated a prediction model for PO-AKI. This model, which integrates PIV and emergency surgery, serves as an effective tool for preoperative risk stratification, facilitating the identification of high-risk patients and optimizing perioperative management.
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