ArticleBMC medical informatics and decision making2026
Machine learning prediction of postoperative acute kidney injury in aortic dissection patients using dynamic inflammatory markers and clinical features.
Article in BMC medical informatics and decision making, 2026. 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
objectiveTo develop and validate a predictive model for acute kidney injury (AKI) after aortic dissection (AD) repair by integrating the dynamic neutrophil-to-lymphocyte ratio (ΔNLR) with key clinical variables.
methodsThis retrospective cohort study included 720 patients who underwent AD surgery. Patients were randomly split into training (70%) and validation (30%) cohorts. AKI was defined per RIFLE criteria. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection from demographics, medical history, imaging, surgical data, and inflammatory ratios (including preoperative, postoperative, and Δ values). Multivariable logistic regression built the final model, evaluated by discrimination (C-statistic), calibration (plots, Hosmer-Lemeshow test), and clinical utility (decision curve analysis).
resultsThe incidence of postoperative AKI was 17.2%. The final model incorporated four independent predictors: ΔNLR (Odds Ratio [OR]: 2.23), preoperative platelet-to-fibrinogen ratio (PFR) (OR: 1.95), open surgery (OR: 5.37), and drinking history (OR: 1.72). The model demonstrated good and consistent discrimination, with a C-statistic of 0.751 (95% CI: 0.708–0.794, p < 0.001) in the training cohort and 0.732 (95% CI: 0.673–0.791, p < 0.001) in the validation cohort. Calibration curves showed excellent agreement between predicted and observed probabilities (Hosmer-Lemeshow test p = 0.172). Decision curve analysis confirmed significant clinical net benefit across a clinically relevant range of risk thresholds (approximately 5% to 80%).
conclusionWe developed a robust predictive model for AKI after AD surgery, highlighting the critical value of dynamic inflammation monitoring via ΔNLR. This practical tool facilitates early identification of high-risk patients, potentially enabling timely preventive strategies to improve postoperative outcomes. External validation is warranted to confirm generalizability. CLINICAL TRIAL NUMBER: Not applicable.
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