ArticleJournal of inflammation research2026
Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Acute Respiratory Distress Syndrome.
Article in Journal of inflammation research, 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
Background: Acute kidney injury (AKI) represents a life-threatening complication of acute respiratory distress syndrome (ARDS), yet early predictive tools remain critically limited. This study aimed to develop and validate a predictive nomogram for AKI in patients with ARDS. Methods: This retrospective cohort study enrolled 944 eligible patients with ARDS. The development cohort included 708 patients (August 2017-November 2024), and the validation cohort comprised 236 patients (December 2024-August 2025). Logistic regression analysis was performed to identify risk factors and constructed a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Results: Nine independent risk factors for AKI were identified: age (OR 1.023, 95% CI 1.006-1.039; p = 0.006), diabetes mellitus (DM) (OR 1.862, 95% CI 1.140-3.044; p = 0.013), immunosuppression (OR 2.455, 95% CI 1.329-4.535; p = 0.004), invasive mechanical ventilation (OR 4.193, 95% CI 2.700-6.511; p < 0.001), lactate dehydrogenase > 500 U/L (OR 1.716, 95% CI 1.015-2.900; p = 0.044), B-type natriuretic peptide > 500 pg/mL (OR 2.847, 95% CI 1.803-4.494; p < 0.001), total bilirubin (OR 1.022, 95% CI 1.013-1.031; p < 0.001), baseline serum creatinine (OR 1.036, 95% CI 1.024-1.048; p < 0.001), and prothrombin time (OR 1.222, 95% CI 1.154-1.293; p < 0.001). The nomogram demonstrated excellent discriminative ability, with AUCs of 0.904 in the development cohort and 0.892 in the validation cohort. Calibration curves and DCA confirmed favorable clinical utility. Conclusion: We developed and validated a nine-factor predictive nomogram that incorporates readily available clinical variables. This tool may facilitate early identification of AKI risk in patients with ARDS and support clinical decision-making.
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