Journal of inflammation research · 2026
Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Acute Respiratory Distress Syndrome.
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Li Yinhui, Lin Hui, Song Yiheng, Niu Jie, Yao Kaixin, Xu Lujun, Wang Lihua, Su Xiaole, Zhao Hui, Qiao Xi
Аннотация
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.
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.