AUTHOR=Lin Hui , Ren Yilin , Cui Jing , Guo Junnan , Wang Mengzhu , Wang Lihua , Su Xiaole , Qiao Xi 

TITLE=Nomogram risk prediction model for acute respiratory distress syndrome following acute kidney injury

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1563425

DOI=10.3389/fmed.2025.1563425

ISSN=2296-858X

ABSTRACT=BackgroundAcute respiratory distress syndrome (ARDS), a severe form of respiratory failure, can be precipitated by acute kidney injury (AKI), leading to a significant increase in mortality among affected patients. This study aimed to identify the risk factors for ARDS and construct a predictive nomogram.MethodsWe conducted a retrospective analysis of 1,241 AKI patients admitted to the Second Hospital of Shanxi Medical University from August 25, 2016, to December 31, 2023. The patients were divided into a study cohort (1,012 cases, including 108 with ARDS) and a validation cohort (229 cases, including 23 with ARDS). Logistic regression analysis was employed to identify the risk factors for ARDS, which were subsequently incorporated into the development of a nomogram. The predictive performance of the nomogram was assessed by AUC, calibration plots, and decision curve analyses, with external validation also performed.ResultsSix risk factors were identified and included in the nomogram: older age (OR = 1.020; 95%CI = 1.005–1.036), smoking history (OR = 1.416; 95%CI = 1.213–1.811), history of diabetes mellitus (OR = 1.449; 95%CI = 1.202–1.797), mean arterial pressure (MAP; OR = 1.165; 95%CI = 1.132–1.199), higher serum uric acid levels (OR = 1.002; 95%CI = 1.001–1.004), and higher AKI stage [(stage 1: reference), (stage 2: OR = 11.863; 95%CI = 4.850–29.014), (stage 3: OR = 41.398; 95%CI = 30.840–52.731)]. The AUC values were 0.951 in the study cohort and 0.959 in the validation cohort. Calibration and decision curve analyses confirmed the accuracy and clinical utility of the nomogram.ConclusionThe nomogram, which integrates age, smoking history, diabetes mellitus history, MAP, and AKI stage, predicts the risk of ARDS in patients with AKI. This tool may aid in early detection and facilitate clinical decision-making.