[Development and validation of a nomogram model for extrapulmonary risk prediction in sepsis-associated acute...

To investigate the independent predictive value of extrapulmonary organ dysfunction for sepsis-associated acute respiratory distress syndrome (SA-ARDS) after controlling for baseline factors such as pulmonary infection, and to construct a corresponding nomogram prediction model and validate its predictive performance.

A retrospective analysis was conducted on clinical data from patients with sepsis admitted to the First Affiliated Hospital of Xinjiang Medical University from April 2023 to March 2025. Patients were randomly divided into a training set and a validation set at a 7 : 3 ratio. Additionally, data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database as an external validation set. The consistency of clinical data among the training set, validation set, and external validation set was compared. In the training set, patients were divided into ARDS group and non-ARDS group according to the 2012 Berlin definition of ARDS. Variables with significant differences in univariate analysis were incorporated into LASSO and Logistic regression models to identify independent risk factors for SA-ARDS, which were then used to construct a nomogram prediction model. The discriminative ability, calibration, and clinical utility of the model were evaluated using receiver operator characteristic curve (ROC curve), calibration curve, and decision curve analysis in the training set, validation set, and external validation set, respectively.

391 sepsis patients were enrolled from the hospital, including 273 in the training set and 118 in the validation set. An additional 400 patients with sepsis were extracted from the MIMIC-IV database as the external validation set. The three groups were balanced and comparable in terms of clinical data (all P>0.05). Of the 273 patients in the training set, 189 developed ARDS. There were statistically significant differences in proportions of patients with pulmonary infection and septic shock, mean arterial pressure, serum lactate, platelet count, procalcitonin, total bilirubin, albumin, blood urea nitrogen, serum creatinine, brain natriuretic peptide, Glasgow Coma Score (GCS), extrapulmonary Sequential Organ Failure Assessment (SOFA) total score, SOFA total score, Acute Physiology and Chronic Health Evaluation II (APACHE II), and SOFA coagulation, hepatic, cardiovascular, renal, and neurological subscores between the ARDS and non-ARDS groups (all P<0.05). LASSO regression identified 13 potential predictors. Multivariate Logistic regression analysis revealed that elevated SOFA cardiovascular score [odds ratio (OR)=1.27, 95% confidence interval (95%CI) was 1.01-1.60, P=0.038], elevated SOFA coagulation score (OR=1.58, 95%CI was 1.08-2.30, P=0.019), elevated SOFA neurological score (OR=1.34, 95%CI was 1.10-1.63, P=0.003), concomitant pulmonary infection (OR=2.63, 95%CI was 1.25-5.52, P=0.011), and elevated APACHE II score (OR=1.40, 95%CI was 1.28-1.53, P<0.001) were independent risk factors for ARDS. A nomogram prediction model was constructed based on these five factors. ROC curve analysis demonstrated that the area under the curve (AUC) of the prediction model in the training set, validation set, and external validation set was 0.90 (95%CI was 0.87-0.94), 0.91 (95%CI was 0.86-0.96), and 0.92 (95%CI was 0.89-0.94), respectively. Calibration curve and decision curve analysis indicated that the model exhibited good calibration and clinical utility.

After comprehensively adjusting for confounding factors including pulmonary infection and the severity of systemic disease, the SOFA cardiovascular, coagulation, and neurological subscores were identified as independent extrapulmonary influencing factors for SA-ARDS. The nomogram model incorporating these factors along with pulmonary infection and APACHE II score demonstrates favorable predictive performance and promising clinical application prospects, offering new insights for the clinical diagnosis and management of SA-ARDS.