Development and Validation of a Nomogram for Predicting Sepsis-Associated Acute Respiratory Distress Syndrome.

Background: Sepsis-associated acute respiratory distress syndrome (ARDS) is a critical condition with high morbidity and mortality. Early identification of patients at high risk is crucial for timely intervention. This study aimed to develop and validate a nomogram for predicting the risk of ARDS in patients with sepsis.

Methods: A total of 308 patients with sepsis were retrospectively enrolled as the development cohort, and 132 patients were enrolled as an external validation cohort. Patients were categorized into ARDS and non-ARDS groups. Univariate and multivariate logistic regression analyses identified independent risk factors for ARDS in the development cohort, which were used to construct a nomogram. The nomogram's performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and the Hosmer-Lemeshow (H-L) test.

Results: In the development cohort, 104 patients (33.77%) developed ARDS. Pulmonary infection (Odds Ratio [OR]=16.82), procalcitonin (PCT) (OR=2.71), tumor necrosis factor-alpha (TNF-α) (OR=1.102), oxygenation index (OR=0.861), and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (OR=1.785) were identified as independent predictors. The nomogram demonstrated excellent discrimination, with an AUC of 0.862 in the development cohort and 0.881 in the validation cohort. Calibration curves showed good agreement between predicted and observed probabilities, supported by non-significant H-L tests (P>0.05). DCA confirmed the nomogram's clinical utility across a wide range of risk thresholds.

Conclusion: The developed nomogram, incorporating five accessible variables, is a reliable and practical tool for predicting the risk of ARDS in patients with sepsis. This model can assist clinicians in identifying high-risk individuals for early preventive measures and personalized management.