Construction and validation of a prognostic model for in-hospital multiple organ dysfunction syndrome in ICU patients with respiratory failure based on ultrasound and laboratory parameters.

Background: Severe respiratory failure (SRF) is a major cause of intensive care unit (ICU) admission, while multiple organ dysfunction syndrome (MODS) serves as a critical contributor to poor prognosis. This research examined the risk factors for in-hospital MODS in individuals with SRF based on ultrasound and laboratory parameters. A predictive model was constructed via the least absolute shrinkage and selection operator (LASSO)-Cox regression and subsequently validated. Methods: Data were collected from individuals with SRF admitted to the ICU of Wuhan Third Hospital between January 1, 2024, and May 31, 2025. LASSO regression was utilized to identify the risk factors for MODS. A Cox proportional hazards model was then established based on the selected variables by LASSO regression. The predictive performance of the models was appraised via the concordance index (C-index). Risk stratification was conducted via X-tile software, and the performance of the stratification system was assessed with the Kaplan-Meier method. Results: In total, 246 individuals with SRF were enrolled and randomly stratified into a training cohort (n=173) and a validation cohort (n=73) in a 7:3 ratio. Variables selected by LASSO regression, including pH, HCO Conclusions: This research constructed and validated a nomogram based on LASSO-Cox regression to predict the MODS risk among individuals with SRF. This nomogram may assist clinicians in identifying individuals at high risk of MODS and tailoring individualized follow-up and treatment strategies based on risk prediction, thereby improving patients' long-term outcomes.