J Thorac Dis. 2026 May 31;18(5):502. doi: 10.21037/jtd-2026-1-0291. Epub 2026 Apr 30.
ABSTRACT
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, HCO3 –, respiratory rate, activated partial thromboplastin time, procalcitonin, and inferior vena cava, were included in the Cox model. The model yielded a C-index of 0.793 in the training cohort and 0.748 in the validation cohort. In the training cohort, the area under the curve (AUC) of the predictive model was 0.824 [95% confidence interval (CI): 0.726-0.923] for the 15-day outcome and 0.809 (95% CI: 0.676-0.942) for the 28-day outcome. In the validation cohort, the corresponding AUCs were 0.721 (95% CI: 0.501-0.942) and 0.737 (95% CI: 0.503-0.971). Based on the constructed model, risk stratification was carried out via X-tile software. According to the optimal cutoff value of 118.4, individuals were categorized into high- and low-risk groups. Statistical analysis demonstrated that the risk of MODS was significantly elevated in the high-risk group in comparison to the low-risk group (P<0.05).
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.
PMID:42306759 | PMC:PMC13266719 | DOI:10.21037/jtd-2026-1-0291
