摘要
Background:Multiple organ dysfunction syndrome(MODS)is a critical complication in trauma-induced sepsis patients and is associated with a high mortality rate.This study aimed to develop and validate predictive models for MODS in this patient population using a nomogram and machine learning approaches.Methods:This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care-IV 2.2 database,focusing on trauma patients diagnosed with sepsis within the first day of intensive care unit(ICU)admission.Predictive variables were extracted from the initial 24 h of ICU data.The dataset(2008–2019)was divided into a training set(2008–2016)and a temporal validation set(2017–2019).Feature selection was conducted using the Boruta algorithm.Predictive models were developed and validated using a nomogram and various machine learning techniques.Model performance was evaluated based on discrimination,calibration,and decision curve analysis.Results:Among 1295 trauma patients with sepsis,349(26.95%)developed MODS.The 28-day mortality rates were 11.21%for non-MODS patients and 23.82%for MODS patients.Key predictors of MODS included the simplified acute physiology score II score,use of mechanical ventilation,and vasopressor administration.In temporal validation,all models significantly outperformed traditional scoring systems(all P<0.05).The nomogram achieved an area under the curve(AUC)of 0.757(95%confidence interval[CI]:0.700 to 0.814),while the random forest model demonstrated the highest performance with an AUC of 0.769(95%CI:0.712 to 0.826).Calibration plots showed excellent agreement between predicted and observed probabilities,and decision curve analysis indicated a consistently higher net benefit for the newly developed models.Conclusion:The nomogram and machine learning models provide enhanced predictive accuracy for MODS in trauma-induced sepsis patients compared to traditional scoring systems.These tools,accessible via web-based applications,have the potential to improve early risk stratification and guide clinical decision-making,ultimately enhancing outcomes for trauma patients.Further external validation is recommended to confirm their generalizability.
