Development and validation of a risk prediction model for consciousness disorders in stroke patients in the intensive care unit (ICU): a retrospective study.

Objective: We used data from stroke patients in the Medical Information Mart for Intensive Care (MIMIC) database to develop and validate risk prediction models for consciousness disorders in stroke patients using 11 machine learning algorithms. It aims to provide a basis for clinical assessment of consciousness changes in stroke patients. Methods: Data of 2,434 stroke patients were extracted from the MIMIC-IV database and randomly split into a training set and a validation set at a 7:3 ratio. Multivariate logistic regression was employed to identify independent predictors, and 11 machine learning algorithms were used to construct predictive models for post-stroke consciousness disorders. Calibration curves were applied to validate the calibration performance of the models, while decision curve analysis (DCA) was utilized to evaluate their clinical applicability, ultimately determining the optimal predictive model. Results: A total of 2,434 ICU stroke patients were included, with 1,706 assigned to the training set and 728 to the validation set. Logistic regression analysis identified four independent predictors (all Conclusion: Multivariate analysis revealed that length of hospital stay, mechanical ventilation, nasogastric tube, and SOFA score are independent predictors of consciousness disorders in ICU stroke patients. The model constructed using the LightGBM algorithm showed the best comprehensive performance and can serve as an intuitive, personalized clinical tool. It assists healthcare providers in the early identification and risk stratification of stroke patients at high risk of consciousness disorders, thereby supporting the timely implementation of interventions to reduce the incidence of complications.