Establishment and validation of a predictive model for tracheotomy in critically ill patients and analysis of the impact of different tracheotomy timing on patient prognosis

Abstract Background In critically ill patients receiving invasive mechanical ventilation (IMV), it is unable to determine early which patients require tracheotomy and whether early tracheotomy is beneficial. Methods Clinical data of patients who were first admitted to the ICU and underwent invasive ventilation for more than 24 h in the Medical Information Marketplace in Intensive Care (MIMIC)-IV database were retrospectively collected. Patients were categorized into successful extubation and tracheotomy groups according to whether they were subsequently successfully extubated or underwent tracheotomy. The patients were randomly divided into model training set and validation set in a ratio of 7:3. Constructing predictive models and evaluating and validating the models. The tracheotomized patients were divided into the early tracheotomy group ( 7 days), and the prognosis of the two groups was analyzed. Results A total of 7 key variables were screened: Glasgow coma scale (GCS) score, pneumonia, traumatic intracerebral hemorrhage, hemorrhagic stroke, left and right pupil responses to light, and parenteral nutrition. The area under the receiver operator characteristic (ROC) curve of the prediction model constructed through these seven variables was 0.897 (95% CI: 0.876–0.919), and 0.896 (95% CI: 0.866–0.926) for the training and validation sets, respectively. Patients in the early tracheotomy group had a shorter length of hospital stay, IMV duration, and sedation duration compared to the late tracheotomy group (p