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

## TL;DR

This study develops a machine learning model to predict consciousness disorders in stroke patients in the ICU, using clinical data to identify high-risk patients and guide early interventions.

## Contribution

The novel contribution is the development and validation of a LightGBM-based predictive model for consciousness disorders in ICU stroke patients using MIMIC-IV data.

## Key findings

- Length of hospital stay, mechanical ventilation, nasogastric tube, and SOFA score are independent predictors of consciousness disorders in ICU stroke patients.
- The LightGBM model outperformed other algorithms with an AUC of 0.824 in training and 0.795 in validation sets.
- Calibration curves and decision curve analysis confirmed the model's strong clinical utility and probability calibration.

## Abstract

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.

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.

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 p < 0.001): length of hospital stay (p < 0.001, 95% confidence interval [CI]: 1.02–1.06), mechanical ventilation (p < 0.001, 95% CI: 0.29–0.72), nasogastric tube (p < 0.001, 95% CI: 1.61–3.79), and Sequential Organ Failure Assessment (SOFA) score (p < 0.001, 95% CI: 1.47–1.74). Among the 11 machine learning models, the Light Gradient Boosting Machine (LightGBM) model exhibited the optimal performance across three dimensions: accuracy (area under the curve [AUC] = 0.824 in the training set, AUC = 0.795 in the validation set), stability (consistency between training and validation set results), and probability calibration (Brier score = 0.132 in the training set, Brier score = 0.140 in the validation set). Calibration curves demonstrated excellent agreement between the model’s predictions and ideal values in both datasets, and DCA confirmed its favorable clinical utility.

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.