Early prediction of in-hospital acute brain injury following cardiac arrest using machine learning: development and external validation.

BACKGROUND

Neurological injury is common after cardiac arrest (CA), but early and accurate assessment remains challenging. Existing evaluation methods often lack sensitivity and specificity. This study aims to develop and validate an interpretable machine learning model for early and reliable assessment of brain injury and its prognostic relevance in ICU patients.

METHODS

In this study, we analyzed 1,419 CA patients from the MIMIC-IV database, randomly split into training and validation cohorts. Six machine learning (ML) algorithms-logistic regression (LR), XGBoost, artificial neural network (ANN), random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN) were developed and evaluated. The top-performing model was interpreted using SHapley Additive exPlanations (SHAP), identifying eight key predictors of brain injury. Finally, clinical data from cardiac arrest patients at our hospital were collected for external validation to assess the model's generalizability and practical utility in an ICU setting.

RESULTS

Among the six machine learning models, the Random Forest model achieved the highest numerical performance in both the internal test and external validation cohorts. In the internal test cohort, Random Forest achieved an ACC of 88.7%, sensitivity of 80.0%, specificity of 90.6%, F1 score of 0.714, NPV of 95.5%, PPV of 64.5%, and AUC of 0.901. In the external validation cohort, Random Forest achieved an ACC of 88.8%, sensitivity of 77.8%, specificity of 90.8%, F1 score of 0.677, NPV of 95.8%, PPV of 60.0%, and AUC of 0.889. SHAP analysis identified eight influential predictors: age, hemoglobin, OASIS, temperature, blood urea nitrogen, white blood cell count, non-invasive diastolic pressure, and non-invasive systolic pressure.

CONCLUSION

Machine learning offers strong potential for early prediction of acute brain injury in in-hospital cardiac arrest patients. Our interpretable model addresses the "black box" concern by providing transparent insights into key predictive features.