Neurocritical care · 2026
Automated Derivation of Cerebral Performance Category at Hospital Discharge After Cardiac Arrest Using Natural Language Processing and Machine Learning.
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Furlow Sophie, Houston Parker, Bao Kevin, Zhou Bo, Velasquez Gerardo, Peterson Katherine, Hayman Adeline, Bains Amanjot, Lieberman Ori, Hendrickson Carolyn M, Zier Lucas, Singhal Neel S, Hemphill J Claude, Zafar Sahar, Westover M Brandon, Fernandes Marta, Amorim Edilberto
Аннотация
BACKGROUND: Clinical notes in the electronic health record (EHR) contain rich information that may be leveraged for automated prognostication using natural language processing (NLP) and machine learning. We evaluated the performance and potential biases of an NLP-based machine learning model for deriving cerebral performance category (CPC) for comatose patients following cardiac arrest using EHR clinical notes.
METHODS: We conducted a retrospective cohort study of adult patients who were comatose after in-hospital or out-of-hospital cardiac arrest across three academic hospitals in the USA. Neurological outcomes were assessed at hospital discharge, with CPC scores of 1-3 indicating good outcomes and 4-5 indicating poor outcomes. An NLP-based logistic regression model was trained on 249 patients using all clinical notes throughout hospitalization. Model performance was evaluated on a holdout set of 108 patients. Performance was also evaluated on the holdout set using only notes documented within the first 24 h of hospitalization.
RESULTS: Using all clinical notes, the model achieved an overall accuracy of 81%, with an area under the receiver operating characteristic curve (AUROC) of 0.90 and an area under the precision-recall curve (AUPRC) of 0.89. When restricted to notes documented within the first 24 h of hospitalization, model accuracy decreased to 74%, with AUROC of 0.83 and AUPRC of 0.83. The model demonstrated higher precision in predicting poor neurological outcomes, particularly among patients who underwent withdrawal of life-sustaining therapy.
CONCLUSIONS: An NLP-based machine learning model demonstrated high accuracy in deriving CPC for comatose patients after cardiac arrest using EHR clinical notes. Clinical documentation within 24 h after cardiac arrest contained substantial prognostic signals about neurological outcomes, reflecting a combination of patient biology, clinician assessment, and potentially early prognostic framing. Future research should incorporate multimodal data and employ interpretable advanced language models to increase accuracy toward fully automatable CPC derivation.
METHODS: We conducted a retrospective cohort study of adult patients who were comatose after in-hospital or out-of-hospital cardiac arrest across three academic hospitals in the USA. Neurological outcomes were assessed at hospital discharge, with CPC scores of 1-3 indicating good outcomes and 4-5 indicating poor outcomes. An NLP-based logistic regression model was trained on 249 patients using all clinical notes throughout hospitalization. Model performance was evaluated on a holdout set of 108 patients. Performance was also evaluated on the holdout set using only notes documented within the first 24 h of hospitalization.
RESULTS: Using all clinical notes, the model achieved an overall accuracy of 81%, with an area under the receiver operating characteristic curve (AUROC) of 0.90 and an area under the precision-recall curve (AUPRC) of 0.89. When restricted to notes documented within the first 24 h of hospitalization, model accuracy decreased to 74%, with AUROC of 0.83 and AUPRC of 0.83. The model demonstrated higher precision in predicting poor neurological outcomes, particularly among patients who underwent withdrawal of life-sustaining therapy.
CONCLUSIONS: An NLP-based machine learning model demonstrated high accuracy in deriving CPC for comatose patients after cardiac arrest using EHR clinical notes. Clinical documentation within 24 h after cardiac arrest contained substantial prognostic signals about neurological outcomes, reflecting a combination of patient biology, clinician assessment, and potentially early prognostic framing. Future research should incorporate multimodal data and employ interpretable advanced language models to increase accuracy toward fully automatable CPC derivation.