BMJ public health · 2026
Early prediction of postoperative delirium in major surgery patients using multimodal preoperative EHR and medical notes: a retrospective cohort study in the Indiana network for patient care.
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Ara Lena, Ben Miled Zina, Boustani Malaz, Mohanty Sanjay
Аннотация
INTRODUCTION: This study assesses the utility of integrating multimodal preoperative electronic health records (EHRs), including unstructured medical notes, for early postoperative delirium prediction. We aim to demonstrate the feasibility of this data integration to facilitate future expansion of the methodology towards risk stratification.
METHODS: Transformer-based models were trained and evaluated using EHR data (2013-2021) from the Indiana Network for Patient Care for patients undergoing major surgeries such as laparotomies, bowel resections and cardiac procedures. Delirium was assessed retrospectively using (1) International Classification of Diseases, 10th revision codes and (2) clinician/nurse assessments from both structured and unstructured documentation.
RESULTS: From an initial cohort of 35 128 control patients, postoperative delirium was identified in 3872 (assessment 1) and 15 351 (assessment 2) cases. Prevalent background comorbidities included pre-existing conditions such as hypertension, diabetes, hypothyroidism and chronic pulmonary disease. Following data balancing, our late fusion multimodal transformer model achieved an area under the receiver operating characteristic curve of 0.80. The model demonstrated increased sensitivity to patients with high disease burden, highlighting the potential of advanced architectures for using unstructured clinical data.
CONCLUSIONS: Using up to 4 years of preoperative EHR data offers the significant advantage of earlier risk identification, facilitating enhanced preoperative surgical patient care. By successfully unlocking the nuanced clinical signals within preoperative unstructured data, this study provides a robust foundation for incorporating medical notes into early postoperative delirium prediction.
METHODS: Transformer-based models were trained and evaluated using EHR data (2013-2021) from the Indiana Network for Patient Care for patients undergoing major surgeries such as laparotomies, bowel resections and cardiac procedures. Delirium was assessed retrospectively using (1) International Classification of Diseases, 10th revision codes and (2) clinician/nurse assessments from both structured and unstructured documentation.
RESULTS: From an initial cohort of 35 128 control patients, postoperative delirium was identified in 3872 (assessment 1) and 15 351 (assessment 2) cases. Prevalent background comorbidities included pre-existing conditions such as hypertension, diabetes, hypothyroidism and chronic pulmonary disease. Following data balancing, our late fusion multimodal transformer model achieved an area under the receiver operating characteristic curve of 0.80. The model demonstrated increased sensitivity to patients with high disease burden, highlighting the potential of advanced architectures for using unstructured clinical data.
CONCLUSIONS: Using up to 4 years of preoperative EHR data offers the significant advantage of earlier risk identification, facilitating enhanced preoperative surgical patient care. By successfully unlocking the nuanced clinical signals within preoperative unstructured data, this study provides a robust foundation for incorporating medical notes into early postoperative delirium prediction.