Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients. Issue 11 (29th September 2020)

• Record Type: Journal Article
• Authors: Yu, Duo; Williams, George W.; Aguilar, David; Yamal, José‐Miguel; Maroufy, Vahed; Wang, Xueying; Zhang, Chenguang; Huang, Yuefan; Gu, Yuxuan; Talebi, Yashar; Wu, Hulin
• Is Part Of: Annals of clinical and translational neurology. Volume 7:Issue 11(2020)
• Page Start: 2178
• Page End: 2185
• Publication Date: 2020-09-29
• Subjects: Nervous system -- Diseases -- Periodicals; Neurology -- Periodicals; 616.8005
• DOI: 10.1002/acn3.51208
• Languages: English
• ISSNs: 2328-9503

Abstract

Abstract: Objective: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients. Methods: The cohort included nontraumatic SAH patients treated with vasopressors for presumed DCI from a large EHR database, the Cerner Health Facts ® EMR database (2000–2014). The outcome of interest was the adverse outcome, defined as death in hospital or discharged to hospice. Machine learning‐based models were developed and primarily assessed by area under the receiver operating characteristic curve (AUC). Results: A total of 2467 nontraumatic SAH patients (64% female; median age [interquartile range]: 56 [47–66]) who were treated with vasopressors for presumed DCI were included in the study. 934 (38%) patients died or were discharged to hospice. The model achieved an AUC of 0.88 (95% CI, 0.84–0.92) with only the initial 24 h EHR data, and 0.94 (95% CI, 0.92–0.96) after the next 24 h. Interpretation: EHR data and machine learning models can accurately predict the risk of the adverse outcome for critically ill nontraumatic SAH patients. It is possible to use EHR data and machine learning techniques to help with clinical decision‐making.