Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage

 What the fuck good does this prediction do? You're predicting to the current absolutely appalling full recovery rate of 10%. Do you tell your patients that? Or are you just leaving them in the dark with this statement? 'All strokes are different,all stroke recoveries are different.' Anyone using that is using it to justify their incompetence in not initiating research to get to 100% recovery.

Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage

Jude P J Savarraj  ¹ , Georgene W Hergenroeder  ¹ , Liang Zhu  ¹ , Tiffany Chang  ¹ , Soojin Park  ¹ , Murad Megjhani  ¹ , Farhaan S Vahidy  ¹ , Zhongming Zhao  ¹ , Ryan S Kitagawa  ¹ , H Alex Choi  ²

Affiliations

• PMID: 33184232
• DOI: 10.1212/WNL.0000000000011211

Abstract

Objective: To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH).

Methods: ML models and standard models (SMs) were trained to predict DCI and functional outcomes with data collected within 3 days of admission. Functional outcomes at discharge and at 3 months were quantified using the modified Rankin Scale (mRS) for neurologic disability (dichotomized as good [mRS ≤ 3] vs poor [mRS ≥ 4] outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SMs, and clinicians were retrospectively compared.

Results: DCI status, discharge, and 3-month outcomes were available for 399, 393, and 240 participants, respectively. Prospective clinician (an attending, a fellow, and a nurse) prognostication of 3-month outcomes was available for 90 participants. ML models yielded predictions with the following area under the receiver operating characteristic curve (AUC) scores: 0.75 ± 0.07 (95% confidence interval [CI] 0.64-0.84) for DCI, 0.85 ± 0.05 (95% CI 0.75-0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI 0.81-0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI -0.02 to 0.4) for DCI, by 0.07 ± 0.03 (95% CI -0.0018 to 0.14) for discharge outcomes, and by 0.14 (95% CI 0.03-0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes.

Conclusion: ML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential(Potential is NOT GOOD ENOUGH, we needs protocols that deliver results. What the fuck are you doing to get there?) to improve SAH management.

© 2020 American Academy of Neurology.