Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage

 Will you please provide recovery protocols rather than this prediction of failure crapola.  Just maybe you might want to talk to survivors, they don't give a shit about your failure to recover predictions.  When you are the 1 in 4 per WHO that has a stroke will you be satisfied with failure to recover predictions?

Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage

View ORCID ProfileJude PJ Savarraj, View ORCID ProfileGeorgene W. Hergenroeder, Liang Zhu, Tiffany Chang, Soojin Park, View ORCID ProfileMurad Megjhani, View ORCID ProfileFarhaan S Vahidy, View ORCID ProfileZhongming Zhao, Ryan S. Kitagawa, View ORCID ProfileH Alex Choi

First published November 12, 2020, DOI: https://doi.org/10.1212/WNL.0000000000011211

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• Article
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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 (SM) 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 neurological disability (dichotomized as ‘good’ (mRS≤3) vs ‘bad’ (mRS≥4) outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SM and clinicians are retrospectively compared.

Results: DCI status, discharge, and 3-month outcomes were available for 399, 393 and 240 subjects respectively. Prospective clinician (an attending, a fellow and a nurse) prognostication of 3-month outcomes was available for 90 subjects. ML models yielded predictions with the following AUC (area under the receiver operating curve) scores: 0.75 ± 0.07 (95% CI: 0.64 to 0.84) for DCI, 0.85 ± 0.05 (95% CI: 0.75 to 0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI: 0.81 to 0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI: -0.02–0.4) for DCI, by 0·07 ± 0.03 (95% CI: -0.0018–0.14) for discharge outcomes, 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 to improve SAH management.

• Received August 26, 2019.
• Accepted in final form September 21, 2020.

• © 2020 American Academy of Neurology