Evidence map›Paper›PMID 33184232›Full record

ArticleNeurology2021

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

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

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 3 pooled it
3.3field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 3 syntheses or guidelines pooled it, 69 citations in OpenAlex.

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  12. Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Jude P J SavarrajFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.ORCID 0000-0003-0194-0734
Georgene W HergenroederFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.ORCID 0000-0002-6170-2191
Liang ZhuFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
Tiffany ChangFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
Soojin ParkFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
Murad MegjhaniFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.ORCID 0000-0002-7613-6444
Farhaan S VahidyFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.ORCID 0000-0002-3464-2111
Zhongming ZhaoFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.ORCID 0000-0002-3477-0914
Ryan S KitagawaFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
H Alex ChoiFrom the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY. Huimahn.A.Choi@uth.tmc.edu jude.p.savarraj@uth.tmc.edu.ORCID 0000-0001-7218-832X
The University of Texas Health Science Center · US

Funding

Multiparametric Prediction of Vasospasm after Subarachnoid HemorrhageK01ES026833 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PARK, SOOJIN · 2015 to 2019
$1.1M
NIEHS NIH HHS K01 ES026833
6 · The paper itself

Abstract

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

methodsML 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.

resultsDCI 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.

conclusionML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential to improve SAH management.

Indexed as

AdultAgedBrain IschemiaFemaleHumansMachine LearningMaleMiddle AgedPredictive Value of TestsProspective StudiesRetrospective StudiesSubarachnoid HemorrhageTime FactorsTreatment Outcome

Identifiers

PMID33184232
PMCPMC7905786
OpenAlexW3103346582

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.