Evidence map›Paper›PMID 37070145›Full record

ArticleInterventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences2025

Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach.

Reza M Taghavi, Guangming Zhu, Max Wintermark, Gabriella M Kuraitis, Eric S Sussman, Benjamin Pulli, Brook Biniam, Sophie Ostmeier, Gary K Steinberg, Jeremy J Heit

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

Article in Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
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 5 institutions in 2 countries.

Reza M TaghaviDepartment of Medicine, University of California at Davis Medical School, Sacramento, CA, USA.
Guangming ZhuDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Max WintermarkDepartment of Neuroradiology, MD Anderson, Houston, TX, USA.
Gabriella M KuraitisDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Eric S SussmanDepartment of Neurosurgery, University of Connecticut, Hartford, CT, USA.
Benjamin PulliDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Brook BiniamDepartment of Medicine, University of Ottawa Medical School, Ottawa, Canada.
Sophie OstmeierDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Gary K SteinbergDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Jeremy J HeitDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0003-1055-8000
Stanford University · USThe University of Texas MD Anderson Cancer Center · USUniversity of California Davis Medical Center · USUniversity of Connecticut · USUniversity of Ottawa · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundAneurysmal subarachnoid hemorrhage results in significant mortality and disability, which is worsened by the development of delayed cerebral ischemia. Tests to identify patients with delayed cerebral ischemia prospectively are of high interest.ObjectiveWe created a machine learning system based on clinical variables to predict delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients. We also determined which variables have the most impact on delayed cerebral ischemia prediction using SHapley Additive exPlanations method.Methods500 aneurysmal subarachnoid hemorrhage patients were identified and 369 met inclusion criteria: 70 patients developed delayed cerebral ischemia (delayed cerebral ischemia+) and 299 did not (delayed cerebral ischemia-). The algorithm was trained based upon age, sex, hypertension (HTN), diabetes, hyperlipidemia, congestive heart failure, coronary artery disease, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain placement. Random Forest was selected for this project, and prediction outcome of the algorithm was delayed cerebral ischemia+. SHapley Additive exPlanations was used to visualize each feature's contribution to the model prediction.ResultsThe Random Forest machine learning algorithm predicted delayed cerebral ischemia: accuracy 80.65% (95% CI: 72.62-88.68), area under the curve 0.780 (95% CI: 0.696-0.864), sensitivity 12.5% (95% CI: -3.7 to 28.7), specificity 94.81% (95% CI: 89.85-99.77), PPV 33.3% (95% CI: -4.39 to 71.05), and NPV 84.1% (95% CI: 76.38-91.82). SHapley Additive exPlanations value demonstrated Age, external ventricular drain placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for delayed cerebral ischemia. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and external ventricular drain placement increased risk of delayed cerebral ischemia.ConclusionMachine learning models based upon clinical variables predict delayed cerebral ischemia with high specificity and good accuracy.

Indexed as

Aneurysm, RupturedBrain IschemiaIntracranial AneurysmMachine LearningSubarachnoid HemorrhageAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedPredictive Value of TestsRisk Factorsartificial intelligenceDelayed cerebral ischemia

Identifiers

PMID37070145
PMCPMC12202946
OpenAlexW4366239160

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.