Rescue therapy for vasospasm following aneurysmal subarachnoid hemorrhage: a propensity score-matched analysis with machine learning

Michael L. Martini, Sean N. Neifert, William H. Shuman, Emily K. Chapman, Alexander J. Schüpper, Eric K. Oermann, J. Mocco, Michael Todd, James C. Torner, Andrew Molyneux, Stephan Mayer, Peter Le Roux, Mervyn D.I. Vergouwen, Gabriel J.E. Rinkel, George K.C. Wong, Peter Kirkpatrick, Audrey Quinn, Daniel Hänggi, Nima Etminan, Walter M. van den BerghBlessing N.R. Jaja, Michael Cusimano, Tom A. Schweizer, Jose I. Suarez, Hitoshi Fukuda, Sen Yamagata, Benjamin Lo, Airton Leonardo de Oliveira Manoel, Hieronymus D. Boogaarts, R. Loch Macdonald

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


OBJECTIVE Rescue therapies have been recommended for patients with angiographic vasospasm (aVSP) and delayed cerebral ischemia (DCI) following subarachnoid hemorrhage (SAH). However, there is little evidence from randomized clinical trials that these therapies are safe and effective. The primary aim of this study was to apply game theory-based methods in explainable machine learning (ML) and propensity score matching to determine if rescue therapy was associated with better 3-month outcomes following post-SAH aVSP and DCI. The authors also sought to use these explainable ML methods to identify patient populations that were more likely to receive rescue therapy and factors associated with better outcomes after rescue therapy. METHODS Data for patients with aVSP or DCI after SAH were obtained from 8 clinical trials and 1 observational study in the Subarachnoid Hemorrhage International Trialists repository. Gradient boosting ML models were constructed for each patient to predict the probability of receiving rescue therapy and the 3-month Glasgow Outcome Scale (GOS) score. Favorable outcome was defined as a 3-month GOS score of 4 or 5. Shapley Additive Explanation (SHAP) values were calculated for each patient-derived model to quantify feature importance and interaction effects. Variables with high SHAP importance in predicting rescue therapy administration were used in a propensity score-matched analysis of rescue therapy and 3-month GOS scores. RESULTS The authors identified 1532 patients with aVSP or DCI. Predictive, explainable ML models revealed that aneurysm characteristics and neurological complications, but not admission neurological scores, carried the highest relative importance rankings in predicting whether rescue therapy was administered. Younger age and absence of cerebral ischemia/ infarction were invariably linked to better rescue outcomes, whereas the other important predictors of outcome varied by rescue type (interventional or noninterventional). In a propensity score-matched analysis guided by SHAP-based variable selection, rescue therapy was associated with higher odds of 3-month GOS scores of 4-5 (OR 1.63, 95% CI 1.22-2.17). CONCLUSIONS Rescue therapy may increase the odds of good outcome in patients with aVSP or DCI after SAH. Given the strong association between cerebral ischemia/infarction and poor outcome, trials focusing on preventative or therapeutic interventions in these patients may be most able to demonstrate improvements in clinical outcomes. Insights developed from these models may be helpful for improving patient selection and trial design.

Original languageEnglish (US)
Pages (from-to)134-147
Number of pages14
JournalJournal of neurosurgery
Issue number1
StatePublished - Jan 2022

Bibliographical note

Publisher Copyright:
© AANS 2022.


  • delayed cerebral ischemia
  • feature importance
  • machine learning
  • propensity score matching
  • rescue therapy
  • subarachnoid hemorrhage
  • vascular disorders
  • vasospasm

PubMed: MeSH publication types

  • Journal Article


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