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Original research
Using machine learning to optimize selection of elderly patients for endovascular thrombectomy
  1. Ali Alawieh1,2,
  2. Fadi Zaraket3,
  3. Mohamed Baker Alawieh4,
  4. Arindam Rano Chatterjee5,
  5. Alejandro Spiotta2
  1. 1 Medical Scientist Training Program, Medical University of South Carolina, Charleston, South Carolina, USA
  2. 2 Department of Neurosurgery, Medical University of South Carolina, Charleston, South Carolina, USA
  3. 3 Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
  4. 4 Department of Electrical and Computer Engineering, University of Texas, Austin, Texas, USA
  5. 5 Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
  1. Correspondence to Ali Alawieh and Dr Alejandro Spiotta, Department of Neurosurgery, Medical University of South Carolina, Charleston, SC 29425, USA; alawieh{at}, spiotta{at}


Background Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have reported higher morbidity and mortality in elderly patients than in their younger counterparts.

Objective To use machine learning algorithms to develop a clinical decision support tool that can be used to select elderly patients for ET.

Methods We used a retrospectively identified cohort of 110 patients undergoing ET for AIS at our institution to train a regression tree model that can predict 90-day modified Rankin Scale (mRS) scores. The identified algorithm, termed SPOT, was compared with other decision trees and regression models, and then validated using a prospective cohort of 36 patients.

Results When predicting rates of functional independence at 90 days, SPOT showed a sensitivity of 89.36% and a specificity of 89.66% with an area under the receiver operating characteristic curve of 0.952. Performance of SPOT was significantly better than results obtained using National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT score, or patients’ baseline deficits. The negative predictive value for SPOT was >95%, and in patients who were SPOT-negative, we observed higher rates of symptomatic intracerebral hemorrhage after thrombectomy. With mRS scores prediction, the mean absolute error for SPOT was 0.82.

Conclusions SPOT is designed to aid clinical decision of whether to undergo ET in elderly patients. Our data show that SPOT is a useful tool to determine which patients to exclude from ET, and has been implemented in an online calculator for public use.

  • thrombectomy
  • stroke

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  • Contributors Each author listed above should receive authorship credit based on the material contribution to this article, their revision of this article, and their final approval of this article for submission to this journal.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests AS: Penumbra. consulting, honorarium, speaker bureau; Pulsar Vascular, consulting, honorarium, speaker bureau; Microvention, consulting, honorarium, speaker bureau, research; Stryker, consulting, honorarium, speaker bureau.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.