Using machine learning to optimize selection of elderly patients for endovascular thrombectomy

Abstract

BackgroundEndovascular 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.ObjectiveTo use machine learning algorithms to develop a clinical decision support tool that can be used to select elderly patients for ET.MethodsWe 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.ResultsWhen 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.ConclusionsSPOT 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.

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis

“…Various techniques such as robotics, machine learning, and natural language processing have been applied to the study of these cardiovascular diseases. Some cutting edge applications of machine learning models include: predicting the presence of a high-risk plaque or an absence of coronary atherosclerosis, using biomarkers in patients with suspected coronary artery disease [4], selecting suitable elderly patients for endovascular therapy to reduce intracerebral hemorrhage after thrombectomy [5], grading of coronary artery stenosis and extent of myocardial ischemia [6,7,8,9,10], as well as stroke lesion outcome prediction [11,12,13,14,15,16,17,18]. Some authors have explored the potential of image-based AI applications in the scoring of non-contrast computerized tomography scans [19,20] as well as machine learning in the prediction of mortality in coronary artery disease and heart failure patients based on echocardiography [21].…”

DWI-Based Algorithm to Predict Disability in Patients Treated with Thrombectomy for Acute Stroke

“…4,5 Current stroke guidelines are based on data from these randomized controlled trials including highly selected patients. Their encouraging results led to more widespread use of endovascular treatment; therefore, broader criteria than those recommended are now used in current practice to treat, for example, elderly patients, 6 patients with minimal stroke symptoms, 7 or patients in late time windows up to 24 hours after stroke onset. 8 On the other hand, the Highly Effective Reperfusion using Multiple Endovascular Devices (HERMES) meta-analysis of the main trials also highlighted that half of all patients have poor clinical outcomes despite successful technical recanalization.…”

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

“…Using technologies to improve triaging during the acute phase has been more productive in recent years in the cerebrovascular field. ML has been used for recognition and differentiation of ischemic stroke using clinical data [ 42 ] and to predict the 90-day mRS score to aid with thrombectomy [ 59 ]. MRI data has been used for the classification of ischemic stroke onset time [ 60 ] and segmentation and phenotyping of acute ischemic lesions [ 55 ].…”