Prediction of blood pressure variability during thrombectomy using supervised machine learning and outcomes of patients with ischemic stroke from large vessel occlusion

• Published in: Journal of thrombosis and thrombolysis Vol. 56; no. 1; pp. 12 - 26
• Main Authors: Najafali, Daniel, Johnstone, Thomas, Pergakis, Melissa, Buganu, Adelina, Ullah, Muhammad, Vuong, Kim, Panchal, Bhakti, Sutherland, Mark, Yarbrough, Karen L., Phipps, Michael S., Jindal, Gaurav, Tran, Quincy K.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-07-2023; Springer Nature B.V
• Subjects: Age; Algorithms; Blood pressure; Cardiology; Cerebral blood flow; Cerebral infarction; Hematology; Ischemia; Learning algorithms; Machine learning; Mechanical ventilation; Medicine; Medicine & Public Health; Risk factors; Stroke; Thrombolysis; Ventilation; Reperfusion; Mechanical thrombectomy; Supervised learning; Machine learning; Blood pressure variability; Random forest; Ischemic stroke; Algorithm; Large vessel occlusion
• ISSN: 1573-742X; 0929-5305; 1573-742X
• DOI: 10.1007/s11239-023-02796-9

Mechanical thrombectomy (MT) is the standard of care for patients with acute ischemic stroke from large vessel occlusion (AIS-LVO). The association of blood pressure variability (BPV) during MT and outcomes are unknown. We leveraged a supervised machine learning algorithm to predict patient characteristics that are associated with BPV indices. We performed a retrospective review of our comprehensive stroke center’s registry of all adult patients undergoing MT between 01/01/2016 and 12/31/2019. The primary outcome was poor functional independence, defined as 90-day modified Rankin Scale (mRS) ≥ 3. We used probit analysis and multivariate logistic regressions to evaluate the association of patients’ clinical factors and outcomes. We applied a machine learning algorithm (random forest, RF) to determine predictive factors for the different BPV indices during MT. Evaluation was performed with root-mean-square error (RMSE) and normalized-RMSE (nRMSE) metrics. We analyzed 375 patients with mean age (± standard deviation [SD]) of 65 (15) years. There were 234 (62%) patients with mRS ≥ 3. Univariate probit analysis demonstrated that BPV during MT was associated with poor functional independence. Multivariable logistic regression showed that age, admission National Institutes of Health Stroke Scale (NIHSS), mechanical ventilation, and thrombolysis in cerebral infarction (TICI) score (OR 0.42, 95% CI 0.17–0.98,
P
 = 0.044) were significantly associated with outcome. RF analysis identified that the interval from last-known-well time-to-groin puncture, age, and mechanical ventilation were among important factors significantly associated with BPV. BPV during MT was associated with functional outcome in univariate probit analysis but not in multivariable regression analysis, however, NIHSS and TICI score were. RF algorithm identified risk factors influencing patients’ BPV during MT. While awaiting further studies’ results, clinicians should still monitor and avoid high BPV during thrombectomy while triaging AIS-LVO candidates quickly to MT.
Graphical abstract