Predicting 24-Hour Blood Pressure Variability Post Thrombectomy Using Machine Learning for Patients with Ischemic...

Mechanical thrombectomy is the standard of care for patients with ischemic stroke from large vessel occlusion. Blood pressure variability (BPV) in the post thrombectomy period is associated with poor functional outcomes. To determine predictive factors associated with increased BPV, a machine learning algorithm was used to identify factors that are linked with increased BPV indices at 24 hours post thrombectomy.

This retrospective study examined all patients from a Comprehensive Stroke Center's registry who underwent mechanical thrombectomy between January 2016 and December 2019. The primary outcome was BPV between patients who had adequate reperfusion post thrombectomy (Thrombolysis in Cerebral Infarction [TICI] grading 2b+) and those who did not. The secondary outcomes were good functional status at 90 days (modified Rankin Scale ≤2) and reperfusion (TICI 2b+). Random forest analysis was leveraged to determine predictors for BPV with reported root mean square error and normalized root mean square error metrics. Multivariable regression analysis was used to determine factors significantly associated with secondary outcomes. P < 0.05 was the threshold for statistical significance.

A total of 395 patients (49%, n = 195 females and 51%, n = 200 males) were included in the final analysis with mean age (± standard deviation) of 65 (±15) years. TICI 2b+ was achieved in 322 (82%) patients. Median Alberta stroke program early CT score and National Institutes of Health Stroke Scale (NIHSS) were 9 and 18, respectively. Higher age, NIHSS, number of passes, and mechanical ventilation were significantly associated with lower likelihood of modified Rankin Scale ≤2 at 90 days in multivariable regression analysis.

This study identified the interval from last-known-well time-to-groin puncture, age, and NIHSS as factors significantly associated with increased 24-hour BPV in random forest analysis. These predisposing factors in our machine learning analysis allow clinicians to identify patients who are at risk of having increased BPV and opportunities to augment these patients' blood pressure control.