Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using
Vahid Farmani1, Helge Kniep2, Mate E Maros3
1Galway Medical Technology Centre, Department of Mechanical and Industrial Engineering, Atlantic Technological University, Galway, Ireland.
Objectives:
Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individual. In this study, we aimed to use machine learning to identify characteristics influencing the likelihood of not benefiting from successful recanalization.
Materials & Methods:
A total of 1718 non-reperfused patients (Thrombolysis in Cerebral Infarction [TICI] ≤ 2a) and 10339 reperfused patients (TICI ≥ 2b) were included in the study as nonreperfusion and reperfusion groups, respectively. The primary target variable was probability of poor functional outcome after three months, defined by the modified Rankin Scale score of 3 to 6. Two random forest (RF) models trained on pre-treatment covariates of nonreperfusion and reperfusion groups, were used to predict the probability of poor outcome under unsuccessful and successful recanalization scenarios, respectively. The individual effect of successful recanalization was defined as the difference in predicted probabilities returned by the two models.
Results:
Strong calibration was achieved by the RF models trained on nonreperfusion group (intercept:0.027, slope: 1.030) and reperfused group (intercept:0.010, slope: 1.017). The average risk reduction under successful recanalization scenario was 22.0 % (95 % CI [21.7 % - 22.3 %]) for the reperfused group and 19.8 % (95 % CI [19.1 % - 20.5 %]) for the nonreperfusion group. Key factors associated with not benefiting from successful recanalization included older age, higher pre-stroke mRS scores and higher National Institutes of Health Stroke Scale score at admission.
Conclusions:
This study highlights the potential of predictive ML techniques to estimate the individual effect of successful recanalization on ischemic stroke patients undergoing MT.
