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Causal and Individual treatment effect in Stroke: A Retrospective Cohort Study
ABSTRACT
Introduction: Stroke is a major cause of death and disability. This study aims to determine the causal effects and individual treatment effects (ITEs) using real-world data. Method: This study included high-risk patients treated at Ramathibodi Hospital, Thailand between 2010-2020. Hospital records were used to identify risk factors including hypertension (HT), diabetes (DM), dyslipidemia (DLP), and atrial fibrillation (AF). Ischemic/ hemorrhagic stroke was the main outcome. Machine learning and conventional methods were used to estimate causal effect, while weighted split-conformal quantile regression conformal inference (CI) was used to calculate ITEs.
Results:
AF, HT, and DM were significant stroke risk factors with average causal (risk) effect ranging from 0.075-0.097, 0.017-0.025, and 0.006-0.01. Estimated causal (risk) ratios from Dragonnet associated with these corresponding factors were 4.56 (4.56,4.57), 2.44 (2.41,2.46), and 1.41 (1.21,1.60) respectively. Mean ITEs indicated that there were several patients with DM or DM with HT who were not currently receiving antiplatelet treatment and would be more likely to benefit if they had received it. Conclusion: This study provides causal estimates of AF, HT, and DM on stroke. This study improves our understanding of stroke risk and highlights the need for further research to inform treatment options for high-risk patients.
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