JMIR Publications

ABSTRACT

Background:

Stroke is a major cause of death and disability worldwide. Identifying individuals who would benefit most from preventative interventions, such as antiplatelet therapy, is critical for personalized stroke prevention. However, traditional methods for estimating treatment effects often focus on the average effect across a population and do not account for individual variations in risk and treatment response.

Objective:

This study aimed to introduce the estimation of individualized treatment effects (ITEs) method for stroke prevention using a novel combination of Dragonnet, a causal neural network, and conformal inference. The study also aimed to determine and validate the causal effects of known stroke risk factors - hypertension (HT), diabetes (DM), dyslipidemia (DLP), and atrial fibrillation (AF) - using both conventional causal model and machine learning models.

Methods:

A retrospective cohort study was conducted using data from 275,247 high-risk patients treated at Ramathibodi Hospital, Thailand, between 2010 and 2020. Patients aged >18 years with HT, DM, DLP, or AF were eligible. The main outcome was ischemic or hemorrhagic stroke, identified using ICD-10 codes. Causal effects of the risk factors were estimated using a range of methods, including: 1. Propensity score-based methods: stratified propensity scores (SPS), inverse probability weighting (IPW), and doubly robust estimation (DRE). 2. Structural causal models (SCM). 3. Double machine learning (DML) 4. Dragonnet, a causal neural network, altogether with weighted split-conformal quantile regression (CQR) was used to estimate ITEs.

Results:

AF, HT, and DM were identified as significant stroke risk factors. Average causal risk effect estimates for these risk factors ranged from 0.075 to 0.097 for AF, 0.017 to 0.025 for HT, and 0.006 to 0.01 for DM, depending on the method used. Dragonnet yielded causal risk ratios of 4.56 for AF, 2.44 for HT, and 1.41 for DM, which is comparable to other causal models and the standard epidemiological case-control study. Mean ITEs analysis indicated that several patients with DM or DM with HT, who were not receiving antiplatelet treatment at the time of data collection, could potentially benefit from this therapy. These patients showed reductions in total risk of -0.015 and -0.016, respectively.

Conclusions:

This study provides a comprehensive evaluation of stroke risk factors and demonstrates the feasibility of using Dragonnet and conformal inference to estimate ITEs of antiplatelet therapy for stroke prevention. The findings highlight the potential of these advanced techniques to inform personalized treatment strategies for stroke, enabling clinicians to identify individuals who are most likely to benefit from specific interventions.