Longitudinal Monitoring and Predictive Modeling of Medication Adherence in Ischemic Stroke Patients

Summary

This protocol describes structured telephone assessment of self-reported medication-taking at 30, 90, and 180 days after discharge and the internal development of an early follow-up prediction model for ischemic stroke survivors. The protocol separates self-reported ingestion from pharmacy-based PDC and uses predefined demographic and day-30 psychosocial predictors.

Abstract

Medication adherence is central to secondary prevention after ischemic stroke, but self-reported adherence may change during follow-up. In this single-center longitudinal framework, 210 participants were enrolled, and 191 completed the 180-day assessment. Medication-taking was assessed using a structured telephone interview for days 1–30, 31–90, and 91–180, and was classified as good when the exposure-day-weighted composite adherence percentage was at least 80%. The Chinese BMQ-Specific and Family APGAR were administered at day 30, day 90, and day 180. A four-predictor logistic model used residence, sex, day-30 BMQ-NCD, and day-30 Family APGAR and was internally validated with 1,000 bootstrap resamples. Among 191 complete cases, good adherence was 84.3% (95% CI, 78.3%–89.1%) at day 30, 77.0% (95% CI, 70.3%–82.7%) at day 90, and 72.8% (95% CI, 65.9%–79.0%) at day 180. Urban residence, male sex, day-30 BMQ-NCD, and day-30 Family APGAR score were associated with day-180 adherence. The apparent AUC was 0.879 (95% CI, 0.821–0.928), and the optimism-corrected AUC was 0.867. The protocol provides a reproducible framework for longitudinal assessment of self-reported adherence and an internally validated day-30 prediction model. External validation and objective adherence measures are required before clinical implementation.

Introduction

Stroke remains a leading cause of long-term disability and mortality worldwide, necessitating a refined clinical definition that accounts for both pathological and imaging evidence¹. Despite advancements in acute interventions, the global burden of stroke continues to escalate, with ischemic stroke accounting for the vast majority of cases². This trend is particularly pronounced in China, where the prevalence of vascular risk factors has reached critical levels, placing an immense strain on the healthcare system³. Evidence suggests that survivors of a first incident stroke face a high risk of recu....

Protocol

The protocol was approved by the Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (Approval No. 2021 Review (1303); approval date, 8 November 2021). All reported procedures were conducted in accordance with the committee's requirements and the approved protocol. The complete analytic dataset is provided in Supplementary File 1.

1. Patient screening and baseline enrollment

1. Identify potential participants from the inpatient records of the Department of Neurology. Select patients diagnosed with acute ischemic stroke via magnetic resonance imaging (MRI)....

Results

Implementation of the standardized protocol and cohort stratification

The participant-flow log reports that 216 individuals were assessed, 6 were excluded, and 210 were enrolled. Seventeen were lost before the day-90 assessment, and two additional participants were lost before day 180, leaving 191 complete cases for the longitudinal and modeling analyses (Supplementary Figure 1). The 19 noncompleters were documented in the screening and follow-up log for parti.......

Discussion

This study describes a standardized longitudinal telephone protocol and internally develops a day-30 prediction model for day-180 self-reported medication adherence after ischemic stroke. Three findings are central. First, the proportion classified as having good adherence decreased from 84.3% at day 30 to 77.0% at day 90 and 72.8% at day 180. Second, BMQ-NCD and Family APGAR scores also decreased across the scheduled assessments. Third, urban residence, sex, day 30 BMQ-NCD, and day 30 Family APGAR were associated with d.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Analysis Script	Study Authors	JoVE_Analysis_Script.py (Uploaded as supplementary material)
Chinese Beliefs about Medicines Questionnaire-Specific (BMQ-Specific)	Original: Prof. Robert Horne. Chinese validation: Jiang S. et al. / Nie B. et al. (as cited in text)	Authorized Chinese Version. Administered via structured interview. Permission obtained from the original developer/copyright holder.
Chinese Family APGAR Index	Original: Dr. Gabriel Smilkstein. Chinese application: Smilkstein G. et al. (as cited in text)	Validated Chinese translation. Version: 5-item scale. Open access/Public domain for clinical research.
Chinese National Institutes of Health Stroke Scale (NIHSS)	Original: National Institutes of Health (NIH). Chinese validation: Lyden P.D. et al. (as cited in text)	Validated Chinese version. 11-item clinical scale (0-42). Open access.
Matplotlib	The Matplotlib development team	Version 3.10.6. Open source (https://matplotlib.org/)
NumPy	The NumPy community	Version 2.3.3. Open source (https://numpy.org/)
Python	Python Software Foundation	Version 3.13.5 (64-bit Windows environment). Open source (https://www.python.org/)
pandas	The pandas development team (NumFOCUS)	Version 2.3.3. Open source (https://pandas.pydata.org/)
SciPy	The SciPy community	Version 1.16.2. Open source (https://scipy.org/)