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

Podsumowanie

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.

Streszczenie

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.

Wprowadzenie

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 recurrent vascular events, with community-based studies highlighting the persistent vulnerability of this population⁴. While managing the acute phase presents significant challenges⁵, the long-term survival and life expectancy for these patients are predominantly dictated by the efficacy of secondary prevention strategies^6,7.

The cornerstone of secondary prevention involves rigorous pharmacological management, including blood pressure lowering and antiplatelet therapy^8,9,10. International guidelines from the American Heart Association (AHA), the European Stroke Organisation (ESO), and the Canadian Stroke Best Practice Recommendations emphasize that adherence to these regimens is essential to prevent recurrence^11,12,13,14. Nevertheless, the clinical utility of these interventions is frequently undermined by poor medication adherence^15,16. Medication adherence is a multi-dimensional construct that includes both initiation and persistence^15,17. Recent registry data indicate that greater adherence to secondary prevention medications significantly improves survival and reduces recurrence rates^17,18. For example, statin adherence has been independently associated with reduced recurrent risk¹⁹.

Despite these benefits, longitudinal studies observe a significant “temporal decay” in adherence rates during the first-year post-discharge²⁰. While demographic factors such as sex differences and socioeconomic status influence persistence^21,22, there is an increasing recognition of the role of psychosocial and cognitive factors in shaping patient behavior^23,24. The Beliefs about Medicines Questionnaire (BMQ) has emerged as a validated tool to assess the cognitive representation of medication through the “Necessity-Concerns” framework²⁵. Systematic reviews have demonstrated that BMQ scores are robust predictors of adherence across various chronic conditions in China²⁶. Similarly, the social environment, particularly family functioning, plays a pivotal role. The Family APGAR Index provides a reliable measure of perceived support, which is critical for patients navigating the complexities of post-stroke life^27,28.

While the NIHSS and modified Rankin Scale describe neurological severity and functional outcome^29,30, they do not directly measure medication beliefs or perceived family functioning³¹. Mobile health and WeChat-based services have been studied as adherence supports^32,33. The present protocol, therefore, has two aims: to define medication-taking assessment at day 30, day 90, and day 180 using consistent terminology, and to develop an interpretable day-180 adherence model using predefined demographic and psychosocial variables collected during the study.

Protokół

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) or computed tomography (CT). Record the total number of initially screened individuals to establish the baseline pool for the participant flow diagram.
2. Screen patients against the prespecified inclusion criteria. Include adults with imaging-confirmed acute ischemic stroke who can complete the interview themselves or through a legally authorized representative.
3. Apply exclusion criteria. Exclude patients with a history of severe mental illness, cognitive impairment (determined by clinical records), or a life expectancy of less than six months. Document the exact reasons and corresponding numbers for any exclusions at this stage (e.g., life expectancy limitations or refusal to provide consent).
4. Before enrollment, obtain written informed consent from the patient or, when the patient cannot provide consent, from a legally authorized representative, using the committee-approved informed consent form. Explain the longitudinal study design, telephone assessments, and 180-day follow-up schedule before signature. Record the consenting person's identity and role, the consent date, and the study staff member who obtained consent.
5. Collect baseline demographic and clinical data within 48 h of admission. Record age, sex, residence, educational level, and monthly income using a standardized case report form.
6. At discharge, have a trained stroke clinician administer the validated Chinese NIHSS using its standard 11 scored items and total range of 0–42²⁹. Record the total score, assessor role, and training status, assessment date, and length of hospital stay.
7. Administer the authorized Chinese BMQ-Specific by a structured face-to-face interview before discharge and by telephone at day 30, day 90 and day 180^25,34.
   NOTE: The instrument contains five Necessity items and five Concerns items. Score each item from 1 (strongly disagree) to 5 (strongly agree), and sum the five items in each subscale; Necessity and Concerns totals, therefore each range from 5 to 25.
8. Calculate the necessity-concerns differential (BMQ-NCD) as Necessity minus Concerns (range, -20 to 20). Higher necessity and higher BMQ-NCD values indicate stronger perceived need relative to concerns, whereas a higher Concerns value indicates greater apprehension.
   NOTE: Require all five responses for each subscale; otherwise, code that subscale and the BMQ-NCD as missing. Use day 30 BMQ-NCD as the continuous model predictor. At the same time, administer the Chinese Family APGAR²⁷.
9. Score Adaptation, Partnership, Growth, Affection, and Resolve as 0 (hardly ever), 1 (some of the time), or 2 (almost always), and sum the five items to an integer total of 0-10; higher scores indicate better perceived family functioning. If any Family APGAR item is missing, code the total as missing. Record the instrument versions, authorization, administrator, respondent, date, item responses, subscale totals, and total scores on the case-report form.

2. Discharge preparation and patient education

1. Conduct a standardized medication counseling session before discharge. Provide a written list of prescribed secondary prevention medications, including antiplatelets, statins, and antihypertensives.
2. Instruct the patient and their primary caregiver on the importance of medication adherence. Demonstrate how to record medication intake if the patient intends to use a pillbox or diary.
3. Verify the patient’s primary telephone contact number. Establish a preferred time window for future follow-up calls to minimize attrition and ensure a high response rate.

3. Post-discharge longitudinal follow-up (Day 30, Day 90, and Day 180)

1. Conduct the first structured telephone interview at day 30 after discharge (target window, plus or minus 3 days). For every secondary-prevention medication class prescribed during days 1–30, record the class name, prescribed daily dose frequency, number of days exposed, and the self-reported number of class-adherent days during days 1–30.
   1. Record the respondent (patient or primary medication-managing caregiver), contact date, regimen changes, temporary physician-directed interruptions, and reasons for noncompletion. At the same interview, administer the BMQ-Specific and Family APGAR and record the day-30 scores
2. Conduct the second interview at day 90 (target window, plus or minus 7 days). Repeat the class-specific medication questions for the nonoverlapping days 31–90 interval and administer the BMQ and Family APGAR. Do not ask the participant to reconstruct days 1–30 again; retain the day-30 record as the source for that interval.
3. Conduct the final interview at day 180 (target window, plus or minus 7 days). Repeat the class-specific medication questions for the nonoverlapping days 91–180 interval, administer the BMQ and Family APGAR, and record the day-180 mRS when available.
4. Use the same structured interview form and respondent rule at all three follow-ups. When a patient cannot respond because of cognitive, language, or physical impairment, use the caregiver who manages the medication regimen and record that substitution. Do not claim pill-count, diary, pharmacy, or interviewer-blinding verification unless it is documented in the source records.
5. Record outpatient visits for descriptive follow-up only. Do not include any post-discharge utilization variable in a prediction model intended for use after the day-30 assessment.
6. Define baseline as the inpatient/discharge assessment and label adherence assessments by calendar time (day 30, day 90, and day 180), rather than T1, T2, and T3. This prevents the previous conflict in which T1 referred both to baseline and to day 30.

4. Quantification of self-reported medication adherence

1. Do not use the term PDC unless adherence is derived from dispensing or refill records. Treat antithrombotic therapy (antiplatelet or oral anticoagulant, as indicated), lipid-lowering therapy, antihypertensive therapy, and glucose-lowering therapy as separate secondary-prevention classes only when prescribed to that participant.
   1. For each prescribed class, define a class-adherent day as a day on which every scheduled dose in that class was reportedly taken; omission of any scheduled dose makes that class-day nonadherent. Physician-directed discontinuations, substitutions, and temporary holds are not counted as expected exposure days after the documented effective date.
   2. Calculate the interval composite self-reported adherence percentage as 100 multiplied by the sum of class-adherent days across prescribed classes divided by the sum of expected class-exposure days across those classes. This exposure-day-weighted calculation accommodates different prescription durations and is equivalent to the mean of class-specific proportions only when every class is prescribed for the same number of days. Use days 1–30 for the day-30 value, days 31–90 for the day-90 value, and days 91–180 for the day-180 value.
2. At each follow-up, classify good adherence as a self-reported proportion of adherent days of at least 80% and poor adherence as less than 80%. Treat missing adherence assessments as missing; do not assign them to the poor-adherence category.

5. Data integration, longitudinal analysis, and predictive modeling

1. Perform the reproducible analysis using the Python programming language (version 3.13.5) with libraries for data manipulation, numerical computation, statistical analysis, and visualization on a 64-bit Windows operating system. Set the random seed to 71636, and code missing values as NA. If the final team instead uses R, replace this section with the exact executed R version, package versions, functions, and script; do not report an unexecuted software workflow.
2. Summarize good-adherence proportions at day 30, day 90, and day 180 with two-sided 95% Clopper-Pearson exact confidence intervals. Test the overall paired change in binary adherence with Cochran's Q test, followed by exact paired McNemar tests with Bonferroni correction.
3. Analyze BMQ-NCD and Family APGAR across day 30, day 90, and day 180 among complete cases using the Friedman test. When the omnibus test is significant, conduct paired Wilcoxon signed-rank comparisons with Bonferroni correction. Report medians and interquartile ranges in addition to means and standard deviations. Calculate individual scale totals from item-level responses before analysis; do not analyze interpolated or manually smoothed questionnaire values.
4. Define the day-180 outcome as good adherence = 1 and poor adherence = 0. Refit a day-30 multivariable logistic regression using four prespecified predictors: residence (Urban = 1; Rural = 0), sex (Male = 1; Female = 0), day-30 BMQ-NCD (continuous), and day-30 Family APGAR score (continuous). Do not use outpatient follow-up visits. Avoid describing univariate screening or backward selection unless that procedure was actually executed and documented in code.
5. Specify the complete fitted equation: logit[P(good adherence at day 180)] = -10.3811 + 1.2921(Urban) - 1.5215(Male) + 0.5211(BMQ-NCD) + 0.6887(Family APGAR). Generate the nomogram directly from these unrounded coefficients and use reader-facing labels, reference categories, score ranges, and directionality.
6. Assess discrimination with the ROC AUC and a percentile-bootstrap 95% confidence interval. Assess overall prediction error with the Brier score and calibration with a calibration plot and calibration slope. Report apparent performance separately from optimism-corrected performance.
7. Perform 1,000 successful bootstrap resamples. In each resample, refit the complete four-predictor model and estimate optimism by comparing performance in the bootstrap sample with performance in the original sample. Subtract mean optimism from apparent performance.

6. Patient confidentiality and data safety

1. Ensure all patient-identifiable information is removed before analysis. Store the master linkage file in a password-protected, encrypted institutional system accessible only to authorized study personnel.
2. Assign a unique alphanumeric ID to each participant at enrollment. Use this ID for all subsequent follow-up forms and digital records.

Wyniki

Wdrożenie znormalizowanego protokołu i stratyfikacja kohorty

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Dyskusja

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...

Materiały

Lista materiałów użytych w tym artykule

Nazwa	Firma	Numer katalogowy	Komentarze
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/)