Pharmacist-Led Follow-Up Program for Rural Acute Coronary Syndrome Patients: The PLURAL-ACS Study
Bibliographic record
Abstract
Background: Rural patients have been shown to have reduced access to care, delayed discharge prescription fills, and frequent readmissions following acute coronary syndrome (ACS) compared to urban patients. While virtual and pharmacist-led programs have shown benefit in providing efficient care to cardiac patients, to our knowledge, their implementation in rural ACS-population have not been assessed. The purpose of this two-phase study was to implement a first-ever pharmacist-led virtual follow-up program for rural Canadian ACS patients and to determine the impact of the program as compared to a matched control group. Methods: Consecutive rural ACS-patients discharged from the Mazankowski Alberta Heart Institute between March-May 2022 were included in the pharmacist-led follow-up pilot program. Structured telephone interviews were used to identify and resolve cardiac medication-related issues for each patient on day 1, 10, and 30 post discharge. Descriptive outcomes of the program were collected, which included the total number and type of cardiac medication-related issues identified and resolved by the program and change in cardiac medication knowledge using questionnaires adapted from prior studies. Program-patients were then compared to a control group, which included ACS patients with usual care (discharged November 2021-July 2022), matched for sex, zone of residence, and age within 10 years. Outcomes were collected from administrative databases and multivariable regression analyses were conducted for comparisons. In the retrospective analysis, the primary outcome was time to prescription fill of discharge ACS-medications within 30 days of discharge. Secondary outcomes included 30-day cardiac-related hospital readmissions, cardiac-related emergency department visits, and primary care practitioner (PCP)-visits. Results: 40 patients entered the 15-week pilot-program and a total of 139 virtual visits were completed. Median time spent per visit was 60 (interquartile range [IQR], 50-80) minutes. A total of 255 cardiac medication-related issues (mean 6 per patient; IQR, 3.75-8.25) were identified, and 91% were resolved by the pharmacist. Discharge prescription errors, real adverse events, and therapy optimization were most common on day 1, 10, and 30 respectively. Cardiac medication knowledge was significantly increased in patients post program compared to their knowledge prior to program implementation (median score difference of 2.5 of 7; IQR, 2-4). When comparing the pilot program participants to matched control group (n=80), there was no significant differences in time to prescription fill (0.25 [IQR, 0.0-0.25] days vs 0 [IQR, 0.0-1.0] days; adjusted hazard ratio [aHR], 1.17; 95% confidence interval [CI], 0.80-1.74), cardiac-related hospital readmissions (8% vs 5%; aHR, 1.69; 95% CI, 0.36-7.96), or cardiac-related emergency department visits (10% vs 8%; HR 1.33; 95% CI, 0.38-4.73). PCP-visit was higher in the program patients (90% vs 73%; aHR, 2.99; 95% CI, 1.47-6.10). Conclusion: Our study highlights that a high number of cardiac medication-related issues are encountered by ACS patients early post hospital discharge. A pharmacist-run post ACS follow-up program identified and resolved majority of medication issues, as well as enhancing patient safety and overall follow-up of care as outpatient. Longer duration studies, with adequate power, are required to confirm these findings and assess the impact of such a program on clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".