Optimising adherence to secondary prevention medications following acute coronary syndrome utilising telehealth cardiology pharmacist clinics: a matched cohort study

Patterns of 12-Month Post-Myocardial Infarction Medication Use According to Revascularisation Strategy: Analysis of 15,339 Admissions in Victoria, Australia

AIM

Clinical guidelines recommend secondary prevention medications following myocardial infarction (MI) regardless of revascularisation strategy. Studies suggest that there is variation in post-MI medication use following percutaneous coronary intervention (PCI) and coronary artery bypass grafts (CABG). We investigated initial dispensing and 12-month patterns of medication use according to revascularisation strategy following non-ST-elevation MI (NSTEMI).

METHOD

We included all public and private hospital admissions for NSTEMI for patients aged ≥30 years in Victoria, Australia, between July 2012 and June 2017. We investigated initial dispensing of P2Y12 inhibitors (P2Y12i), statins (total and high intensity), angiotensin-converting-enzyme inhibitors (ACEi)/angiotensin receptor blockers (ARB), and beta blockers within 60 days after discharge. Twelve-month post-MI medication use was estimated as the proportion of days covered (PDC) over a 12-month period from the date of hospital discharge. Analyses were performed using adjusted regression models, stratified by revascularisation strategy.

RESULTS

There were 15,399 admissions for NSTEMI: 11,754 with PCI and 3,645 with CABG. Following adjustments, predicted probability of initial dispensing in the PCI and CABG groups, respectively, was 0.94 (95% confidence interval 0.93-0.95) vs 0.17 (0.13-0.21) for P2Y12i; 0.69 (0.66-0.71) vs 0.42 (0.37-0.48) for ACEi/ARB; 0.59 (0.57-0.62) vs 0.69 (0.64-0.74) for beta blockers; 0.89 (0.87-0.91) vs 0.89 (0.85-0.92) for statins; and 0.60 (0.57-0.62) vs 0.69 (0.63-0.73) for high intensity statins. The 12-month PDC in the PCI and CABG groups, respectively, was 0.82 (0.80-0.83) vs 0.12 (0.09-0.15) for P2Y12i; 0.62 (0.60-0.65) vs 0.43 (0.39-0.48) for ACEi/ARB; 0.53 (0.51-0.55) vs 0.632 (0.58-0.66) for beta blockers; 0.79 (0.78-0.81) vs 0.78 (0.74-0.81) for statins; and 0.49 (0.47-0.51) vs 0.55 (0.50-0.59) for high intensity statins.

CONCLUSIONS

Post-discharge dispensing of secondary prevention medications differed with respect to revascularisation strategy from 2012 to 2017, despite clear evidence of benefit during this period. Interventions may be needed to address possible clinician and patient uncertainty about the benefits of secondary prevention medications, regardless of revascularisation strategy.

The interventions to improve medication adherence in coronary heart disease patient: A systematic review

Objective

The clinical outcome and quality of life of CHD patients are greatly influenced by medication adherence. Non-adherence of CHD patients to treatment results in sub-optimal clinical outcomes and increasing costs. This study aims to describe effectiveness of the intervention to improve the medication adherence in CHD patients.

Methods

Systematic review methodology was used in this study. Scopus and PubMed were used to search the relevant article systematically. The outcome measured was medication adherence in coronary heart disease patients.

Results

Final screening was 31 articles that met the inclusion criteria in this study of 788 articles. Selection processes the article used the PRISMA guideline. Most of the articles (15 articles) use interventions that utilize information technology (IT) as known with m-health in the form of text messages, website, and smartphone-based applications in increasing medication adherence in CHD patients. The non m-health interventions developed are in the form of self-efficacy programs, monitoring and education by health workers or care workers, pharmacy care by clinical pharmacists, and the use of drugs in the form of multi-capsules. The results of most intervention with m-health can improve the medication adherence in CHD patient effectively. Education and motivation program by professional health care and multi-capsules also increasing the medication adherence in the intervention control. There was a decrease of medication adherence in some articles with long time follow-up that can be attention for the professional health care to manage the patient adherent.

Conclusion

The medication adherence in CHD patient can be improve by various program. Modification of m-health and non m-health intervention can be resolved to increase the communication, motivation, and knowledge about medication adherence in CHD patients.

Development and validation of a Medication Adherence Universal Questionnaire: the MAUQ

BACKGROUND

Different questionnaires assess self-reported medication adherence and others quantify aspects of patients attitudes towards medication, but not together in a single instrument. Gathering these two aspects in a single instrument could reduce patients survey burden.

AIM

The aim of this study was to develop the Medication Adherence Universal Questionnaire (MAUQ) using the Maastricht Utrecht Adherence in Hypertension short version (MUAH-16) factorial structure as the hypothesized model.

METHOD

A multistep process started with the modification of the MUAH-16 to obtain the MAUQ. Patients using at least one antihypertensive medicine were recruited. The two questionnaires, the MUAH-16 and MAUQ, were applied. A confirmatory factor analysis (CFA) was performed using the initial MUAH-16 s-order 4-factor model. An additional bifactor model with four uncorrelated factors and an overall score was tested. The comparative fit index (CFI), root mean square error of approximation (RMSEA) with confidence intervals (CIs), and standardized root mean squared residual (SRMR) were used to assess both models.

RESULTS

A sample of 300 hypertensive patients completed the instruments. The CFA with the second-order 4-factor solution resulted in similar results for the MUAH-16 and MAUQ: CFIs of 0.934 and 0.930, RMSEAs of 0.043 [CI 0.030-0.056] and 0.045 [CI 0.031-0.057] and SRMRs of 0.060 and 0.061, respectively. The CFA with the bifactor model showed slightly better results for both the MUAH-16 and MAUQ: CFIs of 0.974 and 0.976, RMSEAs of 0.030 [CI 0.005-0.046] and 0.028 [CI 0.001-0.044], and SRMRs of 0.043 and 0.044, respectively.

CONCLUSION

CFA demonstrated that the MAUQ presented a better fit to both models than the MUAH-16, obtaining a robust universal free instrument to assess medicine-taking behaviour and four medicine beliefs components.