Determinants of Health-Related Quality of Life in Outpatients with Myocardial Infarction
Bibliographic record
Abstract
Anan S Jarab,1– 3 Razan Z Mansour,3 Suhaib Muflih,3 Walid Al-Qerem,4 Shrouq R Abu Heshmeh,3 Karem H Alzoubi,5,6 Yazid N Al Hamarneh,7 Salah Aburuz,8 Enaam M Al Momany9 1College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates; 2AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates; 3Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan; 4Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan; 5Department of Pharmacy Practice and Pharmacotherapeutics, University of Sharjah, Sharjah, United Arab Emirates; 6Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan; 7Department of Pharmacology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada; 8Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates; 9Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmaceutical Sciences, The Hashemite University, Zarqa, JordanCorrespondence: Karem H Alzoubi, Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan, Email khalzoubi@just.edu.jo; kelzoubi@sharjah.ac.aeBackground: The health-related quality of life (HRQOL) of patients with myocardial infarction (MI) is suboptimal because of the disease’s life-threatening nature, the requirement for long-term lifestyle modifications, and the treatment regimens following MI. This study aimed to evaluate HRQOL and its associated factors in MI patients.Material and Methods: This study was conducted on patients with MI who attended the outpatient cardiology clinic at a major teaching hospital in Jordan. The EQ-5D-3L questionnaire was used to assess HRQOL of the study participants. Quantile regression analysis was conducted to identify the variables associated with HRQOL.Results: The study included 333 patients with a history of MI, with a median age of 58 (57– 60). The median of the total EQ-5D score was 0.65 (0.216– 0.805). Regression results revealed that male patients (Coefficient= 0.110, 95%Cl (0.022– 0.197), P=0.014) and not being diagnosed with diabetes (Coefficient= 0.154, 95%Cl (0.042– 0.266), P=0.007) were associated with increased HRQOL. On the other hand, low income (Coefficient= − 0.115, 95%Cl (− 0.203 - − 0.026), P=0.011), not receiving DPP-4 (Dipeptidyl Peptidase − 4) inhibitors (Coefficient= − 0.321 95%Cl (− 0.462 - − 0.180), P< 0.001), and having low (Coefficient= − 0.271, 95%Cl (− 0.395 - − 0.147), P< 0.001) or moderate (Coefficient= − 0.123, 95%Cl (− 0.202 - − 0.044), P=0.002) medication adherence was associated with decreased HRQOL.Conclusion: The current study demonstrated diminished HRQOL among patients with MI, highlighting the necessity of tailoring interventions to tackle medication adherence barriers in this population. Personalized interventions such as educational programs, counseling, and reminders that consider each patient’s needs and circumstances can greatly enhance medication adherence and, thus, the HRQOL of MI patients. Individuals with lower income levels, female patients, and those with diabetes should be the specific targets of these interventions.Keywords: health-related quality of life, myocardial infarction, EQ-5D, factor, Jordan
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".