Determinants of Health-Related Quality of Life in Outpatients with Myocardial Infarction
Bibliographic record
Abstract
Background: 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".