Understanding the Burden of Myocardial Infarction and Patient Preferences for Treatment: A Real-World Study Assessing Patients’ Perspectives via an Online Survey
Bibliographic record
Abstract
Purpose: Myocardial infarction (MI) remains to be associated with a high risk of recurrent cardiovascular events and disease burden. This study assessed patient perspectives on the burden of disease and treatment in the first year post-MI. Methods: Data were collected via a self-administered online questionnaire posted on the Carenity patient platform in the United States (10/30/2022-12/30/2022). Only patients who had an MI in the prior year were eligible for inclusion. Results: A total of 151 patients completed the survey. The majority were men (69%), median age was 50 years, and 38% had an MI within the previous 90 days. Overall, post-MI complications were reported in 44% of the patients, including depression (23%), recurrent MI (7%), and stroke (7%). Follow-up care was provided by general/clinical cardiologists (67%), interventional cardiologists (38%), and general healthcare providers (59%). Most patients (80%) reported involvement in treatment decisions. The number of prescribed medications was considered the main contributor to post-MI treatment burden; approximately 42% of the patients found it tedious remembering to take their medications. The most commonly quoted post-MI treatment goal was recurrent MI risk reduction. Additionally, 73% of the patients considered improving quality of life to be a key goal. Overall, the patients' emotional well-being, physical well-being, and personal life were particularly impacted by MI. "Stress/anxiety/fear" was the most frequently reported emotion immediately post-MI, and one-third conveyed MI's negative impact on their employment status. MI impacted household finances in 74% of patients, with 38% losing income. Conclusions: MI places a substantial burden on patients. Understanding patient experiences post-MI may enhance patient-centered care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".