Health-related quality of life 1–3 years post-myocardial infarction: its impact on prognosis
Bibliographic record
Abstract
OBJECTIVE: To assess associations of health-related quality of life (HRQoL) with patient profile, resource use, cardiovascular (CV) events and mortality in stable patients post-myocardial infarction (MI). METHODS: The global, prospective, observational TIGRIS Study enrolled 9126 patients 1-3 years post-MI. HRQoL was assessed at enrolment and 6-month intervals using the patient-reported EuroQol-5 dimension (EQ-5D) questionnaire, with scores anchored at 0 (worst possible) and 1 (perfect health). Resource use, CV events and mortality were recorded during 2-years' follow-up. Regression models estimated the associations of index score at enrolment with patient characteristics, resource use, CV events and mortality over 2-years' follow-up. RESULTS: Among 8978 patients who completed the EQ-5D questionnaire, 52% reported 'some' or 'severe' problems on one or more health dimensions. Factors associated with a lower index score were: female sex, older age, obesity, smoking, higher heart rate, less formal education, presence of comorbidity (eg, angina, stroke), emergency room visit in the previous 6 months and non-ST-elevation MI as the index event. Compared with an index score of 1 at enrolment, a lower index score was associated with higher risk of all-cause death, with an adjusted rate ratio of 3.09 (95% CI 2.20 to 4.31), and of a CV event, with a rate ratio of 2.31 (95% CI 1.76 to 3.03). Patients with lower index score at enrolment had almost two times as many hospitalisations over 2-years' follow-up. CONCLUSIONS: Clinicians managing patients post-acute coronary syndrome should recognise that a poorer HRQoL is clearly linked to risk of hospitalisations, major CV events and death. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT01866904) (https://clinicaltrials.gov).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".