Subgroup Differences and Determinants of Patient-Reported Mental and Physical Health in Patients With Ischemic Heart Disease
Bibliographic record
Abstract
BACKGROUND: A growing population is living with ischemic heart disease (IHD). Patient-reported outcomes (PROs) are reliable prognostic tools. Studies exploring PROs are needed to identify vulnerable patients and guide targeted healthcare strategies. OBJECTIVES: The aims of this study were to (i) describe PROs at hospital discharge across 3 diagnostic subgroups: (1) chronic IHD/stable angina, (2) non-ST-elevation myocardial infarction (non-STEMI)/unstable angina, and (3) ST-elevation myocardial infarction (STEMI), and (ii) examine determinants for PROs at hospital discharge in patients with IHD. METHODS: This study included a national cohort with register-data linkage including 14 115 adults with IHD discharged from Danish heart centers. Eligible patients (n = 13 476) were invited to complete a questionnaire, and 7 167 (53%) responded. Questionnaires included the Medical Outcome Study Short-Form 12, the Hospital Anxiety and Depression Scale, EuroQoL, HeartQoL, the Edmonton Symptom Assessment Scale, and ancillary questions. Sociodemographic and clinical characteristics were obtained from national registers. Student t test, χ test, and adjusted linear and logistic regression analyses were conducted to investigate subgroup differences, and adjusted linear and logistic regression analyses were conducted to explore determinants for PROs. RESULTS: Statistically significant subgroup differences were found, with groups reporting worst to best scores for most of PROs being as follows: chronic IHD/stable angina, non-STEMI/unstable angina, and STEMI. Symptoms of anxiety were highly prevalent in the non-STEMI/unstable angina group, with 33.8% exceeding a Hospital Anxiety and Depression Scale-Anxiety cutoff score indicating a possible anxiety disorder. Determinants for worse PROs included female sex, lower educational level, obesity, and poor physical fitness. CONCLUSIONS: Significant differences in PROs across IHD subgroups were observed and determinants for poor outcomes suggested. Results may guide differentiated care initiatives and resource allocation for preventative strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".