Patient-reported outcomes are independent predictors of one-year mortality and cardiac events across cardiac diagnoses: Findings from the national DenHeart survey
Bibliographic record
Abstract
AIMS: Patient-reported quality of life and anxiety/depression scores provide important prognostic information independently of traditional clinical data. The aims of this study were to describe: (a) mortality and cardiac events one year after hospital discharge across cardiac diagnoses; (b) patient-reported outcomes at hospital discharge as a predictor of mortality and cardiac events. DESIGN: A cross-sectional survey with register follow-up. METHODS: Participants: All patients discharged from April 2013 to April 2014 from five national heart centres in Denmark. MAIN OUTCOMES: Patient-reported outcomes: anxiety and depression (Hospital Anxiety and Depression Scale); perceived health (Short Form-12); quality of life (HeartQoL and EQ-5D); symptom burden (Edmonton Symptom Assessment Scale). Register data: mortality and cardiac events within one year following discharge. RESULTS: There were 471 deaths among the 16,689 respondents in the first year after discharge. Across diagnostic groups, patients reporting symptoms of anxiety had a two-fold greater mortality risk when adjusted for age, sex, marital status, educational level, comorbidity, smoking, body mass index and alcohol intake (hazard ratio (HR) 1.92, 95% confidence interval (CI) 1.52-2.42). Similar increased mortality risks were found for patients reporting symptoms of depression (HR 2.29, 95% CI 1.81-2.90), poor quality of life (HR 0.46, 95% CI 0.39-0.54) and severe symptom distress (HR 2.47, 95% CI 1.92-3.19). Cardiac events were predicted by poor quality of life (HR 0.71, 95% CI 0.65-0.77) and severe symptom distress (HR 1.58, 95% CI 1.35-1.85). CONCLUSIONS: Patient-reported mental and physical health outcomes are independent predictors of one-year mortality and cardiac events across cardiac diagnoses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".