Patient-reported health and 1-year mortality in patients with ischemic heart disease – findings from the Denheart study
Notice bibliographique
Résumé
Abstract Background Though survival has improved markedly in ischemic heart disease (IHD), it remains a leading cause of death worldwide. Screening tools to identify patients at risk are ever in demand. Large-scale studies exploring the association between patients' self-reported mental and physical health and mortality are lacking. Purpose (i) to describe patient-reported outcomes (PROs) at discharge in IHD patients deceased and alive at one year, (ii) to investigate the discriminant predictive performance of PRO instruments on mortality, (iii) to investigate differences in time to death among survey responders/non-responders and among three diagnostic sub-groups (chronic ischemic heart disease/stable angina, non-STEMI/unstable angina and STEMI), and (iv) to investigate predictors of one-year mortality among sociodemographic, clinical and self-reported factors. Methods Data from the national DenHeart survey with register-data linkage was used. A total of 14,115 adults with IHD were discharged during one year. Eligible (n=13,476) were invited to complete a questionnaire and 7,167 (53%) responded. Questionnaires included the Health survey short form 12-items (SF-12), Hospital Anxiety and Depression Scale (HADS), EuroQoL-5-dimensions (EQ-5D), HeartQoL, Edmonton Symptom Assessment Scale (ESAS) and ancillary questions. Clinical and demographic characteristics were obtained from registries as were data on one-year mortality. Comparative analyses investigated differences in PROs, and discriminant PRO-performance was explored by Receiver Operating Characteristics (ROC) curves. Kaplan-Meier survival analysis explored differences in time to death across sub-groups. Predictors of mortality were explored using multifactorially adjusted cox regression analyses with time to death as underlying timescale. Results Highly significant and clinically important differences in PROs were found between those alive and those deceased at one year. The best discriminant performance was observed for the physical component scale of the SF-12 (Area Under the Curve (AUC) 0.706) (Figure 1). One-year mortality among responders and non-responders was 2% and 7%, respectively. Significant differences in time to death was observed between responders and non-responders (p<0.001) and among diagnostic subgroups (p<0.001). Strongest predictors of one-year mortality included STEMI (hazard ratio (HR) 2.9 95% confidence interval (CI) 2.3–3.7), Tu comorbidity index score 3+ (HR 3.6, 95% CI 2.7–4.8) and patient-reported feeling unsafe about returning home from hospital (HR 2.07, 95% CI 1.2–3.61). Conclusions One-year post-discharge mortality was expectedly low, however notably higher in certain subgroups. Though clinical predictors may be difficult to modify, factors such as feeling unsafe about returning home should be addressed at discharge. PRO-performance estimates may guide clinicians and researchers in choosing appropriate predictive patient-reported outcome tools. Figure 1. PRO instruments ROC curves Funding Acknowledgement Type of funding source: None
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».