Health Resource Implications of Heart Failure Hospitalizations in Younger Patients Compared With Older Patients
Notice bibliographique
Résumé
Although heart failure (HF) is less common in individuals <50, recent studies have demonstrated a substantial increase in the frequency of young HF over the past 2 decades. 1,2 This has been attributed to the increasing prevalence of obesity, type 2 diabetes mellitus, and hypertension, and better treatments for congenital heart disease, coronary artery disease, dyslipidemia, or hypertension. 2 Recent studies reported decreasing mortality rates in older patients with HF over the past 2 decades, but no appreciable changes in the standardized mortality rate for HF patients <50 years since the turn of the millennium. 1,2However, little is known about hospital resource use by younger versus older patients with HF, and this has major implications for future resource planning.In this retrospective cohort study we examined outcomes for all patients >20 years hospitalized with a primary diagnosis of heart failure in Canada between April 2004 and December 2013.Details on the databases used, International Classification of Diseases, 10th Revision case definitions for HF and all comorbidities, and analytic methods have been published already. 3 This study was approved by the University of Alberta Health Research Ethics Board with waiver of informed consent because we were using deidentified data.In this secondary analysis, we compared 3 outcomes between patients ≤50 years versus those >50 years at the time of their index hospitalization: index hospitalization mortality and, in those that survived to be discharged, length of stay and 30day readmission rates.Adjusted analyses were done using generalized linear mixed models and including baseline covariates recommended by the Centers for Medicare & Medicaid Services (www.cms.gov) for each of the outcomes, as well as hospital type, attending physician specialty, calendar year, number of hospitalizations in the prior 6 months, day of discharge (for the readmission analysis), and 2 random-effects variables to account for clustering effects of province and of hospital.Of the 241 533 patients admitted with a primary diagnosis of HF (mean, 77.4 years, 50.0%male), 7373 (3.1%) were ≤50 (Table ).Younger patients exhibited substantially lower mortality during the index hospitalization (3.3% versus 10.4%, P<0.0001), which was maintained (adjusted odds ratio, 0.36; 95% confidence interval, 0.31-0.41)after adjustment.Of those who survived to discharge (n=217 039), younger patients also had lower 30-day readmissions for any cause (14.1% versus 18.3%, P<0.0001; adjusted odds ratio, 0.82; 95% confidence interval, 0.77-0.88)or for HF (4.7% versus 6.8%, P<0.0001; adjusted odds ratio, 0.74; 95% confidence interval, 0.66-0.83).Although younger patients had shorter length of stay (7.5 days versus 7.8 days, P<0.0001), the difference was not substantial and the association flipped after adjustment for baseline variables: adjusted mean, 8.4 days versus 8.2 days (P=0.002).Although prior studies of young patients with HF have focused on their lower mortality risk, their lower comorbidity burdens, and their increased likelihood of
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 | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».