Differential trajectories in LVEF predicts divergent clinical outcomes in HFrEF patients
Notice bibliographique
Résumé
Abstract Background Recovery or improvement in LVEF is observed in many HFrEF patients following optimal medical management and device therapies, but whether this reflects true myocardial recovery remains controversial and the significance of LVEF decompensation in relation to clinical outcomes is unclear. Purpose To elucidate clinical characteristics and assess prognosis of HFrEF patients with differential trajectories in LVEF. Methods Heart failure (HF) patients were enrolled in a prospective Heart Function registry from outpatient cardiology clinics at an academic institution between Feb 2018 and Nov 2019. Retrospective analysis was conducted on 2D-echocardiography (echo) performed between Jan 2009 and Nov 2019. In total, 590 patients met the inclusion criteria with ≥2 repeated echo evaluations separated by ≥1 year. Patient demographics and clinical characteristics at enrollment were collected through review of medical records. Cardiovascular and HF specific admissions were captured using the corresponding ICD-10-CA codes. During a median follow-up of 5.9 years (IQR: 3.1 to 8.5 years) from the first echo date, clinical outcomes were assessed through composite mortality and hospitalizations endpoints. Results We identified 3 independent cohorts with 279 patients having permanently reduced LVEF (<40%, HFrEF), 236 patients with recovered LVEF (>40% on serial evaluations, HFrecEF) and 75 patients with subsequent decompensation in LVEF (>40%, then <40%, HFdecEF) following initial recovery. Use of ACE inhibitors or ARBs (94% vs. 99% vs. 91%) and beta blockers (88% vs. 87% vs. 87%) at baseline echo was similar amongst HFrEF, HFrecEF and HFdecEF cohorts respectively. HFrecEF cohort had higher usage of MRA (55% vs. 65% vs. 44%, p=0.002) and diuretics (74% vs. 80% vs. 65%, p=0.026). HFdecEF cohort was characterized by a predominance of males (80% vs. 69% vs. 80%, p=0.01), and more patients with ischemic etiology (41% vs. 28% vs. 60%, p<0.001) compared with the HFrecEF cohort and resembled more closely to demographics of the HFrEF cohort. Median LVEF at baseline echo was similar across the cohorts. However, HFdecEF cohort had lower LV end-diastolic diameter (p<0.001), LV end-systolic diameter (p<0.001) and LV mass (p=0.01) compared with the HFrEF cohort sharing similarities with the HFrecEF cohort on baseline echo, suggesting lesser extent of adverse cardiac remodeling in both HFrecEF and HFdecEF cohorts initially. Over a median 5.9 years follow-up, HFdecEF and HFrEF patients had a significantly higher risk (compared to those with HFrecEF) of composite all-cause mortality with all-cause (80% vs. 75% vs. 57%, p=0.004), cardiovascular (48% vs. 50% vs. 29%, p=0.001) and HF hospitalizations (31% vs. 32% vs. 16%, p=0.004). Conclusion HFrEF patients who never recover their LVEF and patients with decompensation in LVEF following initial recovery represent a clinically higher risk group than patients who remained recovered during follow-up. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): University of Alberta Hospital Foundation, Canadian Institutes of Health Research
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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 ».