External Validation of the H <sub>2</sub> F-PEF Model in Diagnosing Patients With Heart Failure and Preserved Ejection Fraction
Notice bibliographique
Résumé
Patients with heart failure (HF) and reduced ejection fraction often have a more straightforward diagnostic pathway compared with patients with heart failure with preserved ejection fraction (HFpEF). 1 As a consequence, patients with HFpEF, a very heterogenous population without a single clear defining feature (ie, low ejection fraction as in heart failure with reduced ejection fraction), may be misdiagnosed, leading to additional testing and clinical uncertainty.This imprecision has also led to difficulty in clinical trial design.Several diagnostic criteria and algorithms have been proposed to aid clinicians with the diagnosis of HFpEF but lacked sensitivity and had reasonable performance metrics. 2 A recent diagnostic model (H 2 FPEF) may have the potential to overcome the hurdle of HFpEF diagnosis. 3he H 2 FPEF diagnostic model was developed in a cohort of patients with unexplained dyspnea who were referred for invasive hemodynamic exercise testing as the gold standard. 3The H 2 FPEF score had good discriminatory performance with an area under the operating curve of 0.88 in a validation cohort where the prevalence of HFpEF was 64%. 3 It uses 6 clinical and echocardiographic variables including age >60 years, body mass index >30 kg/m 2 , hypertension with ≥2 antihypertensive medications, atrial fibrillation, echocardiographic E/e' >9, and pulmonary artery systolic pressure >35 mm Hg.The total score ranges from 0 to 9 with the scores <2 and scores ≥6, respectively, reflecting low and high likelihoods of HFpEF.Patients with intermediate scores between 2 and 5 require further (invasive) evaluations. 3lthough this model is helpful, it has not been evaluated across other patient populations with suspected HFpEF.Using data collected from the modest-sized Alberta HEART (Alberta Heart Failure Etiology and Analysis Research Team) cohort, we evaluated the performance of the H 2 FPEF model across the spectrum of cardiovascular disease, 2,4 including: (1) patients at-risk for heart failure (n=115); (2) patients at-risk for HF with symptoms of other diseases (eg, lung diseases, coronary artery disease, atrial fibrillation, and others; n=48); (3) HFpEF (n=191, including 46 with previous history of low ejection fraction); (4) reduced ejection fraction (n=169); and (5) age-and sex-matched healthy controls (n=98).The study was approved by the Health Research Ethics Boards, and informed consent was obtained from participants. 4All patients' diagnosis was independently adjudicated by 2 cardiologists after review of all previous information and assessment of echo parameters without using an explicit scoring system.Patients in group 3 (adjudicated HFpEF) were defined as HFpEF, and the other 4 groups were pooled as not-HFpEF.We further explored performance of the H 2 FPEF model in those presenting with dyspnea at the baseline visit, and other details on the cohort are as previously reported. 2,4ge ≥60 years, body mass index >30 kg/m 2 , atrial fibrillation, and hypertension were reported respectively in 83%, 53%, 49%, and 94% of patients with HFpEF.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».