Stroke in patients with heart failure and reduced ejection fraction without atrial fibrillation: external validation of a risk model
Notice bibliographique
Résumé
Abstract Background Heart failure (HF) ranks only second to atrial fibrillation (AF) as a cause of cardio-embolic stroke. Although anticoagulation reduces this risk in HF patients not in AF, the risk/benefit profile in relatively unselected populations is not favourable. Identification of patients at high risk of stroke may allow targeted and safer use of prophylactic anticoagulant therapy. Previously, we proposed a simple risk model for stroke in patients with HF and reduced ejection fraction (HFrEF). However, this model was derived from the two older trials (published in 2007/2008) and was not externally validated. Purpose We aimed to evaluate the current incidence of stroke in patients with HFrEF not in AF receiving modern pharmacological therapy and to validate our stroke prediction model. Methods We examined patient-level data from the PARADIGM-HF, ATMOSPHERE, and DAPA-HF trials. The risk score was calculated following: 7.39×(insulin-treated diabetes) + 6.53×(previous stroke) + 2.80×[ln(NT-proBNP (pg/ml)) × 0.1182]). According to the tertile of risk score, we divided the patients into three groups. Patients with AF were defined as those with either AF on an ECG or a history of AF. Results Of the total of 20,159 patients (who experienced 590 strokes) enrolled in the three trials, 12,751 patients did not have AF at baseline. Of those, 1,143 patients (9%) had insulin-treated diabetes, 873 patients (6.8%) had a history of the previous stroke, and the median value of NT-proBNP was 1,243 pg/ml. During a median follow-up of 2.0 years, 346 (2.7%) experienced a stroke (11.7 per 1000 patient-years). Figure 1 shows cumulative incidence function plots for stroke according to the tertile of risk score in 12,331 patients whose risk score can be calculated. The number of strokes in tertile 1, 2 and 3 were 80, 102 and 149, respectively. The 3-year cumulative incidence function rates of stroke were 2.0 (95% CI: 1.5–2.5) % in tertile 1, 2.6 (95% CI: 2.1–3.2) % in tertile 2, and 4.3 (95% CI: 3.6–5.2) % in tertile 3, respectively. In patients with tertile 3, the stroke rate was 18.1 per 1000 patient-years (compared to 20.1 per 1000 patient-years in patients with AF not receiving anticoagulation). In the Cox model, risk for stroke increased according to the elevation in the risk score (tertile 2: HR 1.47 (95% CI 1.09–1.97), tertile 3: HR 2.53 (95% CI 1.92–3.33), with tertile 1 as reference). Figure 2 shows calibration plots by comparing observed and predicted probabilities of stroke at 1 to 3 years. Discrimination evaluated using the overall c-index 0.84 (95% CI: 0.75–0.91) was good. Conclusions These findings validate a previously described predictive model and confirm that it is possible to identify a subset of HFrEF patients without AF who have a risk of stroke that approximates to that in patients with AF. In these patients, the risk/benefit balance might justify the use of prophylactic anticoagulation, but this hypothesis needs to be tested prospectively. Funding Acknowledgement Type of funding sources: Foundation.
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 | 0,045 | 0,071 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| 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 ».