A national project to improve the quality of secondary prevention strategies: the results of the BRING-UP Prevention study
Notice bibliographique
Résumé
Abstract Background Recent Italian real-world data show that more than 30% of patients hospitalised for a documented acute atherothrombotic event are readmitted to hospital in the year following discharge. Adherence to guideline recommendations for secondary prevention strategies appears to be largely inadequate. Aim To try to narrow the gap between what is recommended and what is implemented in clinical practice, we designed a national implementation science project, BRING-UP Prevention, based on educational programmes and patient data collection. Methods BRING-UP Prevention is a nationwide, observational, prospective, multicentre study enrolling patients with a documented prior atherothrombotic event. The study consists of two 3-month enrolment phases followed by a 6-month follow-up with end-point evaluation, with each enrolment phase preceded by an educational intervention to discuss guideline recommendations. The first phase was recently completed. These data relate to the primary endpoint, which was the percentage of patients achieving the target of LDL cholesterol <55 mg/dL. The first phase has recently been completed. These data refer to the primary endpoint, which was the percentage of patients achieving the target LDL cholesterol level of <55 mg/dL. Results 189 cardiology centres collected data on 4790 patients, 2500 discharged from hospital and 2290 managed as outpatients. Follow-up data at 6 months were available for 4643 patients (96.9%). The rate of patients with LDL cholesterol <55 mg/dL increased from 33% to 58.1%, with absolute and relative increases of 25.1 and 76.0%, respectively. Overall, the proportion of patients with LDL cholesterol ≤70 mg/dL increased from 53.5% to 82.2%. At discharge/end of visit, 96% of patients were on statins and 94.7% were still on statins at 6 months. Atorvastatin and rosuvastatin were the most commonly prescribed statins, in more than 75% of cases at high doses. Ezetimibe was prescribed in 84% of cases. The figure shows LDL cholesterol levels at baseline and after 6 months of follow-up. PCSK9Is were prescribed in 7.7% and inclisiran in 2.3% of patients. Pts with partial or total intolerance to statins were 4.5%. Conclusions Data from the first phase of the BRING-UP Prevention study show that: 1) the rate of pts with a LDL cholesterol increased consistently over the 6-month follow-up period; 2) this result was achieved with high intensity statins, very often in combination with ezetimibe, while the use of new lipid-lowering drugs remained limited. These data show that it is possible to significantly increase the percentage of patients achieving guideline-recommended LDL cholesterol levels with a very favourable cost-benefit approach using a high-intensity statin and ezetimibe. The need to use more potent and costly lipid-lowering approaches is limited to a relatively small proportion of patients.
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 | 0,053 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».