Awareness of lipid guideline recommendations for high-risk patients amongst primary care physicians in Canada
Notice bibliographique
Résumé
Abstract Background Lipid guidelines for cardiovascular (CV) risk reduction have evolved in recent years, particularly since the introduction of PCSK9 inhibitors. In many jurisdictions, CV risk management is provided by primary care physicians (PCPs). We surveyed Canadian PCPs regarding their awareness and implementation of the 2021 Canadian Cardiovascular Society (CCS) lipid guideline recommendations for patients following an acute coronary syndrome (ACS) or for those with diabetes but without CV disease. Methods and results From a national database of PCPs with interest and/or experience in CV medicine, we invited PCPs to complete a survey regarding lipid management in high-risk patients. A committee of PCPs and specialists with lipid expertise including several co-authors of the 2021 CCS lipid guidelines had designed the survey to probe awareness and practice patterns. A total of 203 PCPs from across Canada completed the survey between January and March 2022. 23.6% of respondents had previously prescribed a PCSK9 inhibitor. Almost all (96.5%) PCPs concurred that a post-ACS patient should be seen by their PCP within 4 weeks of hospital discharge (79.3% within 2 weeks). Almost half (45.3%) responded that discharge summaries provided inadequate information relevant for PCPs, and 43% felt that lipid management post-ACS was the primary responsibility of specialists. More than half (56%) articulated challenges when seeing a post-ACS patient, related to inadequate discharge information, complexities of polypharmacy and duration of therapies, and managing perceived or real statin intolerance. 62% correctly identified the LDL-C intensification threshold of 1.8 mmol/L in post-ACS patients, while 79% considered that PCSK9 inhibitors were indicated only for those patients who were already receiving statins plus ezetimibe or had substantially elevated LDL-C levels. 55.2% were able to correctly identify clinical features associated with greatest absolute benefit of PCSK9 inhibitors in post-ACS patients. For patients with diabetes but without ASCVD, 80% of PCPs incorrectly believed that PCSK9 inhibitors were indicated for LDL-C levels above threshold despite statin therapy, and only 42% correctly identified the LDL-C threshold for treatment intensification of 2.0 mmol/L. Conclusion While PCPs are aware of the urgency regarding lipid management in post-ACS patients, many encounter challenges after hospital discharge, frequently deferring lipid management to specialists. Thus, almost one year following publication of the 2021 CCS lipid guidelines, substantial knowledge gaps remain regarding intensification thresholds and treatment options for patients post-ACS or for those with diabetes. Innovative and effective knowledge translation programs are urgently required. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Amgen Canada
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 | 0,002 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 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) | 0,004 | 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 ».