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Enregistrement W3082687962 · doi:10.5694/mja2.50756

Implementing cardiovascular disease preventive care guidelines in general practice: an opportunity missed

2020· article· en· W3082687962 sur OpenAlexaboutno aff
Charlotte Hespe, Anna Campain, Ruth Webster, Anushka Patel, Lucie Rychetnik, Mark Harris, David Peiris

Notice bibliographique

RevueThe Medical Journal of Australia · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesNational Health and Medical Research CouncilHeart Foundation
Mots-clésMedicinePsychological interventionDiseaseFamily medicineAtrial fibrillationAuditEmergency medicineInternal medicineNursing

Résumé

récupéré en direct d'OpenAlex

Cardiovascular disease (CVD) is the leading cause of death in Australia.1 New treatment guidelines based on absolute CVD risk estimates were adopted in 2012.2 General practitioners are central to implementing these guidelines, as about 90% of people in Australia consult GPs each year,3 but large evidence–practice gaps in the management of people with CVD in general practice have been reported.4 We therefore examined implementation of the 2012 CVD guidelines in general practice by analysing baseline electronic medical record (eMR) data from two clinical trials of computer-supported interventions for improving CVD care conducted during 2015–2018, the INTEGRATE5 and Q Pulse studies.6 Our analysis is based on data for 102 225 patients from 95 general practices in four Australian states and territories. The study was approved by the Human Research Ethics Committees of the University of Sydney (reference, 2015/616) and the University of Notre Dame (reference, 014105S/016011S). De-identified eMR data — demographic information, medical history, prescribed medications, smoking status, blood pressure, low-density lipoprotein cholesterol (LDL-C) levels — were extracted at each practice with the CAT 4 Clinical Audit tool (PenCS). Absolute CVD risk was calculated according to current guidelines2 and patients with a documented CVD diagnosis (coronary heart disease, cerebrovascular disease, peripheral vascular disease, left ventricular hypertrophy, atrial fibrillation, or heart failure) were identified (Box 1). *Including Aboriginal and Torres Strait Islander people aged 35 years or more and non-Indigenous Australians aged 45 years or more, and people of any age at clinically high risk of CVD. Regular attendance was defined as attending the practice at least three times during the preceding 24 months and at least once during the preceding six months. †Australian Cardiovascular Risk Calculator (based on the Framingham Risk Equation). High CVD risk defined as either 5-year risk exceeding 15%, or presence of a clinically high-risk condition.2 ‡Clinically high-risk conditions: people with diabetes and over 60 years of age, diabetes and albuminuria, estimated glomerular filtration rate below 45 mL/min/1.73 m2, systolic blood pressure above 180 mmHg, diastolic blood pressure above 110 mmHg, or total cholesterol level exceeding 7.5 mmol/L. Guideline-recommended treatment was defined as the prescribing of blood pressure- and lipid-lowering medications for patients at high CVD risk, and also of antiplatelet or anticoagulant medications for patients with established CVD (Supporting Information). The proportions of patients who had attained treatment targets for blood pressure (< 140/90 mmHg for patients at high CVD risk, < 130/80 mmHg for people with established CVD or diabetes) and LDL-C level (< 2.0 mmol/L) were calculated. Of 102 225 patients in the two studies, 10 631 (10.4%) had established CVD and 12 983 (12.7%) clinically high risk conditions; estimated CVD risk was high for 2760 (2.7%) and low or intermediate for 46 205 people (45.2%), while the available eMR data were inadequate for estimating risk for 29 645 participants (29%). Among patients with established CVD, 6038 (56.8%) had been prescribed the guideline-recommended treatments; blood pressure targets had been achieved by 4114 patients (38.7%) and LDL targets by 5645 (53.1%). Among the 15 743 patients at high CVD risk, 6486 (41.2%) were prescribed recommended treatments; 8988 (57.1%) had achieved blood pressure targets and 5714 (36.3%) LDL-C targets (Box 1, Box 2). Our findings indicate that primary care management of patients with CVD is sub-optimal. Adopting the absolute risk assessment approach has not improved adherence to management guidelines,4, 7 similar to the experience in Europe, Canada, and the United Kingdom.8, 9 We may have underestimated CVD risk for patients already receiving blood pressure- and lipid-lowering therapies. Risk estimates were based on information in eMR structured data fields; additional information recorded as free text was not considered. Rural and Aboriginal Medical Service practices were under-represented in our practice sample. GPs play essential roles in identifying patients at risk of CVD and managing their treatment,10 but ensuring their adherence to evidence-based recommendations is challenging. While risk assessment tools are important, overcoming patient, GP, and health system barriers to changes in care delivery will be critical to progress. The University of Notre Dame received a Bupa Health Foundation grant for research into cardiovascular disease and diabetes that funded the Q Pulse study and a quality improvement project in 46 practices in the Central and Eastern Sydney Primary Health Network. Ruth Webster is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1125044), Anushka Patel by an NHMRC Principal Research Fellowship (APP1136898), and David Peiris by a Heart Foundation Future Leader Fellowship (101890) and NHMRC Career Development Fellowship (APP1143904). George Health Enterprises, the social enterprise arm of the George Institute for Global Health, has received funding for the development of fixed dose combination therapy, and has commercial relationships involving digital innovations similar to the interventions in the INTEGRATE study. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,035
score de la tête « metaresearch » (Gemma)0,072
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,058
Score d'incertitude au seuil0,186

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0350,072
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,005
Études des sciences et des technologies0,0010,002
Communication savante0,0040,004
Science ouverte0,0020,004
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,274
Tête enseignante GPT0,533
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations20
Publié2020
Routes d'admission1
Résumé présentoui

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