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Record W3082687962 · doi:10.5694/mja2.50756

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

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Medical Journal of Australia · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilHeart Foundation
KeywordsMedicinePsychological interventionDiseaseFamily medicineAtrial fibrillationAuditEmergency medicineInternal medicineNursing

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.274
GPT teacher head0.533
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it