Absolute risk assessment for guiding cardiovascular risk management in a chest pain clinic

Black, J. Andrew; Sharman, James E.; Marwick, T. H.

doi:10.5694/mja2.51321

Cited by 4 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thereby, deprescribing has been an accumulating focus in clinical settings to minimise tangible harm. Ascertaining the predictive value of prior anginal or heart attack and chest pain or discomfort is pivotal, as patients with these symptoms often seek emergency or outpatient assistance and have a greater willingness to engage in proactive risk factor management 38. Cystatin C is a predictor of cardiovascular risk39; however, it has rarely been adopted into previous predictive models.…”

Section: Discussionmentioning

confidence: 99%

“…Ascertaining the predictive value of prior anginal or heart attack and chest pain or discomfort is pivotal, as patients with these symptoms often seek emergency or outpatient assistance and have a greater willingness to engage in proactive risk factor management. 38 Cystatin C is a predictor of cardiovascular risk 39 ; however, it has rarely been adopted into previous predictive models. Current smoking status has been frequently reported as a risk factor; in this study, we leveraged pack-years of smoking, which is a derived variable calculated using the number of cigarettes smoked per day and years smoked, and it was found to be more sensitive than simply using a binary variable of smoking status.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study

You¹,

Guo²,

Kang³

et al. 2023

Stroke Vasc Neurol

View full text Add to dashboard Cite

BackgroundPrevious prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model.MethodsFrom a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation.ResultsDuring a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026).ConclusionML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study

You¹,

Guo²,

Kang³

et al. 2023

Stroke Vasc Neurol

View full text Add to dashboard Cite

show abstract

“…14 Routine use of ACVRa has been shown to promote better health outcomes through improved cardiovascular management. [23][24][25] However, implementation in Australian general practice has proved problematic: a 2020 survey found that 78% of Australian GPs use ACVRa, with just 45% reporting high assessment rates. 26 In baseline audits of general practice in a recent Australian RCT, only 48% of patients aged 45-69 had ACVRa recorded in their notes.…”

Section: Implementation Of Acvra In General Practicementioning

confidence: 99%

Absolute cardiovascular risk assessment by Australian early-career general practitioners: a cross-sectional study

Morgan,

Ralston,

Davey

et al. 2023

Fam Med Com Health

View full text Add to dashboard Cite

ObjectiveTo determine the prevalence and associations of general practice registrars’ performing absolute cardio-vascular risk (ACVR) assessment (ACVRa).DesignA cross-sectional study employing data (2017–2018) from the Registrar Clinical Encounters in Training project, an ongoing inception cohort study of Australian GP registrars. The outcome measure was whether an ACVRa was performed. Analyses employed univariable and multivariable regression. Analysis was conducted for all patient problems/diagnoses, then for an ‘at-risk’ population (specific problems/diagnoses for which ACVRa is indicated).SettingThree GP regional training organisations (RTOs) across three Australian states.ParticipantsGP registrars training within participating RTOs.Results1003 registrars (response rate 96.8%) recorded details of 69 105 problems either with Aboriginal and/or Torres Strait patients aged 35 years and older or with non-Indigenous patients aged 45 years and older. Of these problems/diagnoses, 1721 (2.5% (95% CI 2.4% to 2.6%)) involved an ACVRa. An ACVRa was ‘plausibly indicated’ in 10 384 problems/diagnoses. Of these, 1228 (11.8% (95% CI 11.2% to 12.4%)) involved ACVRa. For ‘all problems/diagnoses’, on multivariable analysis female gender was associated with reduced odds of ACVRa (OR 0.61 (95% CI 0.54 to 0.68)). There was some evidence for Aboriginal and/or Torres Strait Islander people being more likely to receive ACVRa (OR 1.40 (95% CI 0.94 to 2.08), p=0.10). There were associations with variables related to continuity of care, with reduced odds of ACVRa: if the patient was new to the registrar (OR 0.65 (95% CI 0.57 to 0.75)), new to the practice (OR 0.24 (95% CI 0.15 to 0.38)) or the problem was new (OR 0.68 (95% CI 0.59 to 0.78)); and increased odds if personal follow-up was organised (OR 1.43 (95% CI 1.24 to 1.66)). For ‘ACVRa indicated’ problems/diagnoses, findings were similar to those for ‘all problems/diagnoses’. Association with Aboriginal and/or Torres Strait Islander status, however, was significant at p<0.05 (OR 1.60 (95% CI 1.04 to 2.46)) and association with female gender was attenuated (OR 0.88 (95% CI 0.77 to 1.01)).ConclusionContinuity of care is associated with registrars assessing ACVR, reinforcing the importance of care continuity in general practice. Registrars’ assessment of an individual patient’s ACVR is targeted to patients with individual risk factors, but this may entail ACVRa underutilisation in female patients and younger age groups.

show abstract

Effectiveness of rapid access chest pain clinics: a systematic review of patient outcomes and resource utilisation

Black,

Eaves,

Chapman

et al. 2024

Heart

View full text Add to dashboard Cite

BackgroundRapid Access Chest Pain Clinics (RACPC) are widely used for the outpatient assessment of chest pain, but there appears to be limited high-quality evidence justifying this model of care. This study aimed to review the literature to determine the effectiveness of RACPCs.MethodsA systematic review of studies evaluating the effectiveness of RACPCs was conducted to assess the quality of the evidence supporting this model. Outcomes related to effectiveness included major adverse cardiovascular events, emergency department reattendance, cost-effectiveness and patient satisfaction. Study quality was assessed using the RoB 2 tool, Newcastle-Ottawa quality assessment tool or the Consolidated Criteria for Reporting Qualitative Studies checklist, as appropriate.ResultsThirty-two studies were eligible for inclusion, including one randomised trial. Five analytical cohort studies were included, with three comparing outcomes against non-RACPC controls. Three qualitative studies were included. Most reports were descriptive. Findings were consistent with RACPCs being associated with favourable clinical outcomes, reduced emergency department reattendance, cost-effectiveness and high patient satisfaction. However, there was significant heterogeneity in care models, and overall literature quality was low, with a high risk of publication bias.ConclusionWhile the literature suggests RACPCs are safe and efficient, the quality of the available evidence is limited. Further high-quality data from adequately controlled clinical trials or large scare registries are needed to inform healthcare resource allocation decisions.PROSPERO registration numberCRD42023417110.

show abstract

Absolute risk assessment for guiding cardiovascular risk management in a chest pain clinic

Cited by 4 publications

References 2 publications

Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study

Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study

Absolute cardiovascular risk assessment by Australian early-career general practitioners: a cross-sectional study

Effectiveness of rapid access chest pain clinics: a systematic review of patient outcomes and resource utilisation

Contact Info

Product

Resources

About