Cohort Profile: The PREDICT Cardiovascular Disease Cohort in New Zealand Primary Care (PREDICT-CVD 19)

Wells, Sue; Riddell, Tania; Kerr, Andrew; Pylypchuk, Romana; Chelimo, Carol; Marshall, Roger; Exeter, Daniel J.; Mehta, Suneela; Harrison, Jeff; Kyle, Campbell; Grey, Corina; Metcalf, Patricia; Warren, Jim; Kenealy, Tim; Drury, Paul; Harwood, Matire; Bramley, Dale; Gala, Geeta; Jackson, Rod

doi:10.1093/ije/dyv312

Cited by 53 publications

(89 citation statements)

References 10 publications

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“…The comparison population were patients without prior CVD assessed using the PREDICT software between 1 November 2013 and 12 October 2015, sourced mainly from participating primary health organisations (PHOs) in the Auckland and Northland regions of New Zealand 12. These PHOs provide primary healthcare for approximately 80% of the region’s population and the PREDICT-CVD cohort comprises 4 93 993 individuals, about 90% of those eligible for CVD risk assessment in the region.…”

Section: Methodsmentioning

confidence: 99%

Outcome after myocardial infarction without obstructive coronary artery disease

Williams

Barr

Lee

et al. 2018

Heart

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ObjectiveThe medium-term outcome and cause of death in patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) is not well characterised. The aim of this study was to compare mortality and rates of recurrent events in post myocardial infarction (MI) patients with obstructive coronary artery disease (CAD) and in patients with MINOCA compared with an age and sex-matched cohort without cardiovascular disease (CVD).MethodsWe performed a national cohort study of consecutive patients undergoing coronary angiography for MI during 2 years between 2013 and 2015 from the All New Zealand Acute Coronary Syndrome—Quality Improvement (ANZACS QI) registry. MI patient registry data were linked anonymously to national hospitalisation and mortality records. Age and sex matched patients without known CVD formed the comparison group.ResultsOf the 8305 patients with MI, 897 (10.8%) were classified as MINOCA. Compared with those without known CVD, the adjusted HRs for the primary outcome (all-cause death or recurrent non-fatal MI) were 7.81 (95% CI 6.64 to 9.19, p<0.0001) in those with obstructive CAD and 4.64 (95% CI 3.54 to 6.10, p<0.0001) in those with MINOCA. Kaplan-Meier all-cause mortality at 2 years was 7.9% for those with obstructive CAD, with nearly half being CVD deaths (3.6% CVD deaths and 4.5% non-CVD deaths, respectively). In contrast, MINOCA all-cause mortality was 4.9% with non-CVD death (4.5%) predominating.ConclusionsMINOCA is common and has an adverse outcome rate approximately half than that of those with obstructive CAD. The predominant contributor to mortality is non-CVD death. The rate of events in MINOCA is significantly greater than the population without CVD.

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Section: Methodsmentioning

confidence: 99%

Outcome after myocardial infarction without obstructive coronary artery disease

Williams

Barr

Lee

et al. 2018

Heart

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“…The PREDICT web-based decision support programme has been described previously 14. When PREDICT is used by a practitioner to estimate CVD risk for a patient, an electronic risk profile is stored both in the patient record and anonymously in a central database.…”

Section: Methodsmentioning

confidence: 99%

Developing and validating a cardiovascular risk score for patients in the community with prior cardiovascular disease

Poppe

Doughty

Wells

et al. 2017

Heart

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Objective: Patients with atherosclerotic cardiovascular disease (CVD) vary significantly in their risk of future CVD events yet few clinical scores are available to aid assessment of risk. We sought to develop a score for use in primary care that estimates short-term CVD risk in these patients. Methods:Adults aged <80 years with prior CVD were identified from a New Zealand primary care cohort study (PREDICT), and linked to national mortality, hospitalisation, and dispensing databases. A Cox model with an outcome of myocardial infarction, stroke or CVD death within 2 years was developed. External validation was performed in a cohort from the United Kingdom (UK).Results: 24,927 patients, 63% men, 63% European, median age 65 years (IQR 58-72 years), experienced 1,480 CVD events within 2 years after a CVD risk assessment. A risk score including ethnicity, co-morbidities, BMI, creatinine, and treatment, in addition to established risk factors used in primary prevention, predicted a median 2-year CVD risk of 5.0% (IQR 3.5-8.3%). A plot of actual against predicted event rates showed very good calibration throughout the risk range. The score performed well in the UK cohort but overestimated risk for those at highest risk, who were predominantly patients defined as having heart failure. Conclusions:The PREDICT-CVD secondary prevention score uses routine measurements from clinical practice that enable it to be implemented in a primary care setting. The score will facilitate risk communication between primary care practitioners and patients with prior CVD, particularly as a resource to show the benefit of risk factor modification.

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“…The PREDICT primary care CVD risk factor dataset [25] was used to derive the remaining CVD risk factors required for the risk prediction models, namely: SBP, TC:HDL ratio and family history of premature CVD. This dataset is generated from the PREDICT web-based CVD risk assessment and management decision support system used by approximately 80% of general practitioners in the Auckland and Northland regions of New Zealand (approximately 35–40% of all New Zealand general practitioners).…”

Section: Methodsmentioning

confidence: 99%

“…A 2013 PREDICT dataset extract contained the baseline CVD assessments of 272,645 people with complete data recorded on age, sex, ethnicity, NZDep, smoking status, diabetes, SBP, TC:HDL ratio, prior personal history of CVD and family history of premature CVD. The dataset included approximately 50% of all people eligible for risk assessment in the region, but was biased towards higher risk population groups (such as older age groups, people with diabetes and Maori, Pacific and Indian ethnic groups)[25], so could not be used alone to derive the synthetic population.…”

Section: Methodsmentioning

confidence: 99%

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

et al. 2017

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BackgroundMany national cardiovascular disease (CVD) risk factor management guidelines now recommend that drug treatment decisions should be informed primarily by patients’ multi-variable predicted risk of CVD, rather than on the basis of single risk factor thresholds. To investigate the potential impact of treatment guidelines based on CVD risk thresholds at a national level requires individual level data representing the multi-variable CVD risk factor profiles for a country’s total adult population. As these data are seldom, if ever, available, we aimed to create a synthetic population, representing the joint CVD risk factor distributions of the adult New Zealand population.Methods and resultsA synthetic population of 2,451,278 individuals, representing the actual age, gender, ethnicity and social deprivation composition of people aged 30–84 years who completed the 2013 New Zealand census was generated using Monte Carlo sampling. Each ‘synthetic’ person was then probabilistically assigned values of the remaining cardiovascular disease (CVD) risk factors required for predicting their CVD risk, based on data from the national census national hospitalisation and drug dispensing databases and a large regional cohort study, using Monte Carlo sampling and multiple imputation. Where possible, the synthetic population CVD risk distributions for each non-demographic risk factor were validated against independent New Zealand data sources.ConclusionsWe were able to develop a synthetic national population with realistic multi-variable CVD risk characteristics. The construction of this population is the first step in the development of a micro-simulation model intended to investigate the likely impact of a range of national CVD risk management strategies that will inform CVD risk management guideline updates in New Zealand and elsewhere.

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Cohort Profile: The PREDICT Cardiovascular Disease Cohort in New Zealand Primary Care (PREDICT-CVD 19)

Cited by 53 publications

References 10 publications

Outcome after myocardial infarction without obstructive coronary artery disease

Outcome after myocardial infarction without obstructive coronary artery disease

Developing and validating a cardiovascular risk score for patients in the community with prior cardiovascular disease

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

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