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

Poppe, Katrina; Doughty, Rob; Wells, Sue; Gentles, Dudley; Hemingway, Harry; Jackson, Rod; Kerr, Andrew

doi:10.1136/heartjnl-2016-310668

Cited by 25 publications

(17 citation statements)

References 29 publications

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“…Consider the following three examples. First, Poppe et al used a Cox regression to develop a model (“PREDICT‐CVD”) to predict the risk of future CVD events within two years in patients with atherosclerotic CVD, and directly report an

R_{CS_app}^{2}

of 0.04. However, the corresponding C statistic is 0.72, which shows discriminatory magnitude typical of many prognostic models used in practice.…”

Section: How To Prespecify Rboldcs_boldadj2 Based On Previous Informmentioning

confidence: 99%

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes

Riley

Snell

Ensor

et al. 2018

Statistics in Medicine

538

613

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When designing a study to develop a new prediction model with binary or time‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2, and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox‐Snell R2, which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.

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R_{CS_app}^{2}

of 0.04. However, the corresponding C statistic is 0.72, which shows discriminatory magnitude typical of many prognostic models used in practice.…”

Section: How To Prespecify Rboldcs_boldadj2 Based On Previous Informmentioning

confidence: 99%

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes

Riley

Snell

Ensor

et al. 2018

Statistics in Medicine

538

613

View full text Add to dashboard Cite

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“…The primary outcome reported is the composite of all-cause mortality or non-fatal MI. Deaths were categorised as CVD or non-CVD from International Classification of Diseases, 10th Revision, Australian Modification coded national hospital and mortality datasets,13 with CVD death defined as deaths associated with atherosclerotic cardiac, cerebral or peripheral vascular disease (online supplementary table 2). To identify readmissions with further MI, a primary MI code on a subsequent admission was required.…”

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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“…EHRs are widely used to enable contemporary estimation of disease incidence or prevalence [13][14][15], study prospective associations between risk factors and disease outcomes [16], establish trends over time [17] and model the best use of healthcare resources [18,19]. Importantly, many EHRs also provide high-quality data on medication prescribing.…”

Section: The Promise Of Ehr Datamentioning

confidence: 99%

Promises and pitfalls of electronic health record analysis

et al. 2017

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Routinely collected electronic health records (EHRs) are increasingly used for research. With their use comes the opportunity for large-scale, high-quality studies that can address questions not easily answered by randomised clinical trials or classical cohort studies involving bespoke data collection. However, the use of EHRs generates challenges in terms of ensuring methodological rigour, a potential problem when studying complex chronic diseases such as diabetes. This review describes the promises and potential of EHRs in the context of diabetes research and outlines key areas for caution with examples. We consider the difficulties in identifying and classifying diabetes patients, in distinguishing between prevalent and incident cases and in dealing with the complexities of diabetes progression and treatment. We also discuss the dangers of introducing time-related biases and describe the problems of inconsistent data recording, missing data and confounding. Throughout, we provide practical recommendations for good practice in conducting EHR studies and interpreting their results.

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

Cited by 25 publications

References 29 publications

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes

Outcome after myocardial infarction without obstructive coronary artery disease

Promises and pitfalls of electronic health record analysis

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