Literature DB >> 28232378

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

Katrina K Poppe1,2, Rob N Doughty2,3, Sue Wells1, Dudley Gentles1, Harry Hemingway4, Rod Jackson1, Andrew J Kerr1,5.   

Abstract

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 UK.
RESULTS: 24 927 patients, 63% men, 63% European, median age 65 years (IQR 58-72 years), experienced 1480 CVD events within 2 years after a CVD risk assessment. A risk score including ethnicity, comorbidities, body mass index, creatine 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. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  cardiovascular disease; electronic health record; risk score; secondary prevention

Mesh:

Year:  2017        PMID: 28232378     DOI: 10.1136/heartjnl-2016-310668

Source DB:  PubMed          Journal:  Heart        ISSN: 1355-6037            Impact factor:   5.994


  8 in total

Review 1.  The Evolving Cardiovascular Disease Risk Scores for Persons with Diabetes Mellitus.

Authors:  Yanglu Zhao; Nathan D Wong
Journal:  Curr Cardiol Rep       Date:  2018-10-11       Impact factor: 2.931

2.  An Exploratory Study of the Use of the Electronic Health Records of Hypertensive Patients to Support the Primary Prevention of Stroke in Shanghai.

Authors:  Tingting Yang; Fen Li; Bifan Zhu; Yuqian Chen; Duo Chen; Changying Wang; Zhiying Hou; Jiajie Xu; Shuwei Gu; Jiefeng Liu; Zhuochun Wu; Ying Wang; Chunlin Jin
Journal:  Risk Manag Healthc Policy       Date:  2020-09-28

3.  Value of Transverse Groove on the Earlobe and Hair Growth on the Ear to Predict the Risk for Coronary Artery Disease and Its Severity among Iranian Population, in Tehran City.

Authors:  Reza Arefi; Mohammad Hassan Namazi; Morteza Safi; Habiboulah Saadat; Hossein Vakili; Mehdi Pishgahi; Saeed Alipour Parsa
Journal:  Galen Med J       Date:  2020-05-22

4.  Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease.

Authors:  Andrew J Steele; Spiros C Denaxas; Anoop D Shah; Harry Hemingway; Nicholas M Luscombe
Journal:  PLoS One       Date:  2018-08-31       Impact factor: 3.240

Review 5.  Ischemia and No Obstructive Coronary Artery Disease ( INOCA ): What Is the Risk?

Authors:  Romana Herscovici; Tara Sedlak; Janet Wei; Carl J Pepine; Eileen Handberg; C Noel Bairey Merz
Journal:  J Am Heart Assoc       Date:  2018-09-04       Impact factor: 5.501

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

Authors:  Richard D Riley; Kym Ie Snell; Joie Ensor; Danielle L Burke; Frank E Harrell; Karel Gm Moons; Gary S Collins
Journal:  Stat Med       Date:  2018-10-24       Impact factor: 2.373

Review 7.  Promises and pitfalls of electronic health record analysis.

Authors:  Ruth Farmer; Rohini Mathur; Krishnan Bhaskaran; Sophie V Eastwood; Nish Chaturvedi; Liam Smeeth
Journal:  Diabetologia       Date:  2017-12-15       Impact factor: 10.122

8.  Dual versus single long-acting bronchodilator use could raise acute coronary syndrome risk by over 50%: A population-based nested case-control study.

Authors:  Lianne Parkin; Sheila Williams; Katrina Sharples; David Barson; Simon Horsburgh; Rod Jackson; Billy Wu; Jack Dummer
Journal:  J Intern Med       Date:  2021-07-21       Impact factor: 8.989

  8 in total

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