Development and validation of risk prediction equations to estimate future risk of heart failure in patients with diabetes: A prospective cohort study

BMJ Open

Volumn 5, Issue 9, 2015, Pages

  Hippisley Cox, Julia ^a   Coupland, Carol ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CHOLESTEROL; HEMOGLOBIN A1C; HIGH DENSITY LIPOPROTEIN;

ADULT; AGED; ARTICLE; ATRIAL FIBRILLATION; BODY MASS; CALIBRATION; CARDIOVASCULAR DISEASE; CARDIOVASCULAR MORTALITY; CARDIOVASCULAR RISK; CHRONIC KIDNEY DISEASE; CLINICAL RESEARCH; COHORT ANALYSIS; DATA BASE; DIABETES MELLITUS; DIABETIC PATIENT; ELECTRONIC MEDICAL RECORD; ETHNICITY; EXTERNAL VALIDITY; FAMILY HISTORY; FEMALE; GENERAL PRACTICE; GROUPS BY AGE; HEART FAILURE; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; ISCHEMIC HEART DISEASE; MAJOR CLINICAL STUDY; MALE; NON INSULIN DEPENDENT DIABETES MELLITUS; OUTCOME ASSESSMENT; PREDICTION; PROSPECTIVE STUDY; RISK ASSESSMENT; SMOKING; SYSTOLIC BLOOD PRESSURE; UNITED KINGDOM; DIABETES COMPLICATIONS; ENGLAND; FACTUAL DATABASE; MIDDLE AGED; PROGNOSIS; PROPORTIONAL HAZARDS MODEL; RECEIVER OPERATING CHARACTERISTIC; RISK FACTOR; SEVERITY OF ILLNESS INDEX; VALIDATION STUDY; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; DATABASES, FACTUAL; DIABETES COMPLICATIONS; ENGLAND; FEMALE; HEART FAILURE; HUMANS; MALE; MIDDLE AGED; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; PROSPECTIVE STUDIES; RISK ASSESSMENT; RISK FACTORS; ROC CURVE; SEVERITY OF ILLNESS INDEX;

EID: 84947931125     PISSN: None     EISSN: 20446055     Source Type: Journal    
DOI: 10.1136/bmjopen-2015-008503     Document Type: Article

References (34)

1. National Clinical Guideline Centre. Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. London, 2014:286.
2. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129(25 Suppl 2): S49-73.
3. Anderson KM, Odell PM, Wilson PW, et al. Cardiovascular disease risk profiles. Am Heart J 1991;121:293-8.
4. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart 2008;94:34-9.
5. van der Leeuw J, van Dieren S, Beulens JW, et al. The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus. Heart 2015;101:222-9.
6. Shah AD, Langenberg C, Rapsomaniki E, et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1-9 million people. Lancet Diabetes Endocrinol 2015;3:105-13.
7. van Dieren S, Beulens JW, Kengne AP, et al. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart 2012;98:360-9.
8. Health and Social Care Information Centre. National Diabetes Audit 2012-2013 Report 2 Complications and Mortality, 2014:37. http://www. hscic.gov.uk/catalogue/PUB16496/nati-diab-audi-12-13-rep2.pdf
9. Yang X, Ma RC, So WY, et al. Development and validation of a risk score for hospitalization for heart failure in patients with Type 2 diabetes mellitus. Cardiovasc Diabetol2008;7:9.
10. Home PD, PocockSJ, Beck-Nielsen H, et al. Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (RECORD): a multicentre, randomised, open-label trial. Lancet 2009;373:2125-35.
11. Khan SS, Butler J, Gheorghiade M. Management of comorbid diabetes mellitus and worsening heart failure. JAMA 2014;311:2379-80.
12. Hippisley-Cox J, Coupland C, Brindle P. Derivation and validation of QStroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study. BMJ 2013;346:f2573.
13. Kannel WB, D'Agostino RB, Silbershatz H, et al. Profile for estimating risk of heart failure. Arch Intern Med 1999;159:1197-204.
14. Agarwal SK, Chambless LE, Ballantyne CM, et al. Prediction of incident heart failure in general practice: the Atherosclerosis Risk in Communities (ARIC) study. Circ Heart Fail 2012;5:422-9.
15. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008;336:1475-82.
16. Hippisley-Cox J, Pringle M. Prevalence, Care and Outcomes for patients with diet controlled diabetes in general practice: cross sectional survey. Lancet 2004;364:423-8.
17. Clarke PM, Gray AM, Briggs A, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia 2004;47:1747-59.
18. Davis TM, Coleman RL, Holman RR, et al. Ethnicity and long-term vascular outcomes in Type 2 diabetes: a prospective observational study (UKPDS 83). Diabeti Med 2014;31:200-7.
19. Nichols GA, Gullion CM, Koro CE, et al. The incidence of congestive heart failure in type 2 diabetes: an update. Diabetes Care 2004;27:1879-84.
20. Stratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000;321:405-12.
21. Collins GS, Altman DG. Predicting the 10year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ 2012;344:e4181.
22. Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol 1999;28:964-74.
23. Steyerberg EW, van Veen M. Imputation is beneficial for handling missing data in predictive models. J Epidemiol Community Health 2007;60:979.
24. Moons KG, Donders RART, Stijnen T, et al. Using the outcome for imputation of missing predictor values was preferred. J Epidemiol Community Health 2006;59:1092.
25. Rubin DB. Multiple imputation for non-response in surveys. New York: John Wiley, 1987.
26. Hosmer D, Lemeshow S. Applied logistic regression. New York: John Wiley & Sons, Inc., 1989.
27. Royston P. Explained variation for survival models. Stata J 2006;6:1-14.
28. Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med 2004;23:723-48.
29. Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015;162:55-63.
30. Hippisley-Cox J, Coupland C. Predicting risk of emergency admission to hospital using primary care data: derivation and validation of QAdmissions score. BMJ Open 2013;3:e003482.
31. Hippisley-Cox J, Coupland C. Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study. BMJ Open 2015;5: e007825.
32. Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: prospective open cohort study. BMJ 2012;344:e3427.
33. Majeed A. Sources, uses, strengths and limitations of data collected in primary care in England. Health Stat Q 2004;21: 5-14.
34. Collins GS, Mallett S, Altman DG. Predicting risk of osteoporotic and hip fracture in the United Kingdom: prospective independent and external validation of QFractureScores. BMJ 2011;342: d3651.