A nationwide adaptive prediction tool for coronary heart disease prevention

British Journal of General Practice

Volumn 53, Issue 496, 2003, Pages 866-870

  Holt, Tim A ^a   Ohno Machado, Lucila ^b  

^a Complexity in Primary Care Group ^*  (United Kingdom)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Adaptive learning; Coronary heart disease; Number needed to screen; Primary prevention

Indexed keywords

ACETYLSALICYLIC ACID; ANTIHYPERTENSIVE AGENT; ANTILIPEMIC AGENT; BETA ADRENERGIC RECEPTOR BLOCKING AGENT; DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR;

ADULT; AGED; CARDIOVASCULAR DISEASE; CARDIOVASCULAR RISK; CORONARY ARTERY DISEASE; DATA ANALYSIS; DISEASE COURSE; ELECTRONIC MEDICAL RECORD; FEEDBACK SYSTEM; FEMALE; GENERAL PRACTICE; HEALTH CARE NEED; HEART INFARCTION PREVENTION; HIGH RISK PATIENT; HUMAN; ISCHEMIC HEART DISEASE; MALE; MEDICAL DOCUMENTATION; METHODOLOGY; MODEL; MONITORING; PREDICTION; PREDICTION AND FORECASTING; PRIMARY PREVENTION; QUALITY CONTROL; REGISTER; REVIEW; RISK ASSESSMENT; STANDARDIZATION;

ADULT; AGED; CORONARY DISEASE; FAMILY PRACTICE; FEMALE; HUMANS; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; PRIMARY PREVENTION; RISK ASSESSMENT;

EID: 0242490949     PISSN: 09601643     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (25)

1. National Service Framework for Coronary Heart Disease. London: Department of Health, 2000.
2. Pringle M, Hobbs R. Large computer databases in general practice. BMJ 1991; 302: 741-742.
3. Simpson D, Nicholas J, Cooper K. The use of information technology in managing patients with coronary heart disease. Informatics in Primary Care 2002; 10: 15-18.
4. Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: The Framingham Study. Am J Cardiol 1976; 38: 46-51.
5. Bishop CM. Neural Networks for Pattern Recognition. Oxford: Oxford University Press, 1995.
6. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Haussler D (ed). Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory. New York: ACM Publications, 1992: 144-152.
7. Breiman L, Friedman J, Olshen RA, Stone CJ. Classification and Regression Trees. New York: Chapman & Hall, 1984.
8. Baxt WG. Complexity, chaos and human physiology: The justification for non-linear neural computational analysis. Cancer Lett 1994; 77: 85-93.
9. Lapuerta P, Azen SP, LaBree L. Use of neural networks in predicting the risk of coronary artery disease. Comput Biomed Res 1995; 28: 38-52.
10. Haq IU, Ramsay LE, Yeo WW, et al. Framingham risk function valid for northern European populations? A comparison of methods for estimating absolute coronary risk in high risk men. Heart 1999; 81(1): 40-46.
11. D'Agostino RB Sr, Grundy S, Sullivan LM, et al. CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: Results of a multiple ethnic groups investigation. JAMA 2001; 286(2): 180-187.
12. Hingorani AD, Vallance P. A simple computer program for guiding management of cardiovascular risk factors and prescribing. BMJ 1999; 318(7176): 101-105.
13. Anderson KM, Wilson PWF, Odell PM, Kannel WB. An updated coronary risk profile. A statement for health professionals. Circulation 1991; 83(1): 356-362.
14. Wood D, Durrington P, Poulter N, et al. Joint British recommendations on prevention of coronary heart disease in clinical practice. Heart 1998; 80(suppl 2): 1S-29S.
15. Scottish Intercollegiate Guidelines Network. Lipids and the Primary Prevetion of Coronary Heart Disease. (Publication 40.) Edinburgh: Scottish Intercollegiate Guidelines Network, 1999.
16. Marshall T, Rouse A. Meeting the National Service Framework for coronary heart disease: Which patients have untreated high blood pressure? Br J Gen Pract 2001; 51: 571-574.
17. McManus RJ, Mant J, Meulendijks CF, et al. Comparison of estimates and calculations of risk of coronary heart disease by doctors and nurses during different calculation tools in general practice: Cross sectional study. BMJ 2002; 324(7335): 459-464.
18. Meal AG, Pringle M, Hammersley V. Time changes in new cases of ischaemic heart disease in general practice. Fam Pract 17(5): 394-400.
19. Franklin SS, Larson MG, Khan SA, et al. Does the relation of blood pressure to coronary heart disease risk change with ageing? The Framingham Heart Study. Circulation 103(9): 1245-1249.
20. Calhoun HM, Dong W, Poulter NR. Blood pressure screening, management and control in England: Results from the health survey for England 1994. J Hypertens 1998; 16(6): 747-752.
21. O'Brien ET, Petrie JC, Littler WA, et al. Blood Pressure Measurement: Recommendations of the British Hypertension Society 3rd ed. London: BMJ Publishing Group, 1997.
22. Moher M, Yudkin P, Turner R, et al. An assessment of morbidity registers for coronary heart diseases in primary care. ASSIST (ASSessment of Implementation STrategt) trial collaborative group. Br J Gen Pract 2000; 50: 706-709.
23. Ohno-Machado L. Modeling medical prognosis: Survival analysis techniques. J Biomed Inform 2001; 34(6): 428-439.
24. Summerton N. Symptoms of possible oncological significance: Separating the wheat from the chaff. BMJ 2002; 325: 1254-1255.
25. Black N. Using clinical databases in practice. BMJ 2003; 326: 2-3.