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COHORH STUDY

COHORH STUDY. A research paper on BMJ. What is cohort study?.

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COHORH STUDY

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  1. COHORH STUDY A research paper on BMJ

  2. What is cohort study? • Investigates from exposure to outcome, in a group of patients without, or with appropriate control DATA; study in which subjects who presently have a certain condition and/or receive a particular treatment are followed over time and compared with another group who are not affected by the condition/have not this treatment

  3. Experimental cohort study • Experimentalc.s. (syn. Randomized controlled clinical trial) cohorts of patients are prospectively and randomly allocated to treatment or control and effects (or adverse effects, AE) are monitored. • Advantages: resistance to BIAS, great definitive power ; • Disadvantages: time consuming, expensive, brief study length identifies only short terms adverse effects, size of study normally not large enough to permit identification of rare AEs

  4. Observational cohort study • Observational c.s. relies on the follow-up of patients and controls; patients are non-randomly assigned a treatment, a comparable group (control) is selected and assigned to either no treatment or another treatment; both groups are then followed prospectively to determine the outcome. • Advantages: less expensive than experimental c.s., identifies new hazards even when they occur with a long latency, can estimate the risk • Disadvantages: appropriate control group may be difficult to define, follow-up is often incomplete, bias may be introduced by choice of patients for different treatment according to the characteristics of the individual drugs

  5. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study Julia Hippisley-Cox; Carol Coupland; Yana Vinogradova BMJ 2007

  6. Introduction • CVD is the leading cause of premature death and a major cause of disability in the UK. Asymptomatic patients thought to be at high risk of cardiovascular disease need to be identified so they can be offered advice about lifestyle changes, such as smoking cessation, physical activity, and diet, about treatment to lower blood pressure and modify cholesterol levels, and about use of aspirin when appropriate. • Framingham risk scores: developed during the peak incidence of cardiovascular disease in America. and over-estimate risk. Any systematic overestimation of risk results in an excessive number of people being identified for treatment. • Scotland ASSIGN scores: different baseline.

  7. Practices and cohorts • We include all the contributing practices in the UK once they using their EMIS system for a year. We randomly allocated two thirds of eligible practices to the derivation dataset, saving the remaining third for the validation dataset. The derivation dataset was used to derive a new risk equation for estimating cardiovascular risk, validated using the validation dataset. • Cohorts: We identified an open cohort of patients aged 35-74 at the date of study entry, drawn from patients registered with eligible practices from 1 January 1995 to 1 April 2007. We excluded those with a diagnosis of cardiovascular disease or diabetes before their entry date. We also excluded temporary residents and patients with interrupted periods of registration with the practice. We also excluded 4% of patients who did not have a valid postcode related Townsend score. In addition we only include patients in the analysis once they had a minimum of one year complete data in their medical record .

  8. Inclusion criteria and Exclusion criteria Inclusion criteria Exclusion criteria

  9. Cardiovascular disease risk factors • Age (in single years) and Sex (men or women) • Smoking (current smoker, non-smoker-including former smoker) • Systolic blood pressure(continuous) • Ratio of total serum cholesterol to high density lipoprotein level (continuous) • Left ventricular hypertrophy recorded in clinical records Y/N • Family history of cardiovascular disease in a first degree relative aged less than 60 • Townsend deprivation score(a good area measure of material deprivation based on four variables) • Current prescription of at least one antihypertensive()thiazide, β blocker, calcium channel blocker, or angiotensin converting enzyme inhibitor) • BMI

  10. Two cohorts We randomly allocated 2/3 of 478 eligible practices to the derivation database, saving the remaining 1/3 for the validation database .

  11. Entry date and censor date • Entry date: For each patient we determined an entry date to the cohort analysis, which was the latest of the following dates: 35th birthday, date of registration with the practice, date on which the practice computer system was installed, and the beginning of the study period (1 January 1995). • Censor date: the earliest date of the dates on which they developed the outcome of interest, the study period ended (1 April 2007), date of death, date of deregistration with the practice, or date of last upload of computerized data.

  12. Cardiovascular disease outcomes • The primary outcome was the first recorded diagnosis of cardiovascular disease on the general practice’s clinical computer system either before or at death occurring between 1 January 1995 and 1 April 2007 • Cardiovascular disease: myocardial infarction, coronary heart disease, stroke, and transient ischaemic attack.

  13. Baseline characteristics • Derivation cohort: • 8.4% of men and 12.4% of women were receiving treatment for blood pressure. • 0.4% had a recorded diagnosis of left ventricular hypertrophy • 9.1% of men and 12.1% of women had a family history of premature coronary heart disease in a first degree relative age less than 60.

  14. Computer record incidence of cardiovascular disease per 1000 year person

  15. Baseline clinical characters

  16. Comparison with health survey for England

  17. Adjusted hazard ratio with 95% CI

  18. Model derivation and development • Cox proportional hazards model was used in the derivation dataset to estimate the coefficients associated with each potential risk facto. • Bayes information criterion was used to compared models. • We checked the assumptions of the proportional hazards model for each variable and tested for any non-linear relation between continuous Independent variables and the outcome. • We used fractional polynomials to model non-linear risk relations with continuous variables when appropriate

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