Ethnic group differences in CVD risk estimates using JBS2 and QRISK2 risk scores. Dr Andrew R H Dalton

Transcription

1 Ethnic group differences in CVD risk estimates using JBS2 and QRISK2 risk scores Dr Andrew R H Dalton

2 Cardiovascular Diseases

3 CVD & Ethnic inequalities Standardised mortality ratios (SMR) for heart disease and stroke in South Asians and African Caribbeans compared to Europeans, age 20 69, from [Chaturverdi, N. Heart 2003;89, 681-6]

4 The good, the bad and the

5 Primary Prevention [the] protection of health by personal and communal efforts and elimination of environmental risks A.K.A. Stopping disease in those yet to experience it Recent international interest: US /Can /NZ etc. But England at the vanguard with the NHS Health Check Programme (NHSHC)

6 CVD Risk Scores A prognostic model generated from longitudinal data Predicts the chance of a CVD event in given timespan i.e. NOT the presence of disease Using a suite of CVD risk factors Many uses Clinical- guide treatment decision for individual patient Aid the communication of risk to the patient Formally define eligibility for pharma (NICE- statins) 1 Pre-select high risk populations for the entire preventive process 2 1- NICE 2008; CG67 2-Marshall T. BMJ 2010;340:c1376.

7 CVD risk scores, the NHSHC & Ethnic Health Inequalities Despite common citing in clinical guidelines, evidence risk scores remain under -used in practice 1 However, they are central to NHSHC- Statin /High risk register /lifestyle advice commensurate to risk for all But- DH permits 2 risk scores; QRISK2 & JBS2 Critically these estimate ethnic differences in risk differently QRISK2 1 - intrinsically within the risk score JBS2 2 - a post hoc adjustment (x1.4) for south Asian men 1- Joint British Societies' (JBS2) Heart 2005; 91(suppl 5):v1-v52 2- Hippisley-Cox J, et al. BMJ 2008; 336:1475

8 The question? Are there ethnic differences CVD risk classification between QRISK2 and JBS2 focus on the multiplier applied to south Asian men in JBS2 1 WHY Ethnic risk differences must be accounted for, otherwise People with real need miss treatment from underestimates Overestimates- expose more people to harms of screening 2 ; harms of statins etc. 3 ; impact on cost-effectiveness 1- Jessani S, et al. J Hum Hypertens 2006; 20: Marshall, K. G. CMAJ 2006; 155, Hippisley-cox, J. & Coupland, C. BMJ 2010; 340, c2197.

9 The data Appended data from the 2003 & 2004 HSfE ethnic boost only, Respondents aged 35 to 74 years old, of white, south Asian or Black ethnic groups Excluded those with diagnosed CVD, hypertension and diabetes Variables- those required for risk scores Used multiple imputation for missing biomedical data- imputed 10 datasets, assessing the power based on the fraction of missing information 1 Applied risk score to each dataset, taking mean for each respondent Assumed disease components of QRISK2 absent if missing Classified high risk ( 20%) 1- Graham, J. W., et al., Prevention Science 2007; 8,

10 The data 11,468 eligible respondents

11 Analyses Used non-parametric tests because of skew Present each of the following in each gender /ethnic group The median of each risk score The median difference Spearman coefficient between risk scores Proportion classified high risk for each and the percentage agreement Cohen s Kappa for agreement of high risk Summary of the major CVD Risk factors

12 Sample Male Female Ethnicity White South Asian Black Total Number (%) 3,627 (70.8) 756 (14.8) 470 (9.2) 5,125 Age(years; SE) 51.7 (0.18) 48.3 (0.39) 48.3 (0.38) 50.8 (0.16) Systolic BP(mean; IQR) (17.5) (14.8) (15.8) (17.1) Total Cholesterol (mean; IQR) 5.9 (0.9) 5.6 (0.7) 5.5 (0.7) 5.8 (0.9) BMI (mean; IQR) 27.8 (4.9) 26.2 (4.2) 27.2 (4.6) 27.5 (4.9) Obese (%; SE) 25.2 (0.7) 11.9 (1.2) 21.7 (1.9) 22.8 (0.6) Current smoking (%; SE) 33.0 (0.8) 42.2 (2.1) 46.7 (2.9) 35.3 (0.8) Number (%) 4,560 (71.9) 823 (13.0) 632 (10.0) 6,343 Age(years) 51.9 (0.17) 47.9 (0.34) 47.8 (0.44) 60.0 (0.14) Systolic BP(IQR) (23.0) (18.4) (19.0) (22.1) Total Cholesterol (IQR) 5.9 (1.1) 5.4 (0.9) 5.3 (0.9) 5.8 (1.1) BMI (IQR) 27.3 (6.1) 27.9 (5.6) 30.0 (5.8) 27.7 (6.2) Obese (%; SE) 23.7 (0.6) 26.9 (1.5) 45.7 (2) 26.4 (0.6) Current smoking (%; SE) 31.4 (0.7) 6.5 (1.1) 22.5 (2.2) 28.1 (0.7)

13 Female Male Results White South Asian Black Total JBS Median QRISK risk scoredifference (IQR) 0.78 (3.0) 0.80 (2.1) 0.41 (2.2) 0.72 (2.7) Spearman JBS QRISK Proportion Difference (95% CI) -1.7 (-1.1 to -2.4) -2.5 (-0.5 to -4.5) -3.8 (-1.7 to -5.8) -2.0 (-1.4 to -2.6) high risk Agreement (%) Kappa (se) 0.69 (0.01) 0.75(0.03) 0.76 (0.04) 0.70 (0.01) JBS Median QRISK risk scoredifference (IQR) 3.0 (5.2) 7.5 (8.9) 3.9 (4.7) 3.5 (6.0) Spearman JBS QRISK Proportion high risk Difference (95% CI) 8.8 (5.9 to 9.8) 19.1(16.2 to 22.0) 8.1 (4.0 to 12.3) 8.6 (7.7 to 9.5) Agreement (%) Kappa (se) 0.72 (0.02) 0.60 (0.03) 0.78 (0.05) 0.71 (0.01)

14 Results

15 Results Male Female White South Asian Black JBS2 QRISK2 JBS2 QRISK2 JBS2 QRISK2 Age(years; se) 61.6 (0.21) 64.1 (0.21) 57.8 (0.50) 61.3 (0.68) 62.6 (0.75) 64.9 (0.75) Systolic BP(median; IQR) (18.5) (19.2) (14.3) (15.5) (17.1) 147 (17.1) Total Chol (median; IQR) 6.0 (0.9) 6.0 (0.9) 5.5 (0.8) 5.5 (0.9) 5.6 (0.9) 5.6 (0.9) BMI (median; IQR) 28.3 (4.9) 28.4 (5.1) 26.4 (4.2) 26.4 (4.1) 28 (4.3) 28.4 (4) Obese (%; SE) 29.1 (1.3) 30.8 (1.5) 13.4 (2) 14.4 (2.6) 27.2 (4) 25.7 (4.4) Current smoking (%; SE) 45.7 (1.5) 35.3 (1.6) 53.6 (3.4) 47.2 (4.5) 51.3 (5.6) 38.5 (6.1) Diabetes (%; SE) 8.6 (0.8) 10.8 (1) 27.2 (2.6) 37.8 (3.6) 16.8 (3.4) 19.8 (4) Age(years; se) 65.7 (0.26) 67.8 (0.20) 63.6 (0.71) 65.2 (0.69) 66.8 (0.79) 66.5 (0.74) Systolic BP(median; IQR) (21.8) 146 (20.3) (15.1) (13.7) (9.6) (15) Total Chol (median; IQR) 6.6 (1) 6.6 (1) 5.7 (1.4) 5.8 (1.3) 5.7 (0.8) 5.7 (1.1) BMI (median; IQR) 28.5 (6.2) 28.6 (6.4) 28.8 (4.9) 28.6 (5.2) 31.6 (5) 31.5 (5.7) Obese (%; SE) 33.4 (1.9) 33 (1.7) 34.2 (5.5) 33.3 (5.1) 68.5 (6.4) 59.5 (5.7) Current smoking (%; SE) 53.4 (2.2) 35.1 (1.9) 11.9 (5.1) 8.9 (4.3) 30.8 (9.2) 25 (7.3) Diabetes (%; SE) 12.8 (1.3) 10.6 (1.1) 57.9 (5.7) 46 (5.4) 63 (6.6) 55.4 (5.8)

16 Main Findings The Framingham based JBS2 higher risk prediction & no. high risk in men Women no difference in risk score, larger proportion 20% using QRISK2 The south Asian men, exposed to post hoc risk adjustment see largest difference between scores

17 Strengths and limitations No access to CVD event data, therefore not a validation of risk scores Missing data- but evidence MI can reduce bias Some absent QRISK2 covariates, e.g. AF & Heart Failure BME groups under represented in QResearch- may underestimate risk However; Use of the HSfE allowed comparison of classification in more ethnically diverse population than before The comparison of risk classification, in addition to validity can give important data to health services

18 Implications Evidence the newer QRISK2 more accurately predicts CVD events than JBS2 1 South Asian men may be exposed to greater harm associated with screening- side-effects from statins, general anxieties, etc. Areas with large south Asian populations may have higher spending on CVD prevention Significant impact on NHSHC May result in example of inverse care law Women- QRISK2 may better predict risk? 1- Collins, GS. & Altman, DG. BMJ 2012; 344, e4181.

19 Local example Difference in proportion of population at high risk Proportion of practice south Asian <25% 25-75% >75% Total Mean QRISK2 8.2 ( ) 9.2 ( ) 9.5 ( ) 8.7 (8.4-9) Mean JBS ( ) 12.4 ( ) 13.3 ( ) 11.5 ( ) Difference 2.4 ( ) 3.2 ( ) 3.8 ( ) 2.9 (2.7-3) Median diff in prop'n high risk 3.9 ( ) 6.1 ( ) 7.7 ( ) 4.7 ( ) Median diff in no high risk 69 (10-158) 74 (31-157) 120 (46-317) 76 (10-317)

20 Implications International? The use of ethnicity (perhaps other non-clinical risk factors) may be important in the equity of CVD risk scores The UK is ahead in its use of such variables Further simple risk adjustments may create perverse outcome Prior work show complex recalibration can maintain inaccuracies 1 1- Collins, GS. & Altman, DG. BMJ 2012; 344, e4181.

21 Thanks to my co-authors Dr. Alex Bottle, Dr. Michael Soljak, Prof. Azeem Majeed & Dr Christopher Millett and the NHS Ealing for funding my PhD and providing local data Questions?