Development and Validation of a Total Coronary Heart Disease Risk Score in Type 2 Diabetes Mellitus

Development and Validation of a Total Coronary Heart Disease Risk Score in Type 2 Diabetes Mellitus Xilin Yang, PhDa, Wing-Yee So, FRCPa, Alice P.S. Kong, FRCPa,b, Ronald C.W. Ma, MRCPa, Gary T.C. Ko, FRCPc, Chung-Shun Ho, PhDd, Christopher W.K. Lam, PhDd, Clive S. Cockram, MDa, Juliana C.N. Chan, MDa,*, and Peter C.Y. Tong, PhDa There are no validated risk scores for predicting coronary heart disease (CHD) in Chinese patients with type 2 diabetes mellitus. This study aimed to validate the UKPDS risk engine and, if indicated, develop CHD risk scores. A total of 7,067 patients without CHD at baseline were analyzed. Data were randomly assigned to a training data set and a test data set. Cox models were used to develop risk scores to predict total CHD in the training data set. Calibration was assessed using the Hosmer-Lemeshow test, and discrimination was examined using the area under the receiver-operating characteristic curve in the test data set. During a median follow-up of 5.40 years, 4.97% of patients (n ⴝ 351) developed incident CHD. The UKPDS CHD risk engine overestimated the risk of CHD with suboptimal discrimination, and a new total CHD risk score was developed. The developed total CHD risk score was 0.0267 ⴛ age (years) ⴚ 0.3536 ⴛ sex (1 if female) ⴙ 0.4373 ⴛ current smoking status (1 if yes) ⴙ 0.0403 ⴛ duration of diabetes (years) ⴚ 0.4808 ⴛ Log10 (estimated glomerular filtration rate [ml/min/1.73 m2]) ⴙ 0.1232 ⴛ Log10 (1 ⴙ spot urinary albumin-creatinine ratio [mg/mmol]) ⴙ 0.2644 ⴛ non– high-density lipoprotein cholesterol (mmol/L). The 5-year probability of CHD ⴝ 1 ⴚ 0.9616EXP(0.9440 ⴛ [RISK SCORE ⴚ 0.7082]). Predicted CHD probability was not significantly different from observed total CHD probability, and the adjusted area under the receiver-operating characteristic curve was 0.74 during 5 years of follow-up. In conclusion, the UKPDS CHD risk engine overestimated the risk of Chinese patients with type 2 diabetes mellitus and the newly developed total CHD risk score performed well in the test data set. External validations are required in other Chinese populations. © 2008 Elsevier Inc. All rights reserved. (Am J Cardiol 2008;101:596 – 601)

Type 2 diabetes mellitus and vascular diseases, including coronary heart disease (CHD), are major health care burdens.1 However, the Framingham CHD risk score2 and other risk scores for CHD, such as the Systematic Coronary Risk Evaluation and Collaborative Analysis of Diagnostic Criteria in Europe study risk equations,2 do not provide reliable cardiovascular risk estimates in patients with type 2 diabetes mellitus. The United Kingdom Prospective Diabetes Study (UKPDS3 and a Scottish study4 developed risk scores for diabetic populations. However, these risk scores were derived from predominantly Caucasian populations, and their validity in other ethnic groups, such as Chinese, remains to be proved. In addition, these risk scores3,4 predicted only myocardial infarction and CHD death, which represent an extreme end of the CHD spectrum. In this regard, patients with CHD presenting with a

Department of Medicine and Therapeutics, bLi Ka Shing Institute of Health Sciences, cHong Kong Institute of Diabetes and Obesity, and d Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, SAR, China. Manuscript received September 7, 2007; revised manuscript received and accepted October 9, 2007. This work was supported by a Merck Sharp & Dohme University Grant, the Hong Kong Foundation for Research and Development in Diabetes, Hong Kong, China and the Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, SAR, China. *Corresponding author: Tel.: 852-2632-3138; Fax: 852-2632-3108. E-mail address: [email protected] (J.C.N. Chan). 0002-9149/08/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.amjcard.2007.10.019

unstable angina accounted for nearly 50% of patients admitted with acute coronary syndromes, with a mortality rate as high as 5% at 6 months.5 In this study, we examined the validity of the UKPDS CHD risk engine in Chinese and developed a CHD risk score for Chinese diabetic population. Methods The Prince of Wales Hospital is a regional hospital that serves a population of 1.2 million. The Hong Kong Diabetes Registry established in 1995 enrolls 30 to 50 ambulatory diabetic patients each week. Referral sources include general practitioners, community and specialty clinics, and patients discharged from hospitals. Enrolled patients with hospital admissions within 6 to 8 weeks before assessment accounted for ⬍10% of all referrals. The 4-hour assessment of complications and risk factors was performed on an outpatient basis, modified from the European St. Vincent Declaration and the implementation of quality management in diabetes care protocol,6 with a total number of 7,920. Patients with known CHD and heart failure at baseline were excluded from this analysis. Patients with type 1 diabetes, defined as presentation with diabetic ketoacidosis, acute symptoms with heavy ketonuria (ketones ⬎3⫹), or continuous requirement of insulin within 1 year of diagnosis, were also excluded.7 Data for 7,067 type 2 diabetic patients without a history of CHD and heart failure were used in the www.AJConline.org

Coronary Artery Disease/Predicting CHD in Type 2 Diabetes Mellitus

597

Table 1 Baseline clinical and biochemical characteristics of 7,067 Chinese type 2 diabetic patients without a history of coronary heart disease (CHD) or heart failure at enrollment during 5.40 years of follow-up in the training and test data sets Training Data Set (n ⫽ 3,521) Median or Percent Men Smoking status Current Ex Retinopathy Sensory neuropathy Peripheral arterial disease Previous stroke Age (yrs) Body mass index (kg/m2) Years of diagnosed diabetes Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Hemoglobin A1c (%) Spot urine ACR (mg/mmol) eGFR (ml/min/1.73 m2) Low-density lipoprotein cholesterol (mmol/L) High-density lipoprotein cholesterol (mmol/L) Triglycerides (mmol/L) Non–high-density lipoprotein cholesterol (mmol/L) Baseline drug treatment Use of oral antidiabetic drugs Use of antihypertensive drugs Use of insulin Use of lipid-lowering drugs Use of angiotensin-converting enzyme inhibitors/ angiotensin II receptor blockers Events during follow-up Total CHD All-cause death

Test Data Set (n ⫽ 3,546)

Interquartile Range

Median or Percent

Interquartile Range

Total (n ⫽ 7,067) Median or Percent

44.6%

46.1%

45.4%

19.0% 13.1% 25.7% 25.4% 5.7% 4.2% 57 24.7 5 134 76 7.3 2.00 105.4 3.11 1.24 1.39 3.87

21.3% 13.8% 27.2% 26.3% 6.7% 3.8% 56 24.6 5 134 76 7.3 2.00 104.6 3.12 1.25 1.37 3.88

20.6% 13.4% 26.4% 25.8% 6.2% 3.9% 57 24.7 5 134 76 7.3 2.00 104.9 3.11 1.25 1.38 3.87

61.6% 34.4% 17.6% 13.0% 19.9%

5.14% 9.77%

current analysis. Ethical approval was obtained from the Chinese University of Hong Kong Clinical Research Ethics Committee, and written informed consent was obtained from all patients for data analysis and research purpose. Details of assessment methods, laboratory assays, and definitions were described.8 Family history of CHD was defined as premature heart disease in first-degree relatives younger than 60 years. Peripheral arterial disease was defined as lower-limb amputation, absence of foot pulses on palpation further confirmed using Doppler ultrasound examination with an ankle-brachial ratio ⱕ0.90, or history of revascularization for peripheral arterial disease. Renal function was assessed using serum creatinine, and the abbreviated Modification of Diet in Renal Disease formula modified for Chinese9 was used to estimate glomerular filtration rate (eGFR): eGFR (ml/min/1.73 m2) ⫽ 186 ⫻ (serum creatinine ⫻ 0.011)⫺1.154 ⫻ (age)⫺0.203 ⫻ (0.742 if female) ⫻ 1.233, where serum creatinine was expressed as micromoles per liter (converted from original milligrams per deciliter) and 1.233 is the adjusting coefficient for Chinese ethnicity. A sterile random spot urine sample was used to measure albumin-creatinine ratio (ACR). All end points, including hospital admissions and mortality, were censored on July 30, 2005. A trained team of

21 4.9 9 27 14 2.2 9.76 43.4 1.27 0.46 1.11 1.45

60.0% 33.0% 17.3% 11.8% 20.6%

4.79% 9.50%

21 4.9 10 28 14 2.1 10.40 42.9 1.26 0.44 1.13 1.38

Interquartile Range

21 4.9 10 27 14 2.2 10.08 43.1 1.27 0.45 1.11 1.41

60.8% 33.7% 17.5% 12.4% 20.3%

4.97% 9.64%

personnel in the Hospital Authority of Hong Kong coded all hospital discharge diagnoses (of the public hospitals that accounted for 95% of hospital bed-days in Hong Kong) according to the International Classification of Diseases, Ninth Revision, and other related information. We retrieved discharge principle diagnoses from the Hong Kong Hospital Authority Central Computer System. In this study, total CHD was defined as myocardial infarction (code 410) or ischemic heart disease (codes 411 to 414). Clinicians working in Hong Kong adopted the definition of the American Heart Association10 to define myocardial infarction and ischemic heart disease. For validation of the UKPDS CHD risk engine, we used the definition of hard CHD (myocardial infarction and CHD death) as described by the UKPDS group.3,11 Statistical Analysis System (release 9.10; SAS Institute, Cary, North Carolina) was used to perform statistical analysis. The data set was randomly and evenly divided into the 2 data sets (split-half validation) of the training data set for development of the predicting model and the test data set for validation of the developed total CHD risk hazard score. In the training data set, Cox proportional regression analysis with backward algorithms (p ⬍0.05 for stay) was used to identify baseline predictors of total CHD. The candidate baseline variables for inclusion in the final model were age,

598

The American Journal of Cardiology (www.AJConline.org)

Table 2 Parameter estimates of the risk score for coronary heart disease (CHD) in Chinese type 2 diabetic patients using the training data set Predicting Model Variables

Age (yrs) Sex (1 if woman; 0 if man) Current smoker (1 if yes; 0 if otherwise) Duration of diagnosed diabetes (yrs) Log10(eGFR) Log10(1 ⫹ spot urine ACR) (mg/mmol) Non–high-density lipoprotein cholesterol (mmol/L) Hemoglobin A1c (%) Ln(total cholesterol–high-density lipoprotein cholesterol ratio) Systolic blood pressure (/10 mm Hg)

Hong Kong CHD Risk Score Predictors*

UKPDS Risk Engine Predictors Reestimated in the Cohort

Parameter (⫾ SEM)

Hazard Ratio (95% CI)

p Value

Hazard Ratio (95% CI)

p Value

0.0267 ⫾ 0.0071 ⫺0.3536 ⫾ 0.1641 0.4373 ⫾ 0.1837 0.0403 ⫾ 0.0102 ⫺0.4808 ⫾ 0.2166 0.1232 ⫾ 0.0497 0.2644 ⫾ 0.066

1.03 (1.01–1.04) 0.70 (0.51–0.97) 1.55 (1.08–2.22) 1.04 (1.02–1.06) 0.62 (0.40–0.95) 1.13 (1.03–1.25) 1.30 (1.15–1.48) — —

0.0002 0.0312 0.0173 ⬍0.0001 0.0264 0.0132 ⬍0.0001

1.04 (1.03–1.06) 0.81 (0.59–1.10) 1.40 (0.99–1.98)

⬍0.0001 0.1787 0.0561

1.03 (0.95–1.16) 2.74 (1.67–4.50)

0.5136 ⬍0.0001

—

1.09 (1.01–10.17)

0.0205

* Model fit statistics: p ⬍0.0001 for likelihood ratio test and likelihood ratio chi-square ⫽ 125.23. Shrinkage of the fitted model ⫽ 0.9440.

gender, current and exsmoker status, duration of diabetes, systolic/diastolic blood pressure, body mass index, waist circumference, high-density lipoprotein cholesterol, lowdensity lipoprotein cholesterol, triglycerides, non– highdensity lipoprotein cholesterol, total cholesterol, Ln(total– high-density lipoprotein cholesterol ratio, as used in the UKPDS hard CHD risk engine); Log10(1 ⫹ ACR [milligrams per millimolar]), Log10 (eGFR [milliliters per minute per 1.73 m2), sensory neuropathy, retinopathy, peripheral arterial disease, family history of CHD, previous stroke, and drug use variables (Table 1). Proportional hazards assumption and functional form were checked and verified using the Supremum test.12 A p value ⬍0.05 for proportional hazards assumption was considered to violate the assumptions. Similarly, a p value ⬍0.05 for functional forms indicated that further transformation of the continuous variables was needed. Let LR denote the likelihood ratio chi-square and p denote the number of the predictors in the final model. Estimated shrinkage is (LR ⫺ p)/LR, and shrinkage ⬍0.85 may indicate a concern of overfitting.13 Based on a Cox proportional hazard model, risk score is the linear predictor defined as X1 ⫻ ␤1 ⫹ X2 ⫻ ␤2, . . . , ⫹ Xp ⫻ ␤p, and the t-year probability of CHD ⫽ 1 ⫺ S0(t)EXP(SHRINKAGE ⫻ [RISK SCORE ⫺ MEANS OF RISK SCORE]), where X1, X2, . . . ,Xp are baseline independent variables; ␤1, ␤2, . . . , ␤p are the estimated parameters of baseline variables 1 to p, respectively; and S0(t) is survival function over t years when the risk score assumes the value of its means. Then, t-year probability of CHD ⫽ 1 ⫺ S0(t)EXP(SHRINKAGE ⫻ [RISK SCORE ⫺ MEANS OF THE RISK SCORE]). Validation of the risk equation was performed using the test data set. Calibration was checked using the Hosmer and Lemeshow test. Data were divided into deciles of the predicted absolute risk of CHD. Chi-square test (8 degrees of freedom) was constructed using the predicted and observed numbers of CHD stratified by deciles of predicted absolute risk during 5 years of follow-up. A p value ⬍0.05 indicated a significant difference between predicted and observed rates of CHD, suggesting poor calibration.

In survival analysis, overall C index can be regarded as a natural extension of area under the receiver-operating characteristic curve (aROC) and thus a measure of discrimination.14 However, it is difficult to select cut-off values using trade-off between sensitivity and specificity of risk scores derived from survival models. Direct application of aROC in survival models may be problematic because aROC depends on follow-up time.13 More recently, several groups15,16 developed algorithms to calculate time-specific sensitivity, specificity, and aROC. In this study, we used the method of Pencina and D’Agostino14 to calculate overall C index (or c statistics) for overall discrimination and the method of Chambless and Daio16 to calculate aROC, sensitivity, and specificity during 5 years of follow-up for the 5-year specific discrimination and selection of cut-off values. Additional analysis was performed to check the significance of predictors used by the UKPDS hard CHD risk engine in the training data set for comparison with the set of predictors used in the newly developed CHD risk score. Results During a median follow-up of 5.40 years (interquartile range 2.87 to 7.81), 4.97% of patients (n ⫽ 351) had incident total CHD, giving an incidence of 9.28 (95% confidence interval [CI] 8.31 to 10.24) per 1,000 person-years. A total of 2.22% of patients (n ⫽ 157) had hard CHD events during 5.59 years (interquartile range 2.95 to 7.88) of follow-up. Population characteristics are listed in Table 1. Based on estimates of parameters listed in Table 2, the CHD risk score and t-year probability of total CHD were constructed as total CHD risk score ⫽ 0.0267 ⫻ age (years) ⫺ 0.3536 ⫻ sex (1 if female) ⫹ 0.4373 ⫻ current smoking status (1 if yes) ⫹ 0.0403 ⫻ duration of diabetes (years) ⫺ 0.4808 ⫻ Log10(eGFR [ml/min/1.73 m2] ⫹ 0.1232 ⫻ Log10 (1 ⫹ ACR [mg/mmol]) ⫹ 0.2644 ⫻ non– high density lipoprotein cholesterol (mmol/L) and the 5-year probability of CHD ⫽ 1 ⫺ 0.9616EXP(0.9440 ⫻ [RISK SCORE ⫺ 0.7082]). None of the drugs listed in Table 1 was found to be

Coronary Artery Disease/Predicting CHD in Type 2 Diabetes Mellitus

Figure 1. Predicted versus observed hard CHD probability during a 5-year follow-up period using the UKPDS hard CHD risk engine in 7067 Hong Kong Chinese patients with type 2 diabetes.

Figure 2. Predicted versus observed total CHD probability during a 5-year follow-up period using the Hong Kong (HK’s) total CHD risk score in the test data set.

significant and thus they were not included in the predicting model. The p values of the Supremum test of all predictors in the final model for testing proportional hazards assumption and functional form were nonsignificant (p ⬎0.05). The predicted hard CHD probability using the UKPDS CHD risk engine overestimated the probability of hard CHD during a 5-year follow-up period (p ⬍0.05; Figure 1). The overall C index of the UKPDS hard CHD risk engine was 0.610 (95% CI 0.581 to 0.639). The predicted total CHD probability by the newly developed total CHD risk score was not significantly different from the observed probability of total CHD during a 5-year follow-up (p ⬎0.05; Figure 2). The overall C index of the current risk score was 0.704 (95% CI 0.675 to 0.733). The adjusted aROC was 0.737 during 5 years of follow-up. At 1.0352 of the risk score, sensitivity and specificity were 67.6% and 68.5%, respectively. Sensitivity and specificity at different cut-off values are listed in Table 3. Discussion As in the case of overestimation of the absolute risk of CHD in general Chinese populations using the Framingham risk score,17 we found that the UKPDS CHD risk engine overestimated the risks of CHD for Hong Kong Chinese type 2 diabetic patients with fair discrimination power. Conversely, the newly developed total CHD risk score had a discriminatory power of 0.70 (overall C index) while main-

599

taining acceptable calibration for predicting total CHD within 5 years of follow-up. Overall C indexes of the Framingham CHD risk score were 0.79 for men and 0.83 for women in their original population and 0.63 to 0.75 for men and 0.66 to 0.83 for women in other US populations.18 Liu et al17 validated the Framingham risk score in the general Chinese population, and the recalibrated risk score had overall C indexes of 0.71 and 0.74 for Chinese men and women, respectively. Guzder et al19 reported the overall C index of the UKPDS CHD risk engine to be 0.67 in a separate type 2 diabetic population in the United Kingdom. Donnan et al4 developed another risk score for hard CHD events in a type 2 diabetic population with discrimination of 0.71 in the original cohort and 0.69 (overall C index) in an independent data set. The addition of multiple biomarkers in the traditional Framingham CHD risk score resulted in only a small improvement in discrimination.20 Compared with these CHD risk scores for either general or diabetic populations, our newly developed CHD risk score achieved an aROC of 0.74 or overall C index of 0.70, similar to other CHD risk scores. For the 2 risk engines specifically developed for diabetic patients (UKPDS and that of Donnan et al4), the UKPDS risk engine3 used 6 predictors (age, gender, smoking status, hemoglobin A1c, systolic blood pressure, and Ln[total cholesterol– high-density lipoprotein cholesterol ratio]), whereas that of Donnan et al4 used 12 variables (duration of diabetes, onset age, total cholesterol, current smoking status, exsmoking status, gender, Log10[hemoglobin A1c], follow-up time ⫻ hemoglobin A1c, systolic blood pressure, treated hypertension, systolic blood pressure ⫻ treated hypertension, and body height). Our newly developed risk score used 7 variables. Although such conventional risk factors as systolic blood pressure and total cholesterol– high-density lipoprotein ratio, which were selected by the UKPDS model, were significant in our model, they were rendered nonsignificant when eGFR, ACR, and non– high-density lipoprotein cholesterol were included in our equation. The noninclusion of hemoglobin A1c in our risk equation (as either a continuous or categorical variable; i.e., 1 if ⱖ7.0% or 0 if otherwise) may be caused by confounding effects of ACR on hemoglobin A1c. In the UKPDS, Ln(total cholesterol– high-density lipoprotein cholesterol ratio) was a significant predictor for CHD, whereas non– high-density lipoprotein cholesterol was selected by our model. Compared with newly diagnosed patients in the UKPDS, patients enrolled in our registry had variable durations of disease and were more likely to have albuminuria. eGFR and albuminuria were major predictors for CHD, which agreed with other reports.21,22 Taken together, the multiple factors included in the risk equation further lent support to the international guidelines regarding the need of comprehensive assessment for patients with diabetes at referral and at regular intervals thereafter.23,24 There are several possible reasons for the low predictive performance of the UKPDS CHD risk engine in our cohort. First, the UKPDS cohort was a predominantly Caucasian population, whereas ours was Chinese, known to have lower risks of CHD.17 Second, the UKPDS cohort was derived from a randomized clinical trial of patients with newly diagnosed type 2 diabetes, whereas our patients had longer

600

The American Journal of Cardiology (www.AJConline.org)

Table 3 Sensitivity, specificity, and positive and negative predictive values at selected risk scores and the probability of total coronary heart disease (CHD) within 5 years of follow-up in the test data set Risk Score Cut-Off Value* 0.6528 0.8425 0.8896 0.9098 0.9317 0.9552 0.9725 1.0352 1.0695 1.0907 1.1229 1.1795 1.2413 1.2707 1.2971 0.6528

Total CHD Probability Cut-Off Value*

Sensitivity

Specificity

Positive Predictive Value

Negative predictive Value

0.0365 0.0435 0.0455 0.0463 0.0473 0.0483 0.0491 0.0520 0.0536 0.0547 0.0563 0.0593 0.0628 0.0645 0.0661 0.0365

0.812 0.751 0.734 0.727 0.709 0.700 0.684 0.676 0.653 0.646 0.615 0.597 0.566 0.535 0.500 0.812

0.503 0.594 0.614 0.625 0.634 0.644 0.654 0.685 0.694 0.705 0.714 0.734 0.753 0.762 0.771 0.503

0.067 0.075 0.077 0.079 0.079 0.080 0.080 0.086 0.086 0.088 0.086 0.090 0.092 0.090 0.088 0.067

0.984 0.982 0.981 0.981 0.980 0.980 0.979 0.980 0.978 0.978 0.977 0.976 0.975 0.974 0.972 0.984

* Calculated using the CHD risk score and the 5-year probability of total CHD.

durations of type 2 diabetes. Third, Asian diabetic patients have 1 of the highest prevalences of albuminuria (⬃40% had microalbuminuria and 20% had macroalbuminuria)25 and higher incidences of end-stage renal disease compared with Caucasians.26 The high prevalence of albuminuria and chronic kidney disease in the cohort may at least partially account for the difference in risk profiles between the UKPDS cohort and ours.27,28 Our study had several limitations. First, only drug use at baseline was used to develop and validate the risk score. The inclusion of quantified drug treatments during the entire follow-up period may further refine the model. Second, the CHD risk score can only be used to calculate absolute risk of CHD in a relatively short term, such as a 5-year period. However, unlike the UKPDS, our risk score was derived from patients with varying durations of diabetes and thus can be used in a repeated way, for example, every 5-year period during care of patients with type 2 diabetes. 1. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 2004;27:1047–1053. 2. Coleman RL, Stevens RJ, Retnakaran R, Holman RR. Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes. Diabetes Care 2007;30: 1292–1293. 3. Stevens RJ, Kothari V, Adler AI, Stratton IM. The UKPDS risk engine: a model for the risk of coronary heart disease in type II diabetes (UKPDS 56). Clin Sci (Lond) 2001;101:671– 679. 4. Donnan PT, Donnelly L, New JP, Morris AD. Derivation and validation of a prediction score for major coronary heart disease events in a U.K. type 2 diabetic population. Diabetes Care 2006;29:1231–1236. 5. Fox KA, Goodman SG, Klein W, Brieger D, Steg PG, Dabbous O, Avezum A. Management of acute coronary syndromes. Variations in practice and outcome; findings from the Global Registry of Acute Coronary Events (GRACE). Eur Heart J 2002;23:1177–1189. 6. Piwernetz K, Home PD, Snorgaard O, Antsiferov M, Staehr-Johansen K, Krans M, for the DiabCare Monitoring Group of the St. Vincent Declaration Steering Committee. Monitoring the targets of the St. Vincent declaration and the implementation of quality management in diabetes care: the DiabCare initiative. Diabet Med 1993;10:371–377.

7. Laakso M, Pyorala K. Age of onset and type of diabetes. Diabetes Care 1985;8:114 –117. 8. Yang XL, So WY, Kong AP, Ho CS, Lam CW, Stevens RJ, Lyu RR, Yin DD, Cockram CS, Tong PC, Wong V, Chan JC. Development and validation of stroke risk equation for Hong Kong Chinese patients with type 2 diabetes: the Hong Kong Diabetes Registry. Diabetes Care 2007;30:65–70. 9. Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y, Xu JS, Huang SM, Wang LN, Huang W, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol 2006;17:2937–2944. 10. Gibler WB, Cannon CP, Blomkalns AL, Char DM, Drew BJ, Hollander JE, Jaffe AS, Jesse RL, Newby LK, Ohman EM, Peterson ED, Pollack CV. Practical implementation of the guidelines for unstable angina/non-ST-segment elevation myocardial infarction in the emergency department: a scientific statement from the American Heart Association Council on Clinical Cardiology (Subcommittee on Acute Cardiac Care), Council on Cardiovascular Nursing, and Quality of Care and Outcomes Research Interdisciplinary Working Group, in Collaboration With the Society of Chest Pain Centers. Circulation 2005;111:2699 –2710. 11. UK Prospective Diabetes Study (UKPDS). VIII. Study design, progress and performance. Diabetologia 1991;34:877– 890. 12. Lin DY, Wei LJ, Ying Z. Checking the Cox model with cumulative sums of Martingale-based residuals. Biometrika 1993;80:557–572. 13. Harrell FE Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361–387. 14. Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 2004;23:2109 –2123. 15. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005;61:92–105. 16. Chambless LE, Diao G. Estimation of time-dependent area under the ROC curve for long-term risk prediction. Stat Med 2006;25:3474 –3486. 17. Liu J, Hong Y, D’Agostino RB Sr., Wu Z, Wang W, Sun J, Wilson PW, Kannel WB, Zhao D. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA 2004;291:2591– 2599. 18. D’Agostino RB Sr., Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001;286:180 –187. 19. Guzder RN, Gatling W, Mullee MA, Mehta RL, Byrne CD. Prognostic value of the Framingham cardiovascular risk equation and

Coronary Artery Disease/Predicting CHD in Type 2 Diabetes Mellitus

20.

21.

22.

23.

the UKPDS risk engine for coronary heart disease in newly diagnosed type 2 diabetes: results from a United Kingdom study. Diabet Med 2005;22:554 –562. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, Jacques PF, Rifai N, Selhub J, Robins SJ, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 2006;355:2631–2639. So WY, Kong AP, Ma RC, Ozaki R, Szeto CC, Chan NN, Ng V, Ho CS, Lam CW, Chow CC, et al. Glomerular filtration rate, cardiorenal end points, and all-cause mortality in type 2 diabetic patients. Diabetes Care 2006;29:2046 –2052. Gerstein HC, Mann JF, Yi Q, Zinman B, Dinneen SF, Hoogwerf B, Halle JP, Young J, Rashkow A, Joyce C, Nawaz S, Yusuf S. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 2001;286:421– 426. IDF Clinical Guidelines Task Force. Global guideline for type 2 diabetes: recommendations for standard, comprehensive, and minimal care. Diabet Med 2006;23:579 –593.

601

24. Association AD. Clinical practice recommendations. Diabetes Care 2007;30(suppl):S1–S103. 25. Wu AY, Kong NC, de Leon FA, Pan CY, Tai TY, Yeung VT, Yoo SJ, Rouillon A, Weir MR. An alarmingly high prevalence of diabetic nephropathy in Asian type 2 diabetic patients: the MicroAlbuminuria Prevalence (MAP) Study. Diabetologia 2005;48:17–26. 26. Karter AJ, Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV. Ethnic disparities in diabetic complications in an insured population. JAMA 2002;287:2519 –2527. 27. Yang XL, So WY, Kong APS, Ho CS, Lam WK, Ng MHL, Lyu RR, Yin DD, Chow CC, Cockram CS, Tong PCY, Chan JCN. Modified end-stage renal disease risk score for Chinese type 2 diabetic patients—the Hong Kong Diabetes Registry. Diabetologia 2007;50: 1348 –1350. 28. Yang XL, So WY, Kong AP, Clarke P, Ho CS, Lam CW, Ng MH, Lyu RR, Yin DD, Chow CC, et al. End-stage renal disease risk equations for Hong Kong Chinese patients with type 2 diabetes: Hong Kong Diabetes Registry. Diabetologia 2006;49:2299 –2308.