2Department of Endocrinology, Farhat Hached University Hospital, Sousse Tunisia
Aims: To evaluate the concordance between 3 models predicting the 10 years CVD risk among diabetic patients followed in primary health care centers of Sousse, Tunisia.
Methods: Cross sectional study was conducted in 2011 among diabetic patients followed in 5 randomly selected primary health care centers from the city of Sousse. Data were collected using the "Summary of Diabetes Self-Care Activities" questionnaire and the patient’s clinical records. Patient’s CVD risk was evaluated using the 2008-Framingham, the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort and the Atherosclerosis Risk in Communities (ARIC) models.
Results: Patients with complete data for CVD risk prediction accounted for 136. The mean age of participants was 53.8 ± 8.1 years, 111(81.6 %) were females, 120(88.2 %) had abdominal obesity. Lack of the therapeutic targets achievement was highlighted. Contrary to the 2 other risk models, the 2008-Framingham model was applicable on all participants. Agreement between the 2008-Framingham and the ACC/AHA Pooled Cohort models were better than those between these 2 models and the ARIC model. The agreement levels between the 3 models were most important at the high risk.
Conclusion: CVD risk factors are highly prevalent among diabetic patients in the primary healthcare centers of Sousse city. The use of non calibrated risk models showed disagreement in CVD risk prediction among them. A national program of healthy lifestyle promotion should be implemented before investment in the calibration of original CVD risk scores for Tunisian general and diabetic populations.
Keywords: Cardiovascular Diseases; Type 2 Diabetes Mellitus; Primary Health Care Models; Cardiovascular-Primary Prevention
• The Atherosclerosis Risk in Communities study (ARIC) prediction model calculating the 10-year risk of heart attack or coronary heart disease risk in adults and applicable for black or white people between 45 to 65 years [21]. This model was previously suggested by the ADA and the American Heart Association (AHA) to be incorporated into the decision-making process for aspirin prescription in patients with DM [22].
• The 2008 updated Framingham model, developed in general populations with diabetes as a risk factor and which, unlike the original 1998 and revised 2002 Framingham risk models, predicts the 10 Year Risk of General Cardiovascular Disease including all of the potential manifestations and adverse consequences of atherosclerosis (Coronary heart disease death, Nonfatal myocardial infarction, Coronary insufficiency or angina, Fatal or nonfatal ischemic or hemorrhagic stroke, Transient ischemic attack, Intermittent claudication, Heart failure) for patients aged between 20 and 74 years [23]. This model was previously validated in different ethnic groups [24].
• The American College of Cardiology /American Heart Association (ACC/AHA) Pooled Cohort model, evaluates the 10-year risk of fatal coronary and non-fatal coronary events. This model is intended for use in African, American and non- Hispanic white people from 40 through 79 years of age with an LDL-cholesterol < 190 mg/dL [25]. It was developed by analyzing lengthy population-based cohort studies (including the Framingham and the ARIC studies) and was recommended to replace the 2008-Framingham risk score in the 2013 ACC/AHA guidelines [26].
These 3 risk engines were chosen because they use almost the same variables that are widely available in the primary healthcare setting in Tunisia including: the race (ARIC and ACC/ AHA Pooled Cohort models), the gender, the age, the smoking status, the systolic blood pressure, the use of blood pressure lowering medication, the total cholesterol and the High Density Lipoprotein Cholesterol (HDL-C). Patients are considered to be at high risk if the predicted risk is > 10%, 20%, 7.5% using respectively the ARIC model, the 2008-Framingham model or the ACC/AHA Pooled Cohort model [22, 26-28]. They are considered to be at intermediate risk if the predicted risk is between 5% - 10%, 10% - 20% and 5% - 7.5% using the same risk models respectively [26- 28].
For univariable and multivariable analysis, 3 new variables were created in order to determine the 3 following dependent variables:
• discrepancy in ordering CVD risk between the 2008-Framingham and the ACC/AHA models (“yes” was coded as “1” and “no” was coded as “0”)
• discrepancy in ordering CVD risk between the 2008-Framingham and the ARIC models (“yes” was coded as “1” and “no” was coded as “0”)
• discrepancy in ordering CVD risk between the ACC/AHA and the ARIC models (“yes” was coded as “1” and “no” was coded as “0”)
Besides, each dummy variable represented one category of each explanatory variable and was coded 1 if the case falls in that category and zero if not.
In the univariable analysis, the associated factors with each dependent variable were determined using the χ2 and student-t tests when comparing percentages and means respectively. In multivariable analysis, for each dependent variable, all explanatory variables that were significant at the 20% level were included respectively in 3 binary logistic regression models. Then, a stepwise backward approach was used to select the independent variables significantly associated with each dependent variable. Results of binary logistic regression models were expressed as Odds Ratios (ORs) with confidence level of 95%. All statistical tests were 2-tailed, and p values < 0.05 were considered statistically significant.
were tobacco user, 45 (33.1%) were practicing the recommended physical activity, 37 (27.2%) were taking the recommended amount of fruits and vegetables and 29 (21.3%) were compliant with the sugar free diet (Table 1). Regarding the achievement of the ADA therapeutic targets, 73 (53.7%) had systolic blood pressure upper than 140 mmHg, 100 (73.5%) patients had glycated hemoglobin values upper than 7%, 77 (56.5%) had LDL-C upper than 2.6 m mol/L and 65 (47.8%) had triglycerides upper than 1.7 mmol/L (Table 1).
Characteristics |
n (%) |
Mean (SD) |
Lifestyle |
||
Tobacco use |
10(7.4) |
|
Practice of the recommended physical activity |
45(33.1) |
|
5 serving fruits and vegetables intake per day |
37(27.2) |
|
Duration of diabetes (years) |
|
4.1(3.4) |
Diabetes treatment |
||
Oral drugs |
110(80.9) |
|
Insulin |
4(2.9) |
|
Oral drugs & insulin |
12(8.8) |
|
Sugar free diet only |
10(7.4) |
|
Daily sugar free diet compliance |
29(21.3) |
|
Daily diabetes medication compliance |
90(66.2) |
|
Anthropometric measures |
||
BMI (Kg/m2) |
|
31.8(9.5) |
Overweight |
50(36.8) |
|
Obesity |
70(51.5) |
|
Waist to height ratio > 0.5 |
120(88.2) |
|
Blood pressure |
||
Anti-hypertension medication |
67(49.3) |
|
SBP>140mmHg |
73(53.7) |
|
DBP>90mmHg |
39(28.7) |
|
Glycemic control |
||
Fasting plasma glucose (mmol/l) |
|
10.7(4.9) |
Glycated hemoglobin > 7% |
100(73.5) |
|
Lipid profile |
||
Total cholesterol (mmol/l) |
|
5.1(1.0) |
LDL-C > 2.6 mmol/l |
77(56.6) |
|
Low HDL-C level |
24(17.6) |
|
TG > 1.7 mmol/l |
65(47.8) |
|
Metabolic syndrome |
121(89.0) |
|
10 years CVD risk prediction |
||
2008 Framingham model (%) |
|
19.5(14.4) |
Scores < 10% |
39(28.7) |
|
Scores 10%-20% |
48(35.3) |
|
Scores > 20% |
49(36.0) |
|
ACC/AHA Pooled Cohort model (%) |
|
10.0(10.5) |
Scores < 5% |
52(38.2) |
|
Scores 5%-7.5% |
23(16.9) |
|
Scores > 7.5% |
51(37.5) |
|
Non applicable |
10(7.4) |
|
ARIC model (%) |
|
13.9(9.4) |
Scores < 5% |
9(6.6) |
|
Scores 5%-10% |
37(27.2) |
|
Scores >10% |
61(44.9) |
|
Non applicable |
29(21.3) |
Results of the univariable analysis for the discrepancies between the 3 models are shown in table 3. After multivariable analysis, the younger age remained as a significantly associated factor to the discrepancies between the 3 prediction models. The other most influencing factors on the discrepancies between the 2008-Framingham and the ACC/AHA Pooled Cohort models were: higher diastolic blood pressure and higher LDL-C level. Those influencing the most the discrepancies between the 2008-Framingham and ARIC models were: a BMI under 30 and higher HDL-C level. While the other predictor of discrepancy between the ACC/AHA and ARIC models were female gender (Table 4).
High proportion of females was found among participants. Indeed a predominance of females among the primary healthcare users of Sousse city was highlighted in a previous study [31-34]. Concerning the control of CVD risk factors among participants, there was high prevalence of abdominal obesity and a lack of therapeutic targets achievement among them. In line with these findings, previous studies indicated poor control of major risk factors associated to abdominal obesity in the primary healthcare settings of many other countries [35]. These results illustrate the gap in CVD prevention at the primary care setting in Tunisia. The 2008-Framingham was the most applicable model to the participants and classified more patients at intermediate risk. Its greatest level of agreement was with the ACC/AHA Pooled Cohort model. The 2008-Framingham model is a well-regarded risk tool for evaluating CVD. It has been validated in multiple populations [24]. However, it was reported that it is likely to overestimate CVD risk regardless the glycemic status (normoglycemia, prediabetes, and diabetes) [36]. The ACC/AHA Pooled Cohort equation was designed to overcome some of the limitations of the 2008-Framingham model [26, 37]. It was based on cohorts including participants from the ARIC, the Framingham, the Coronary Artery Risk Development in Young Adults (CARDIA), the Cardiovascular Health (CHS) and Offspring studies [26]. While it has also concern about risk overestimation in addition to a temporary intermediate risk group producing [38, 39, 40].
2008 Framingham model |
ACC/AHA Pooled cohort Model |
||||||||
Concordance |
Discrepancy |
p-value |
K* |
Concordance |
Discrepancy |
p-value |
K* |
||
Overall comparaison |
|||||||||
2008 Framingham model n(%) |
87(69.05) |
39(30.95) |
< 0.001 |
0.54 |
|||||
ACC/AHA Pooled cohort Model n (%) |
87(69.05) |
39(30.95) |
< 0.001 |
0.54 |
|||||
ARIC model n (%) |
63(58.87) |
44(41.12) |
< 0.001 |
0.35 |
59(57.84) |
43(42.15) |
< 0.001 |
0.37 |
|
Comparaison at high risk |
|||||||||
2008 Framingham model n(%) |
111(88.1) |
15 (11.9) |
0.6 |
0.75 |
|||||
ACC/AHA Pooled cohort Model n(%) |
111(88.1) |
15 (11.9) |
0.6 |
0.75 |
|||||
ARIC model n(%) |
77(71.9) |
30(28.1) |
< 0.001 |
0.46 |
78 (76.5) |
24(23.5) |
0.002 |
0.54 |
|
Comparaison at intermediate risk |
|||||||||
2008 Framingham model n(%) |
88(69.8) |
38(30.1) |
< 0.001 |
0.26 |
|||||
ACC/AHA Pooled cohort Model n(%) |
88(69.8) |
38(30.1) |
< 0.001 |
0.26 |
|||||
ARIC model n(%) |
66(61.7) |
41(38.4) |
0.75 |
0.17 |
71(69.6) |
31(30.3) |
0.011 |
0.26 |
|
Comparaison at low risk |
|||||||||
2008 Framingham model n(%) |
101(80.1) |
25(19.9) |
< 0.001 |
0.57 |
|||||
ACC/AHA Pooled cohort Model n(%) |
101(80.1) |
25(19.9) |
< 0.001 |
0.57 |
|||||
ARIC model n(%) |
90(84.1) |
17(15.9) |
< 0.001 |
0.44 |
71(69.6) |
31(30.4) |
< 0.001 |
0.26 |
Characteristics |
2008 Framingham model versus ACC/AHA Pooled Cohort model |
2008 Framingham model versus ARIC model |
ACC/AHA Pooled Cohort model versus ARIC model |
||||||
Agreement |
Discrepancy |
p-value |
Agreement |
Discrepancy |
p-value |
Agreement |
Discrepancy |
p-value |
|
Age(years) mean(SD) |
55.5(8.4) |
52.3(5.4) |
0.012 |
57.7(4.4) |
52.5(6.5) |
0.001 |
57.2(5.3) |
52.0(5.2) |
<0.001 |
Female n (%) |
70(80.5) |
34(87.2) |
0.36 |
41(75.9) |
22(81.5) |
0.571 |
42(71.2) |
42(97.7) |
0.001 |
Tobacco use n(%) |
6(11.3) |
3(12) |
0.93 |
5(12.8) |
2(11.1) |
0.855 |
7(17.5) |
- |
0.027 |
Practice of the recommended physical activity n(%) |
28(32.6) |
11(28.2) |
0.626 |
16(30.2) |
9(33.3) |
0.774 |
22(37.9) |
10(23.3) |
0.117 |
5 serving fruits and vegetables intake per day n(%) |
26(30.6) |
8(21.1) |
0.275 |
19(35.8) |
7(26.9) |
0.428 |
21(36.2) |
9(21.4) |
0.111 |
Waist to Height Ratio > 0.5 n(%) |
77(91.7) |
34(91.9) |
0.967 |
47(92.2) |
23(88.5) |
0.594 |
50(89.3) |
38(92.7) |
0.569 |
Obesity n(%) |
43(50) |
23(60.5) |
0.279 |
33(63.5) |
9(33.3) |
0.011 |
31(53.4) |
24(57.1) |
0.714 |
Metabolic syndrome n(%) |
77(88.5) |
35(89.7) |
0.838 |
49(90.7) |
23(85.2) |
0.453 |
51(86.4) |
39(90.7) |
0.51 |
Diabetes duration>5 years n(%) |
35(46.1) |
11(35.5) |
0.316 |
21(46.7) |
8(38.1) |
0.513 |
23(46.0) |
15(41.7) |
0.69 |
Treatment by insulin n(%) |
9(11.1) |
3(8.1) |
0.617 |
7(13.7) |
3(11.5) |
0.787 |
9(15.8) |
2((5.1) |
0.107 |
Systolic blood pressure(mmHg) mean(SD) |
14.1(2.3) |
14.2(1.9) |
0.83 |
14.9(2.1) |
14.2(1.5) |
0.107 |
14.2(2.4) |
14.1(1.7) |
0.825 |
Diastolic blood pressure(mmHg) mean(SD) |
8.1(1.2) |
8.7(1.3) |
0.025 |
8.5(1.2) |
8.5(1.2) |
0.824 |
8.2(1.2) |
8.4(1.2) |
0.369 |
Glycated hemoglobin(%) mean(SD) |
8.8(2.2) |
9.3(2.0) |
0.26 |
9.1(2.1) |
8.8(1.6) |
0.357 |
9.1(2.3) |
8.9(1.7) |
0.812 |
Total cholesterol(m mol/l) mean(SD) |
5.0(0.9) |
5.4(0.9) |
0.017 |
5.1(1.0) |
5.5(1.1) |
0.143 |
5.0(0.9) |
5.3(0.8) |
0.1 |
LDL-C(m mol/l) mean(SD) |
2.8(0.7) |
3.1(0.8) |
0.031 |
2.9(0.8) |
3.2(1.2) |
0.137 |
2.9(0.8) |
3.0(0.7) |
0.453 |
HDL-C(m mol/l) mean(SD) |
1.4(0.3) |
1.5(0.3) |
0.048 |
1.3(0.4) |
1.4(0.4) |
0.235 |
1.3(0.4) |
1.5(0.3) |
0.003 |
Triglyceride(m mol/l) mean(SD) |
2.0(1.3) |
1.8(0.7) |
0.31 |
2.1(1.5) |
1.9(0.6) |
0.406 |
2.0(1.4) |
1.7(0.7) |
0.125 |
2008 Framingham model versus ACC/AHA Pooled Cohort model |
2008 Framingham model versus ARIC model |
ACC/AHA Pooled Cohort model versus ARIC model |
|||||||
p-value |
OR |
IC 95% |
p-value |
OR |
IC 95% |
p-value |
OR |
IC 95% |
|
Age (year) |
0.008 |
0.92 |
0.87-0.98 |
<0.001 |
0.81 |
0 .73-0.91 |
< 0.001 |
0.8 |
0.72-0.89 |
Female |
0.002 |
36.5 |
3.82-349.07 |
||||||
Male |
1 |
1 |
|||||||
BMI>30 (Kg/m2) |
|
0.001 |
0.09 |
0.02-0.39 |
|||||
BMI ≤ 30 (Kg/m2) |
1 |
1 |
|||||||
Diastolic blood pressure (mmHg) |
0.005 |
1.7 |
1.17-2.50 |
||||||
LDL-C (mmol/l) |
0.009 |
2.19 |
1.21-3.90 |
||||||
HDL-C (mmol/l) |
|
0.025 |
8,33 |
1.30-53.3 |
The ARIC model was the less applicable model to the participants (78.7%) and classified more patients at high risk. Its agreement with the 2 other models were inferior to the agreement between them. This model was previously recommended by the ADA and AHA to be used in patients with DM [20,22]. In fact diabetes specific models were suggested to be used in patients with DM instead of general population models because of a discriminatory advantage of diabetes-specific models over general population-based models for CVD risk stratification in diabetes [46]. However, concerns were reported about risk overestimation by the ARIC model [47]. Furthermore, there is a large variation in the reported performance of diabetes specific risk models [48]. Lack of external validation of these models was also reported in the literature [10]. Further research is required to elucidate if diabetes specific risk models have superior performance than general population models.
The younger age was found to be associated to the discrepancies between the 3 compared models. Another study found similar result highlighting that the younger age is associated to discordance between the 2008-Framingham and UKPDS models [49]. Whereas, different other characteristics were associated with the discrepancies between the 3 models such as a BMI under 30 associated to the discrepancies between the 2008-Framingham and ARIC models similarly to recent studies finding which showed that weight status is associated to a discordance between the ACC/AHA and 2008-Framingham models [50, 51]. Other characteristic found to be associated to the models discrepancies was the female gender when comparing ACC/AHA and ARIC models. In a previous study it was reported that the use of 2008-Framingham rather than a diabetes-specific engine (UKPDS) classified more women at high risk [49]. The opposite was reported in another study [52]. While no significant difference was found between the Framingham and ARIC models among men and women in a study led among multiple ethnic patients [24]. Future calibration of the proposed models should take into account these sources of disagreement.
This study reported the cardiovascular risk profile of patients with diabetes in Tunisia where diabetes is highly prevalent. It focused on primary healthcare as it is a crucial setting for non communicable diseases prevention and management. It provided information to the local clinicians about the issues of using non calibrated CVD risk prediction models. However, the performance of such models was not assessed because of the cross-sectional design. In addition, several other risk models that take into account the family history of CVD, diabetes duration, atrial fibrillation, homoscyteinemia, microalbuminuria…etc were not included to the models comparison. Nonetheless in order to control the sources of disagreement, the 3 models were selected as they use the same variables to predict the CVD risk. In addition, the selected models use data that are easily available in the primary healthcare setting of Tunisia. Finally, data related to the patient’s life style were self reported and not measured objectively which could lead to under or overestimation by the participants.
In order to optimize the care of the Tunisian diabetic patients a national program of healthy lifestyle promotion should be implemented before investment in the calibration of original CVD risk scores for Tunisian general and diabetic populations. Because of the long time required to develop a Tunisian CVD prediction model in addition to resource constraints, it would be more efficient to re-calibrate one of the proposed models. This prediction tool should be then evaluated for its impact on clinical decision and CVD incidence.
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