Journal of Occupational
Health and Epidemiology
Rafsanjan university Of medical sciences
Volume 12, Issue 2 (Spring 2023)
J Occup Health Epidemiol 2023, 12(2): 78-85 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hasandokht T, Maroufizadeh S, Joukar F, Salari A, Mansour-Ghanaei F. Ten-Year Cardiovascular Disease Risk Evaluation in PERSIAN Guilan Cohort Study: A Cross-Sectional Study. J Occup Health Epidemiol 2023; 12 (2) :78-85
URL: http://johe.rums.ac.ir/article-1-656-en.html
URL: http://johe.rums.ac.ir/article-1-656-en.html
Related article in
Google Scholar
Google Scholar
Similar articles
Tolou Hasandokht1 
, Saman Maroufizadeh2 , Farahnaz Joukar3 , Arsalan Salari4 , Fariborz Mansour-Ghanaei *5
, Saman Maroufizadeh2 , Farahnaz Joukar3 , Arsalan Salari4 , Fariborz Mansour-Ghanaei *5
1- Associate Prof., in Preventive and Community Medicine, Cardiovascular Diseases Research Center, Dept. of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
2- Assistant Prof., in Biostatistics, Dept. of Biostatistics and Epidemiology, School of Health, Guilan University of Medical Sciences, Rasht, Iran.
3- Associate Prof., in Epidemiology, Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
4- Cardiovascular Diseases Research Center, Department of Cardiology,
5- Professor of Medicine, Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran. ,fmansourghanaei@gmail.com
2- Assistant Prof., in Biostatistics, Dept. of Biostatistics and Epidemiology, School of Health, Guilan University of Medical Sciences, Rasht, Iran.
3- Associate Prof., in Epidemiology, Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
4- Cardiovascular Diseases Research Center, Department of Cardiology,
5- Professor of Medicine, Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran. ,
Article history
Received: 2022/10/29
Accepted: 2023/04/16
ePublished: 2023/06/28
Accepted: 2023/04/16
ePublished: 2023/06/28
Subject:
Epidemiology
Keywords: Cardiovascular Diseases [MeSH], Risk Assessment [MeSH], Cohort Study [MeSH], Framingham Heart Study [MeSH]
Full-Text [PDF 472 kb]
(690 Downloads)
| Abstract (HTML) (1684 Views)
.jpg)
Fig. 1. Flowchart of the study population
GCS= Guilan cohort study, CVD= cardiovascular disease
Table 1. Demographic and clinical characteristics of the participants in the PERSIAN Guilan Cohort Study by sex
.jpg)
Fig. 2. Distribution of high estimated 10-year risk of CVD events (≥20%) using three cardiovascular disease risk assessment tools in the PERSIAN Guilan Cohort Study.
Note. ACC/AHA: American College of Cardiology/American Heart Association; FRS: Framingham Risk Score; WHO/ISH: World Health Organization/International Society of Hypertension
Full-Text: (265 Views)
Introduction
Cardiovascular diseases (CVDs) are the most important public health problem in the world [1], particularly in low- and middle-income countries [2]. The proportions of mortality related to ischemic heart disease (IHD) from all death among Iranian men and women were 40% and 44%, respectively [3]. Several health behaviors and factors such as unhealthy diet, obesity, physical inactivity, smoking, high blood pressure, glucose intolerance, and high cholesterol level contribute to predisposition to CVD. A national report of STEPwise approach to surveillance (STEPS) study indicated less than 4 % of Iranian adults have ≥ 6 ideal controlled CVD risk factors in 2016 [4]. Hence, this issue needs immediate multicomponent prevention programs at the public health and individual levels. A recent study showed the high healthcare cost for secondary prevention in high-risk CVD people in first year, while it substantially decreased over time [5]. To reduce CVD healthcare cost and effectively implement prevention programs at the community level, target oriented strategies have been proposed in previous studies [6]. For this purpose cardiovascular risk prediction models are known as a practical method to identifying those at higher risk who will derive the greatest benefit [7]. Several CVD risk score models have been presented including Framingham risk score (FRS), Systematic Coronary Risk Evaluation (SCORE) algorithm, world health organization (WHO/ISH) risk chart, and American College of Cardiology/American Heart Association (ACC/AHA) risk score [8]. WHO recommended a prevention program for non-communicable disease (NCD) as well as a CVD risk chart for stratification of CVD risk in the low- and middle-income countries. In response to substantial CVD burden in Iran, WHO CVD risk chart was used in the national prevention program. We estimate the frequency of 10-year CVD risk among Iranian people based on PERSIAN Guilan Cohort Study using ACC/AHA, Framingham risk score, and WHO risk chart as well as assess the agreement between the three risk scores.
Materials and Methods
This is a descriptive study on PERSIAN Guilan Cohort Study (PGCS) (The Ethics Approval No was IR.GUMS.REC.1398.480); a prospective, population-based cohort study was designed in Guilan province as described in detail previously [9-11]. In brief, The Guilan cohort study (GCS) involved 10520 participants aged within 35-70 years in northern Iran, Guilan province, between October 8, 2014 and January 20, 2017 as part of the Prospective Epidemiological Research Studies in Iran (PERSIAN). Eligible subjects were invited to participate in the study through telephone call. All study data including demographic characteristics, socio-economic status, lifestyle and sleep habits, anthropometric indices, blood pressure, and biological samples were recorded based on the cohort protocol. Also, annual active follow-ups were planned for next 15 years for all participants.
We excluded data related to those with a history of CVD. According to GCS, all data related to those with a past history of myocardial infarction, coronary angioplasty, coronary bypass surgery, stroke, and any history of ischemic heart disease based on self-report and patient’s health data were excluded.
For the present study, the data related to individuals aged 40-70 years were used. The data included:
1) Demographic factors (age, sex, place of residence (city or rural), marital status (single, married, widow, divorced), occupation (employed, unemployed), years of education),
2) Anthropometric indices (weight, height, body mass index: BMI),
3) History of smoking and alcohol,
4) Clinical data (hypertension (HTN), diabetes mellitus (DM), taking anti-hypertensive medications,
5) Laboratory data such as fasting blood sugar (FBS), triglycerides (TG), high-density lipoprotein (HDL), low density lipoprotein (LDL), total cholesterol (TC), and systolic blood pressure (SBP). Body mass Index (BMI) was classified as underweight (BMI<18.5 kg/m2), normal weight (BMI= 18.5-24.99 kg/m2), overweight (BMI= 25-29.9 kg/m2), and obese (BMI≥ 30 kg/m2).
The 10-year CVD events for every individual were calculated based on the three risk scores models (ACC/AHA, FRS, WHO/ISH).
Cardiovascular Disease Risk Assessment Tools
ACC/AHA: The American College of Cardiology/ American Heart Association Atherosclerotic Cardiovascular Disease (ACC/AHA ASCVD) Risk Score estimates the 10-year risk for atherosclerotic CVD, defined as fatal or nonfatal myocardial infarction, or fatal or non-fatal stroke, based on the pooled cohort equations. The variables included age, gender, race (other), total cholesterol, HDL, systolic blood pressure, on treatment for hypertension, smoking status, and diabetes [12]. We defined three categories as low risk (calculated risk score <5%), moderate (risk = 5% -20%), and high (risk ≥ 20 %). ASCVD risk was calculated at individual level for each participant using COX proportional hazard model based on the coefficients of ACC/AHA model [13].
FRS: Framingham Risk Score is based on the data derived from middle-class White Americans in the 1970s to 1980s. It estimates the 10-year risk of CVD (coronary artery disease, stroke, peripheral vascular disease, congestive heart failure, and cardiac death) using age, gender, systolic blood pressure, total cholesterol, HDL, smoking status, treatment of hypertension, and diabetes status [14]. We used the Framingham multivariable risk Function which has been formulated by D’Agostino et al [15]. FRS was defined as low (risk <10%), moderate (risk = 10%-20%), and high (risk ≥ 20%).
WHO/ISH: The WHO prediction paper charts for each sub-region were developed to estimate the 10-year risk of CVD (myocardial infarction, angina pectoris, and stroke) based on several variables including age, gender, diabetes status, smoking status, systolic blood pressure, and total cholesterol [14]. We calculated the risk for each individual using a R syntax code based on Collins & et al study [16]. WHO risk score was classified as low (risk <10%), moderate (risk = 10 % - 20 %), and high (risk ≥ 20%).
In the present study, continuous variables were expressed as mean ± standard deviation (SD) while categorical variables as number (percentage). Demographic and clinical characteristics between males and females were compared using independent t test for continuous variables, and chi-square test for categorical variables. The weighted kappa coefficient (κw) was used to assess agreement between the three CVD risk assessment tools. The value of κw ranges from -1 to 1. A κw value of 0 indicates there is no agreement, whereas a κw value of 1 indicates perfect agreement. Values of κw are often interpreted as follows: <0.20, poor; 0.21-0.40, fair; 0.41- 0.60, moderate; 0.61-0.80, substantial; >0.80, very good. All data analyses were performed using SPSS for windows, version 16.0 (SPSS Inc., Chicago, IL, USA), except for the κw values, which were calculated using MedCalc for Windows, version 18.9.1 (MedCalc Software, Ostend, Belgium).
Results
After excluding the data related to those with a history of CVD (10.6%), 9398 individuals aged 40-70 were entered in the study (Figure 1). The demographic and clinical characteristics of the participants are outlined in Table 1. The mean age of the participants was 52.53 ± 8.25 years ranging within 40-73 years. Of the participants, 53.9% were female and more than 24% were smoker. The frequency of DM, HTN, and overweight/obese was 24.4%, 43.7%, and 72.5%, respectively.
Cardiovascular diseases (CVDs) are the most important public health problem in the world [1], particularly in low- and middle-income countries [2]. The proportions of mortality related to ischemic heart disease (IHD) from all death among Iranian men and women were 40% and 44%, respectively [3]. Several health behaviors and factors such as unhealthy diet, obesity, physical inactivity, smoking, high blood pressure, glucose intolerance, and high cholesterol level contribute to predisposition to CVD. A national report of STEPwise approach to surveillance (STEPS) study indicated less than 4 % of Iranian adults have ≥ 6 ideal controlled CVD risk factors in 2016 [4]. Hence, this issue needs immediate multicomponent prevention programs at the public health and individual levels. A recent study showed the high healthcare cost for secondary prevention in high-risk CVD people in first year, while it substantially decreased over time [5]. To reduce CVD healthcare cost and effectively implement prevention programs at the community level, target oriented strategies have been proposed in previous studies [6]. For this purpose cardiovascular risk prediction models are known as a practical method to identifying those at higher risk who will derive the greatest benefit [7]. Several CVD risk score models have been presented including Framingham risk score (FRS), Systematic Coronary Risk Evaluation (SCORE) algorithm, world health organization (WHO/ISH) risk chart, and American College of Cardiology/American Heart Association (ACC/AHA) risk score [8]. WHO recommended a prevention program for non-communicable disease (NCD) as well as a CVD risk chart for stratification of CVD risk in the low- and middle-income countries. In response to substantial CVD burden in Iran, WHO CVD risk chart was used in the national prevention program. We estimate the frequency of 10-year CVD risk among Iranian people based on PERSIAN Guilan Cohort Study using ACC/AHA, Framingham risk score, and WHO risk chart as well as assess the agreement between the three risk scores.
Materials and Methods
This is a descriptive study on PERSIAN Guilan Cohort Study (PGCS) (The Ethics Approval No was IR.GUMS.REC.1398.480); a prospective, population-based cohort study was designed in Guilan province as described in detail previously [9-11]. In brief, The Guilan cohort study (GCS) involved 10520 participants aged within 35-70 years in northern Iran, Guilan province, between October 8, 2014 and January 20, 2017 as part of the Prospective Epidemiological Research Studies in Iran (PERSIAN). Eligible subjects were invited to participate in the study through telephone call. All study data including demographic characteristics, socio-economic status, lifestyle and sleep habits, anthropometric indices, blood pressure, and biological samples were recorded based on the cohort protocol. Also, annual active follow-ups were planned for next 15 years for all participants.
We excluded data related to those with a history of CVD. According to GCS, all data related to those with a past history of myocardial infarction, coronary angioplasty, coronary bypass surgery, stroke, and any history of ischemic heart disease based on self-report and patient’s health data were excluded.
For the present study, the data related to individuals aged 40-70 years were used. The data included:
1) Demographic factors (age, sex, place of residence (city or rural), marital status (single, married, widow, divorced), occupation (employed, unemployed), years of education),
2) Anthropometric indices (weight, height, body mass index: BMI),
3) History of smoking and alcohol,
4) Clinical data (hypertension (HTN), diabetes mellitus (DM), taking anti-hypertensive medications,
5) Laboratory data such as fasting blood sugar (FBS), triglycerides (TG), high-density lipoprotein (HDL), low density lipoprotein (LDL), total cholesterol (TC), and systolic blood pressure (SBP). Body mass Index (BMI) was classified as underweight (BMI<18.5 kg/m2), normal weight (BMI= 18.5-24.99 kg/m2), overweight (BMI= 25-29.9 kg/m2), and obese (BMI≥ 30 kg/m2).
The 10-year CVD events for every individual were calculated based on the three risk scores models (ACC/AHA, FRS, WHO/ISH).
Cardiovascular Disease Risk Assessment Tools
ACC/AHA: The American College of Cardiology/ American Heart Association Atherosclerotic Cardiovascular Disease (ACC/AHA ASCVD) Risk Score estimates the 10-year risk for atherosclerotic CVD, defined as fatal or nonfatal myocardial infarction, or fatal or non-fatal stroke, based on the pooled cohort equations. The variables included age, gender, race (other), total cholesterol, HDL, systolic blood pressure, on treatment for hypertension, smoking status, and diabetes [12]. We defined three categories as low risk (calculated risk score <5%), moderate (risk = 5% -20%), and high (risk ≥ 20 %). ASCVD risk was calculated at individual level for each participant using COX proportional hazard model based on the coefficients of ACC/AHA model [13].
FRS: Framingham Risk Score is based on the data derived from middle-class White Americans in the 1970s to 1980s. It estimates the 10-year risk of CVD (coronary artery disease, stroke, peripheral vascular disease, congestive heart failure, and cardiac death) using age, gender, systolic blood pressure, total cholesterol, HDL, smoking status, treatment of hypertension, and diabetes status [14]. We used the Framingham multivariable risk Function which has been formulated by D’Agostino et al [15]. FRS was defined as low (risk <10%), moderate (risk = 10%-20%), and high (risk ≥ 20%).
WHO/ISH: The WHO prediction paper charts for each sub-region were developed to estimate the 10-year risk of CVD (myocardial infarction, angina pectoris, and stroke) based on several variables including age, gender, diabetes status, smoking status, systolic blood pressure, and total cholesterol [14]. We calculated the risk for each individual using a R syntax code based on Collins & et al study [16]. WHO risk score was classified as low (risk <10%), moderate (risk = 10 % - 20 %), and high (risk ≥ 20%).
In the present study, continuous variables were expressed as mean ± standard deviation (SD) while categorical variables as number (percentage). Demographic and clinical characteristics between males and females were compared using independent t test for continuous variables, and chi-square test for categorical variables. The weighted kappa coefficient (κw) was used to assess agreement between the three CVD risk assessment tools. The value of κw ranges from -1 to 1. A κw value of 0 indicates there is no agreement, whereas a κw value of 1 indicates perfect agreement. Values of κw are often interpreted as follows: <0.20, poor; 0.21-0.40, fair; 0.41- 0.60, moderate; 0.61-0.80, substantial; >0.80, very good. All data analyses were performed using SPSS for windows, version 16.0 (SPSS Inc., Chicago, IL, USA), except for the κw values, which were calculated using MedCalc for Windows, version 18.9.1 (MedCalc Software, Ostend, Belgium).
Results
After excluding the data related to those with a history of CVD (10.6%), 9398 individuals aged 40-70 were entered in the study (Figure 1). The demographic and clinical characteristics of the participants are outlined in Table 1. The mean age of the participants was 52.53 ± 8.25 years ranging within 40-73 years. Of the participants, 53.9% were female and more than 24% were smoker. The frequency of DM, HTN, and overweight/obese was 24.4%, 43.7%, and 72.5%, respectively.
Fig. 1. Flowchart of the study population
GCS= Guilan cohort study, CVD= cardiovascular disease
Table 1. Demographic and clinical characteristics of the participants in the PERSIAN Guilan Cohort Study by sex
| Total (n=9398) |
Male (n=4337) |
Female (n=5061) |
||
| Age (years), mean ± SD | 52.53 ± 8.25 | 52.49 ± 8.21 | 52.57 ± 8.29 | |
| Marital status, n (%) | Single | 263 (2.8) | 60 (1.4) | 203 (4.0) |
| Married | 8519 (90.6) | 4210 (97) | 4309 (85.1) | |
| Widowed | 512 (5.4) | 43 (1.0) | 469 (9.3) | |
| Divorced/Separated | 104 (1.1) | 24 (0.6) | 80 (1.6) | |
| Place of residence, n (%) | Urban | 4172 (44.4) | 1855 (42.8) | 2317 (45.8) |
| Rural | 5226 (55.6) | 2482 (57.2) | 2744 (54.2) | |
| Years of education (years) | 6.49 ± 4.49 | 7.56 ± 4.49 | 5.57 ± 4.28 | |
| Occupation, n (%) | Employed | 5134 (54.6) | 3848 (88.7) | 1286 (25.4) |
| Unemployed | 4264 (45.4) | 489 (11.3) | 3775 (74.6) | |
| BMI (kg/m2), mean ± SD | 28.13 ± 5.10 | 26.01 ± 4.19 | 29.95 ± 5.10 | |
| BMI, n (%) | Underweight | 124 (1.3) | 97 (2.2) | 27 (0.5) |
| Normal | 2463 (26.2) | 1704 (39.3) | 759 (15.0) | |
| Overweight | 3750 (39.9) | 1858 (42.8) | 1892 (37.4) | |
| Obese | 3061 (32.6) | 678 (15.6) | 2383 (47.1) | |
| Smoking, n (%) | Yes | 2305 (24.5) | 2253 (51.9) | 52 (1.0) |
| No | 7093 (75.5) | 2084 (48.1) | 5009 (99.0) | |
| Alcohol, n (%) | Yes | 1243 (13.2) | 995 (22.9) | 248 (4.9) |
| No | 8155 (86.8) | 3342 (77.1) | 4813 (95.1) | |
| Diabetes, n (%) | Yes | 2291 (24.4) | 884 (20.4) | 1407 (27.8) |
| No | 7107 (75.6) | 3453 (79.6) | 3654 (72.2) | |
| Hypertension, n (%) | Yes | 4104 (43.7) | 1684 (38.8) | 2641 (52.2) |
| No | 5294 (56.3) | 2653 (61.2) | 2420 (47.8) | |
| Antihypertensive drug use, n (%) | Yes | 2025 (21.5) | 834 (19.2) | 1191 (23.5) |
| No | 7373 (78.5) | 3503 (80.8) | 3870 (76.5) | |
| SBP (mm Hg), mean ± SD | 118.57 ± 16.74 | 119.07 ± 16.56 | 118.14 ± 16.88 | |
| LDL (mg/L, mean ± SD | 113.35 ± 32.04 | 112.63 ± 31.65 | 113.96 ± 32.35 | |
| HDL-c (mg/dL), mean ± SD | 48.58 ± 11.04 | 46.64 ± 10.53 | 50.25 ± 11.20 | |
| TC (mg/dL), mean ± SD | 193.57 ± 38.29 | 191.79 ± 38.16 | 195.10 ± 38.34 | |
| TG (mg/dL), mean ± SD | 160.6 ± 101.8 | 165.7 ± 111.7 | 156.29 ± 92.40 | |
| FBS (mg/dl), mean ± SD | 105.07 ± 37.71 | 103.85 ± 35.42 | 106.12 ± 39.54 | |
SD: Standard Deviation; BMI: Body Mass Index; SBP: Systolic Blood Pressure; LDL, Low Density Lipoprotein; HDL: High Density Lipoprotein; TC: Total Cholesterol; TG, Triglyceride; FBS: Fasting Blood Sugar.
Table 2 presents the distribution of CVD risk scores based on ACC/AHA, FRS, and WHO/ISH severity cutoffs. The frequency of people with high CVD risk according to ACC/AHA, FRS, and WHO/ISH was 5.4%, high, 16.1%, 17.5%, respectively. The frequency of high CVD risk score in males was higher than in females based on all three risk scores.
Table 2. Distribution of estimated 10-year risk of CVD events using three cardiovascular disease risk assessment tools in the PERSIAN Guilan Cohort Study by sex and age group
Table 2. Distribution of estimated 10-year risk of CVD events using three cardiovascular disease risk assessment tools in the PERSIAN Guilan Cohort Study by sex and age group
| CVD Risk Score | Total | Age | p-value | Sex | p-value | |||
| <55 years | ≥55 years | Male | Female | |||||
| ACC/AHA | Low (<5%) | 5752 (61.2) | 4650 (81.6) | 1102 (29.8) | <0.001 | 1736 (40.0) | 4016 (79.4) | <0.001 |
| Moderate (5 % to 20%) |
3136 (33.4) | 1006 (17.7) | 2130 (57.6) | 2143 (49.4) | 993 (19.6) | |||
| High (≥20%) | 510 (5.4) | 43 (0.8) | 467 (12.6) | 458 (10.6) | 52 (1.0) | |||
| FRS | Low (<10%) | 5750 (61.2) | 4440 (77.9) | 1310 (35.4) | <0.001 | 1724 (39.8) | 4026 (79.5) | <0.001 |
| Moderate (10% to 20%) | 2137 (22.7) | 1000 (17.5) | 1137 (30.7) | 1328 (30.6) | 809 (16.0) | |||
| High (≥20%) | 1511 (16.1) | 259 (4.5) | 1252 (33.8) | 1285 (29.6) | 226 (4.5) | |||
| WHO/ISH | Low (<10%) | 6588 (70.1) | 5352 (93.9) | 1236 (33.4) | <0.001 | 2996 (69.1) | 3592 (71.0) | <0.001 |
| Moderate (10% to 20%) | 1167 (12.4) | 251 (4.4) | 916 (24.8) | 483 (11.1) | 684 (13.5) | |||
| High (≥20%) | 1643 (17.5) | 96 (1.7) | 1547 (41.8) | 858 (19.8) | 785 (15.5) | |||
ACC/AHA: American College of Cardiology/American Heart Association; FRS: Framingham Risk Score; WHO/ISH: World Health Organization/International Society of Hypertension
Fig. 2 indicates that 23.5% of the study population were in the high-risk group according to at least one high CVD risk score; 11.0% were classified as high CVD risk score based on at least two scores, and 4.55% were as high CVD risk base on all three. None of the study population had a high CVD risk score based on only ACC/AHA. However, 5.15% of people were in the high-risk group only by FRS and 7.32% of our study subjects were classified as high risk only by WHO/ISH (Fig. 2).
Fig. 2. Distribution of high estimated 10-year risk of CVD events (≥20%) using three cardiovascular disease risk assessment tools in the PERSIAN Guilan Cohort Study.
Note. ACC/AHA: American College of Cardiology/American Heart Association; FRS: Framingham Risk Score; WHO/ISH: World Health Organization/International Society of Hypertension
Regarding CVD severity risk score, a complete agreement was obtained between the three CVD risk assessment tools (i.e., all CVD risk assessment tools ... with each other) for 5647 (60.1%) participants. As reported in Table 3, the highest agreement was found between ACC/AHA and FRS tools (κw=0.672, complete agreement=82.3%), which is considered substantial. The agreements between ACC/AHA and WHO/ISH tools as well as between FRS and WHO/ISH tools were fair (κw=0.351 and κw=0.357, respectively).
Table 3. Agreement between CVD risk assessment tools in the PERSIAN Guilan Cohort Study
Table 3. Agreement between CVD risk assessment tools in the PERSIAN Guilan Cohort Study
| ACC/AHA | Weighted kappa coefficient | Complete agreement, n (%) | ||||
| Low | Moderate | High | ||||
| FRS | Low | 5424 | 326 | 0 | 0.672 | 7733 (82.3%) |
| Moderate | 328 | 1804 | 5 | |||
| High | 0 | 1006 | 505 | |||
| ACC/AHA | Weighted kappa coefficient | Complete agreement, n (%) | ||||
| Low | Moderate | High | ||||
| WHO/ISH | Low | 5174 | 1406 | 8 | 0.351 | 6228 (66.3%) |
| Moderate | 477 | 621 | 69 | |||
| High | 101 | 1109 | 433 | |||
| FRS | Weighted kappa coefficient | Complete agreement, n (%) | ||||
| Low | Moderate | High | ||||
| WHO/ISH | Low | 5002 | 579 | 169 | 0.357 | 6285 (66.9%) |
| Moderate | 1280 | 333 | 524 | |||
| High | 306 | 255 | 950 | |||
ACC/AHA: American College of Cardiology/American Heart Association; FRS: Framingham Risk Score; WHO/ISH: World Health Organization/International Society of Hypertension
Discussion
This paper reports the distribution of 10-year CVD risk events in a rural and urban population based on a cohort study in north of Iran according to ACC/AHA, FRS, and WHO/ISH tools. Also, we assessed the agreement of these risk assessment tools. According to our results, a large proportion of the study participants without known CVD had CVD risk factors such as DM, HTN, and smoking, which was consistent with the findings reported in national studies [3, 4]. According to a study on non-communicable diseases’ risk factors in Iran, HTN, DM, and smoking were responsible for a considerable number of deaths in 2019 [17]. Dramatically, a modeling study projected about 9.2 million of Iranian people would have DM by 2030 [18].
Our findings revealed a remarkable number of individuals with high and moderate 10-year CVD risk based on all three CVD risk assessment tools. The most frequency of high CVD risk was reported based on WHO/ISH tool followed by FRS and ACC/AHA. WHO risk score found 17.5% of the study population as high risk which was near the result obtained from FRS (16%). However, only 5% of population was high risk base on AHA risk score. Furthermore, we observed WHO risk chart can find most individuals as high CVD risk among those older than 50 years old. However, most people with high 10-year CVD risk in younger was reported by FRS. In parallel with our finding, Ofori et al. reported 15.2% and 16.9% were classified as high CVD risk according to WHO risk score and FRS, respectively [19]. Another study on diabetic patients reported that the frequency of individuals with high 10-year CVD risk using FRS and AHA was 16.31% and 12.39%, respectively [20]. However, more than 26% of a study population of the central part of Iran were classified as high CVD risk based on FRS but only 4.2% of those were high risk based on the WHO chart [21]. Dramatically, our findings indicated more than 23% of study population were at high CVD risk based on at least one risk score.
Hence, implementing prevention strategies seems to be important. The present study reported all people who were deemed as high CVD risk by AHA were classified as high-risk group according to the two other risk scores. Nevertheless, we have a number of people who were classified as high risk only by WHO or FRS. A considerable agreement was reported between ACC/AHA and FRS, while a slight concordance was seen between ACC/AHA and WHO/ISH risk scores as
well as between FRS and WHO/ISH. Parallel with the present study regarding low agreement between WHO chart and other two risk score, a recent study conducted in Iran reported poor agreement between the result of WHO chart and FRS (kappa 0.17) [21]. Further, a large cohort study conducted among Asian population showed FRS stratified CVD risk better than WHO risk chart did [22].
The present study reported the WHO chart can identify the highest number of people with high CVD risk compared to ACC/AHA and FRS. Hence, Iran noncommunicable disease prevention program using WHO chart provide a suitable opportunity for preventive interventions. On the other hand, CVD risk prediction in low resource countries has an important role to identify high-risk people who will benefit from the treatment. Because of limited resources, misclassification of CVD risk for primary and secondary prevention strategies is unacceptable. In spite of some studies recommending WHO risk chart for low-income countries [23], the WHO risk chart was not derived from a real cohort study [24].
In addition, the ACC/AHA risk score was reported to be suitable among Asian population without any treatment for risk factors while overestimating the risk for subjects under treatment [25]. Chia & Et al proposed FRS for CVD risk prediction in primary care among Asian population. However, a large number of study population were under treatment for risk factors which may affect the validity of the result [25]. According to our findings, the WHO risk chart found most people as high-risk group, while the best agreement was observed between FRS and ACC/AHA risk score. On the other hand, FRS was reported to be appropriate for identifying those at high risk for both CHD and CVD in Iranian people according to a population-based prospective cohort [26].
Although we presented only descriptive data on CVD risk stratification by WHO chart, FRS and ACC/AHA risk score and do not have any validation analysis data, present study have some strength deserve mentioning. First, Guilan Cohort Study comprises urban and rural population with various lifestyle factors to be representative of north of Iran. Secondly, all study data were collected and confirmed based on a national cohort study protocol. Further, we studied a large population being higher than similar previous studies in our country such as 3838 individuals in Bozorgmanesh study [26], 2103 participant in Mirzaei study [21], and 3201 individual in Motamed study [27].
Conclusion
The present study highlighted that a considerable number of the population were at moderate and high 10-year risk for CVD. The WHO risk chart stratified most individuals as high CVD risk group followed by FRS and ACC/AHA. A substantial agreement was observed between FRS and ACC/AHA, while a poor agreement was reported between WHO risk chart and FRS as well as between WHO and ACC/AHA.
Acknowledgement
We gratefully acknowledge all participants in the GCS and all staff of the Gastrointestinal and Liver Diseases Research Center along with vice-chancellor for research of Guilan University of medical sciences.
Conflict of interest: None declared.
This paper reports the distribution of 10-year CVD risk events in a rural and urban population based on a cohort study in north of Iran according to ACC/AHA, FRS, and WHO/ISH tools. Also, we assessed the agreement of these risk assessment tools. According to our results, a large proportion of the study participants without known CVD had CVD risk factors such as DM, HTN, and smoking, which was consistent with the findings reported in national studies [3, 4]. According to a study on non-communicable diseases’ risk factors in Iran, HTN, DM, and smoking were responsible for a considerable number of deaths in 2019 [17]. Dramatically, a modeling study projected about 9.2 million of Iranian people would have DM by 2030 [18].
Our findings revealed a remarkable number of individuals with high and moderate 10-year CVD risk based on all three CVD risk assessment tools. The most frequency of high CVD risk was reported based on WHO/ISH tool followed by FRS and ACC/AHA. WHO risk score found 17.5% of the study population as high risk which was near the result obtained from FRS (16%). However, only 5% of population was high risk base on AHA risk score. Furthermore, we observed WHO risk chart can find most individuals as high CVD risk among those older than 50 years old. However, most people with high 10-year CVD risk in younger was reported by FRS. In parallel with our finding, Ofori et al. reported 15.2% and 16.9% were classified as high CVD risk according to WHO risk score and FRS, respectively [19]. Another study on diabetic patients reported that the frequency of individuals with high 10-year CVD risk using FRS and AHA was 16.31% and 12.39%, respectively [20]. However, more than 26% of a study population of the central part of Iran were classified as high CVD risk based on FRS but only 4.2% of those were high risk based on the WHO chart [21]. Dramatically, our findings indicated more than 23% of study population were at high CVD risk based on at least one risk score.
Hence, implementing prevention strategies seems to be important. The present study reported all people who were deemed as high CVD risk by AHA were classified as high-risk group according to the two other risk scores. Nevertheless, we have a number of people who were classified as high risk only by WHO or FRS. A considerable agreement was reported between ACC/AHA and FRS, while a slight concordance was seen between ACC/AHA and WHO/ISH risk scores as
well as between FRS and WHO/ISH. Parallel with the present study regarding low agreement between WHO chart and other two risk score, a recent study conducted in Iran reported poor agreement between the result of WHO chart and FRS (kappa 0.17) [21]. Further, a large cohort study conducted among Asian population showed FRS stratified CVD risk better than WHO risk chart did [22].
The present study reported the WHO chart can identify the highest number of people with high CVD risk compared to ACC/AHA and FRS. Hence, Iran noncommunicable disease prevention program using WHO chart provide a suitable opportunity for preventive interventions. On the other hand, CVD risk prediction in low resource countries has an important role to identify high-risk people who will benefit from the treatment. Because of limited resources, misclassification of CVD risk for primary and secondary prevention strategies is unacceptable. In spite of some studies recommending WHO risk chart for low-income countries [23], the WHO risk chart was not derived from a real cohort study [24].
In addition, the ACC/AHA risk score was reported to be suitable among Asian population without any treatment for risk factors while overestimating the risk for subjects under treatment [25]. Chia & Et al proposed FRS for CVD risk prediction in primary care among Asian population. However, a large number of study population were under treatment for risk factors which may affect the validity of the result [25]. According to our findings, the WHO risk chart found most people as high-risk group, while the best agreement was observed between FRS and ACC/AHA risk score. On the other hand, FRS was reported to be appropriate for identifying those at high risk for both CHD and CVD in Iranian people according to a population-based prospective cohort [26].
Although we presented only descriptive data on CVD risk stratification by WHO chart, FRS and ACC/AHA risk score and do not have any validation analysis data, present study have some strength deserve mentioning. First, Guilan Cohort Study comprises urban and rural population with various lifestyle factors to be representative of north of Iran. Secondly, all study data were collected and confirmed based on a national cohort study protocol. Further, we studied a large population being higher than similar previous studies in our country such as 3838 individuals in Bozorgmanesh study [26], 2103 participant in Mirzaei study [21], and 3201 individual in Motamed study [27].
Conclusion
The present study highlighted that a considerable number of the population were at moderate and high 10-year risk for CVD. The WHO risk chart stratified most individuals as high CVD risk group followed by FRS and ACC/AHA. A substantial agreement was observed between FRS and ACC/AHA, while a poor agreement was reported between WHO risk chart and FRS as well as between WHO and ACC/AHA.
Acknowledgement
We gratefully acknowledge all participants in the GCS and all staff of the Gastrointestinal and Liver Diseases Research Center along with vice-chancellor for research of Guilan University of medical sciences.
Conflict of interest: None declared.
References
1. Tran DT, Palfrey D, Welsh R. The Healthcare Cost Burden in Adults with High Risk for Cardiovascular Disease. Pharmacoecon Open. 2021;5(3):425-35. [DOI] [PMID] [PMCID]
2. Khan SS, Sidney S, Lloyd-Jones DM, Rana JS. National and Global Trends of Cardiovascular Disease Mortality, Morbidity, and Risk. In: Wong ND, Amsterdam EA, Toth PP, eds. ASPC Manual of Preventive Cardiology. Contemporary Cardiology. Berlin, Germany: Springer, Cham; 2021. PP.17-33. [DOI]
3. Malekzadeh R. Health in IranBurden of Diseases and risk factors. Tehran: Islamic Republic of Iran Ministry of Health and Medical Education, Deputy of Research and Technology; 2018 Mar.
4. Rahmani F, Asgari S, Khalili D, Habibi Moeini AS, Tohidi M, Azizi F, Hadaegh F. National trends in cardiovascular health metrics among Iranian adults using results of three cross-sectional STEPwise approaches to surveillance surveys. Sci Rep. 2021;11(1):58. [DOI] [PMID] []
5. Tran DT, Palfrey D, Welsh R. The Healthcare Cost Burden in Adults with High Risk for Cardiovascular Disease. Pharmacoecon Open. 2021;5(3):425-35. [DOI] [PMID] [PMCID]
6. Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012;344:e3318. [DOI] [PMID]
7. Mauricio R, Khera A. Cardiovascular Risk Assessment: From Global Risk Scoring to Risk Enhancing Factors. In: Wong, N.D., Amsterdam, E.A., Toth, P.P. eds. ASPC Manual of Preventive Cardiology. Contemporary Cardiology. Berlin, Germany: Springer, Cham; 2021. [DOI]
8. Garg N, Muduli SK, Kapoor A, Tewari S, Kumar S, Khanna R, et al. Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline-recommended statin uses. Indian Heart J. 2017;69(4):458-63. [DOI] [PMID] [PMCID]
9. Mansour-Ghanaei F, Joukar F, Naghipour MR, Sepanlou SG, Poustchi H, Mojtahedi K, et al. The PERSIAN Guilan Cohort Study (PGCS). Arch Iran Med. 2019;22(1):39-45. [PMID]
10. Poustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar AA, Hekmatdoost A, et al. Prospective Epidemiological Research Studies in Iran (the PERSIAN Cohort Study): Rationale, Objectives, and Design. Am J Epidemiol. 2018;187(4):647-55. [DOI] [PMID] [PMCID]
11. Eghtesad S, Mohammadi Z, Shayanrad A, Faramarzi E, Joukar F, Hamzeh B, et al. The PERSIAN Cohort: Providing the Evidence Needed for Healthcare Reform. Arch Iran Med. 2017;20(11):691-5. [PMID]
12. Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 Pt B):2889-934. [DOI] [PMID]
13. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 Pt B):2935-59. [DOI] [PMID] [PMCID]
14. Mendis S, Lindholm LH, Mancia G, Whitworth J, Alderman M, Lim S, et al. World Health Organization (WHO) and International Society of Hypertension (ISH) risk prediction charts: assessment of cardiovascular risk for prevention and control of cardiovascular disease in low and middle-income countries. J Hypertens. 2007;25(8):1578-82. [DOI] [PMID] [PMCID]
15. D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et AL. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-53. [DOI] [PMID]
16. Collins D, Lee J, Bobrovitz N, Koshiaris C, Ward A, Heneghan C. whoishRisk - an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world. F1000Res. 2016;5:2522. [DOI] [PMID] [PMCID]
17. Azadnajafabad S, Mohammadi E, Aminorroaya A, Fattahi N, Rezaei S, Haghshenas R, et al. Non-communicable diseases' risk factors in Iran; a review of the present status and action plans. J Diabetes Metab Disord. 2021:22:1-9. [DOI] [PMID] [PMCID]
18. Javanbakht M, Mashayekhi A, Baradaran HR, Haghdoost A, Afshin A. Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran: Evidence from Micro-Simulation Markov Model and Bayesian Meta-Analysis. PLoS One. 2015;10(7):e0132505. [DOI] [PMID] [PMCID]
19. Ofori S, Dodiyi-Manuel S, Akpa MR. Comparison of 3 risk estimators to guide initiation of statin therapy for primary prevention of cardiovascular disease. J Clin Lipidol. 2017;11(6):1441-7. [DOI] [PMID]
20. Alaei Faradonbeh N, Nikaeen F, Akbari M, Almasi N, Vakhshoori M. Cardiovascular disease risk prediction among Iranian patients with diabetes mellitus in Isfahan Province, Iran, in 2014, by using Framingham risk score, atherosclerotic cardiovascular disease risk score, and high-sensitive C-reactive protein. ARYA Atheroscler. 2018;14(4):163-8. [DOI] [PMID] [PMCID]
21. Mirzaei M, Mirzaei M. Agreement between Framingham, IraPEN and non-laboratory WHO-EMR risk score calculators for cardiovascular risk prediction in a large Iranian population. J Cardiovasc Thorac Res. 2020;12(1):20-6. [DOI] [PMID] [PMCID]
22. Selvarajah S, Kaur G, Haniff J, Cheong KC, Hiong TG, van der Graaf Y, et al. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int J Cardiol. 2014;176(1):211-8 [DOI] [PMID]
23. Mendis S, Lindholm LH, Anderson SG, Alwan A, Koju R, Onwubere BJ, et al. Total cardiovascular risk approach to improve efficiency of cardiovascular prevention in resource constrain settings. J Clin Epidemiol. 2011;64(12):1451-62. [DOI] [PMID]
24. World Health Organization. Prevention of cardiovascular disease: guidelines for assessment and management of total cardiovascular risk. Geneva, Switzerland: World Health Organization; 2007. [Article]
25. Chia YC, Lim HM, Ching SM. Validation of the pooled cohort risk score in an Asian population - a retrospective cohort study. BMC Cardiovasc Disord. 2014;14:163. [DOI] [PMID] []
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
