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 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 1  |  Issue : 1  |  Page : 11-15

Cardiovascular disease risk prediction among employees registered in staff clinic of a tertiary care institute of northern india using available risk scoring charts


1 Department of Community Medicine, HIMS, Dehradun, Uttarakhand, India
2 Department of Hospital Administration, PGIMER, Chandigarh, India
3 Department of Hospital Administration, GMCH, Chandigarh, India
4 Department of Senior Medical Officer, PGIMER, Chandigarh, India
5 Department of Community Medicine, PGIMER, Chandigarh, India

Date of Submission17-Nov-2020
Date of Acceptance07-Dec-2020
Date of Web Publication31-Dec-2020

Correspondence Address:
Dr. Sudip Bhattacharya
Department of Community Medicine, HIMS, Dehradun, Uttarakhand
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jascp.jascp_2_20

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  Abstract 


Introduction: Noncommunicable diseases (NCD) is responsible for 52% of the demises and 38% of the total disease burden in the South-East Asia Region. Eighty percent of total deaths from NCD occur in poor countries. It is projected that cardiovascular diseases (CVD) will be the major killer in India by 2020. Methodology: This cross-sectional study will be carried out in staff clinic for one year in PGIMER, India. A scoring for risk prediction of CVD mortality in next ten years will be calculated by the tools to be tested (WHO CVD Risk Prediction Chart, QRISK2-2017 and by Framingham point scores). We will use simple random sampling using a sample size of 400. Results: During statistical analysis, proportions will be calculated for nominal data, and continuous data were given as mean and standard deviation, while categorical variables were compared using the Chi-square test for difference of proportion. Kappa statistics will be used to measure inter-rater reliability. All analyses will be two-tailed, and P < 0.05 was considered as statistically significant. Discussion: Those patients who will fall under the high-risk scores, counselling (food behavior change, lifestyle modification) will be given in the staff clinic OPD. It will be extremely helpful to the staffs according the risk score they can modify their lifestyle through individualized tailor-made counselling. There may be reduction in mortality among the staff members, and disease burden on staff clinic may be reduced. We can also inculcate health promoting behavior within the hospital setting. It will also increase job satisfaction; improved administration- employee relations; and they will perform better.

Keywords: Health promotion, noncommunicable disease, risk prediction


How to cite this article:
Bhattacharya S, Kumar A, Mehra A, Sandhu AS, Singh A. Cardiovascular disease risk prediction among employees registered in staff clinic of a tertiary care institute of northern india using available risk scoring charts. J Appl Sci Clin Pract 2020;1:11-5

How to cite this URL:
Bhattacharya S, Kumar A, Mehra A, Sandhu AS, Singh A. Cardiovascular disease risk prediction among employees registered in staff clinic of a tertiary care institute of northern india using available risk scoring charts. J Appl Sci Clin Pract [serial online] 2020 [cited 2021 Jan 25];1:11-5. Available from: http://www.jascp.com/text.asp?2020/1/1/11/306101




  Introduction Top


Noncommunicable diseases (NCD) are increasing at a rapid pace with serious concern for the world including developing countries. It is responsible for 52% of the demises and 38% of the total disease burden in the South-East Asia Region. Eighty percent of total deaths from NCD occur in poor countries. It is projected that cardiovascular diseases (CVD) will be the major killer in India by 2020.[1] A variety of approaches have been tried in various parts of the world to tackle NCD. Many large community-based interventions programs have been successfully implemented. However, in India such efforts have been lacking.[2] It is documented in 2008 that total 57 million deaths occurred in the world, among them 36 million (63%) were due to NCDs. Majority is due to cardiovascular diseases, diabetes, cancer and chronic respiratory diseases. Nearly 80% of these NCD deaths (29 million) occurred in poor countries.[3] Though developed countries like America, the Eastern Mediterranean, Europe, and the Western Pacific are also affected by NCDs, badly. Though reverse scenario is observed in the African Region, there are still higher number deaths are occurring from infectious diseases rather than NCDs. However, the prevalence of NCDs is rising in a rapid pace. It is anticipated from various studies that by 2020 the burden of NCDs will exceed the infectious disease. It is projected that by 2030, NCDs will emerge as major killer disease among Africa also.[1] WHO projections are also telling the similar story (increase burden of NCDs in the next decade). NCD deaths are expected to increase by 15% worldwide between 2010 and 2020.[4]

It is documented in Global Burden of Disease study age-standardized estimates (2010), nearly a quarter (24.8%) of all deaths in India are attributable to CVD. Even the age-standardized CVD death rate in India is higher (272 per 100 000 population) than the global average (235 per 100 000 population).

One interesting fact is that regarding the NCDs that they share some common risk factors. They are biological (ethnicity, genetic etc.), behavioral (smoking, alcohol etc.) and metabolic (metabolic disorder). Among all risk factors behavioral risk factors, including tobacco use, physical inactivity, and unhealthy diet, are responsible for about 80% of coronary heart disease and cerebrovascular disease. But interesting fact is that major deaths from NCDs are preventable.[5]

The role of epidemiological research for NCD prevention is enormous. It can prevent, even reverse the disease trend of NCDs by timely identifying the major risk factors, good screening programme, correct diagnosis, and appropriate interventions. Conventional epidemiologic studies measure the impact of a risk factor on a certain disease by calculating relative risk and odds ratios. However, these measures of risk are difficult to interpret at an individual level. This is because, an individual may have a disease without any exposure to risk factors or vice versa. Hence, in decision-making process about the clinical interventions, absolute disease risk measurement is important for everyone. Multiple disease risk prediction (or health risk appraisal) models have been developed. These risk prediction models are constructed to assess the impact of multiple risk factors together for the estimation of an individual's absolute disease risk. The development of risk prediction models/tools is an interesting as well as complicated area of modern epidemiological research. This is due to gathered epidemiologic findings are usually translated into clinical applications. If the future risk of an individual for specific diseases can be predicted with accuracy, then efficient and tailormade treatment strategies can be chosen. There are many risk prediction tools (The Framingham Risk Score, QRISK2, The WHO/ISH risk prediction charts etc.) are available for predicting CVD, same holds true for other diseases. However, their prediction accuracy and clinical utility vary widely [Annexure 1] and [Annexure 2].[6]

Amidst various tools/models “The Framingham Risk Score” is the most well-known example of a health risk assessment model to assesses the cardiovascular disease (CVD) risk an individuals level and it also suggests lifestyle changes and/or early pharmacologic treatment for individuals.[7]

QRISK2 is also a cardiovascular disease (CVD) risk prediction tool. It uses traditional risk factors (age, systolic blood pressure, smoking status and ratio of total serum cholesterol to high-density lipoprotein cholesterol) together with body mass index, ethnicity, measures of deprivation, family history, chronic kidney disease, rheumatoid arthritis, atrial fibrillation, diabetes mellitus, and antihypertensive treatment.[8]

The WHO/ISH risk prediction charts indicate 10-year risk of a fatal or nonfatal major cardiovascular event (myocardial infarction or stroke). The factors like- age, sex, blood pressure, smoking status, total blood cholesterol and presence or absence of diabetes mellitus has been incorporated for 14 WHO epidemiological sub-regions. There are two sets of charts. One set (14 charts) can be used in settings where blood cholesterol can be measured another one where blood cholesterol cannot be measured.[9] The present study has therefore been planned with the following objectives- 1. To ascertain the CVD risk factors for predicting the 10-year risk of stroke or myocardial infarction (MI) among the employees registered in staff clinic (residents, staff nurse, office staff, technicians, and others) of PGIMER, Chandigarh, India. 2. To cross-validate the scoring system of the three standard CVD risk prediction charts (QRISK2--2017, WHO/ISH, NIH Framingham risk score) among these staffs.

Rationale

In this context, Ottawa Charter on Health Promotion advocated a settings-based approach.[10],[11],[12],[13],[14] In line with the concept of 'Charity Begins at Home', in Post Graduate Institute of Medical Education and Research (PGIMER), our hospital can be taken up as a setting for implementing CVD related interventions program. This includes conducting risk factor surveillance for its employees registered in staff clinic as one of its components. Tertiary care hospitals with their huge resources and infrastructure are ideal for initiating preventive and promotional activities, for which baseline data on prevalence of CVD risk factors is essential & is available.


  Methodology Top


This cross-sectional study will be carried out in staff clinic of PGIMER, India. Our study duration will be of one year. A scoring for risk prediction of CVD mortality in next ten years will be calculated by the tools to be tested (WHO CVD Risk Prediction Chart, QRISK2-2017 and by Framingham point scores). We will use simple random sampling. It is calculated with Epi-info software (stat cal).

Our total population is 26000, with a prevalence of 14%, confidence limit 5, design effect of 1, and confidence level of 99%, our sample size came 316. However, we will consider 400 samples to compensate the missing value.

Data collection

It will be organized as a part of health promotion in hospital setting. Four hundred staff who will opt in for the screening program will be included in the study. Sociodemographic information including age, sex, and occupation was collected. Diabetes mellitus, hypertension status, and smoking and alcohol history will be taken. Blood pressure and random blood sugar measurements will be recorded for each participant. It will also calculate the 10-year risk of fatal or nonfatal major cardiovascular events according to age, gender, blood pressure, smoking status, and presence or absence of diabetes.

Weight and height will be measured in the OPD with stadiometer and weight machine. Blood pressure will be measured using the mercury sphygmomanometer. History of rheumatoid arthritis, chronic kidney disease, rhythm abnormality and smoking will be obtained. As the screening program will be carried out during working hours. We will measure random blood sugar using a glucometer. Blood for lipid profile will also be tested. All newly detected high risk subjects will recalled and counselled in the staff clinic by making telephonic contact.

Patients reporting to the staff clinic will also be enrolled in the study until sample size is obtained.

CVD risk factor profiling of the staff will be done using WHO–International Society of Hypertension (ISH) risk prediction chart along with other two charts (described earlier) will be used see the comparative analysis of the prediction results. Our inclusion criteria will be 1. Staff with Age >40 and <60 years and he/she should be of a beneficiary of the staff clinic. We will exclude the staffs who are aged <40 years >60 years, not a present beneficiary of staff clinic, and staffs who are seriously ill. Ethical clearance has been obtained from Institute Ethical Committee, PGIMER, Chandigarh. Informed written consent will be obtained from each participant.


  Results and Discussion Top


The statistical package SPSS (version 22) developed by IBM (International Business Machines) will be used for analysis, proportions will be calculated for nominal data, and continuous data were given as mean and standard deviation, while categorical variables were compared using the Chi-square test for difference of proportion. Kappa statistics will be used to measure inter-rater reliability. All analyses will be two-tailed, and P < 0.05 was considered as statistically significant.

If the patients are fall under the high-risk scores, counselling (food behavior change, lifestyle modification) will be given in the staff clinic OPD. It will be extremely helpful to the populations according the risk score they can modify their lifestyle through individualized tailor-made counselling. There may be reduction in mortality, reduced burden on staff clinic in the form of resources. We can also inculcate health promoting behavior within the hospital setting. It will also increase job satisfaction; improved administration- employee relations; better performance.

Limitations of our study

Risk prediction tools like The Framingham Risk Score, QRISK2, The WHO/ISH risk prediction charts and many more are available for predicting CVD, same holds true for other diseases. However, their prediction accuracy and clinical utility vary widely because each tool has its own limitations.

Despite advances in research and medical care, NCD remains as diseases with high mortality.

Other factors that may influence this risk prediction include age of the staff, lifestyle, socioeconomic status, as well as comorbidity. To address these limitations, we will collect information about sociodemographic variables and analyse them using appropriate methodologies.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.


  Annexures Top








 
  References Top

1.
Organisation Mondiale de la Santé, Alwan AA, Agis T. Global Status Report on Noncommunicable Diseases 2010. Geneva: World Health Organization; 2011.  Back to cited text no. 1
    
2.
Nissinen A, Berrios X, Puska P. Community-based noncommunicable disease interventions: Lessons from developed countries for developing ones. Bull World Health Organ 2001;79:963-70.  Back to cited text no. 2
    
3.
Fuster V, Kelly BB. Developing Countries I of M (US) C on P the GE of CDM the C. In: Epidemiology of Cardiovascular Disease. National Academies Press (US); 2010. Available from: https://www.ncbi.nlm.nih.gov/books/NBK45688/. [Last accessed on 2017 Jul 27].  Back to cited text no. 3
    
4.
Mathers CD, Loncar D. Projections of Global Mortality and Burden of Disease from 2002 to 2030. PLoS Med 2006;3:e442.  Back to cited text no. 4
    
5.
Prabhakaran D, Jeemon P, Roy A. Cardiovascular diseases in India: Current epidemiology and future directions. Circulation 2016;133:1605-20.  Back to cited text no. 5
    
6.
Oh SM, Stefani KM, Kim HC. Development and application of chronic disease risk prediction models. Yonsei Med J 2014;55:853-60.  Back to cited text no. 6
    
7.
D'Agostino RB, Pencina MJ, Massaro JM, Coady S. Cardiovascular disease risk assessment: Insights from Framingham. Glob Heart 2013;8:11-23.  Back to cited text no. 7
    
8.
Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: Independent and external validation of an updated version of QRISK2. BMJ 2012;344:e4181.  Back to cited text no. 8
    
9.
WHO | WHO/ISH Cardiovascular Risk Prediction Charts. WHO. Available from: http://www.who.int/cardiovascular_diseases/guidelines/Chart_predictions/en/. [Last accessed on 2017 Jul 27].  Back to cited text no. 9
    
10.
Mcqueen DV, De Salazar L. Health promotion, the Ottawa Charter and developing personal skills: A compact history of 25 years. Health Promot Int 2011;26 Suppl 2:ii194i-201.  Back to cited text no. 10
    
11.
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 2017;187:647-55.  Back to cited text no. 11
    
12.
Nakai M, Watanabe M, Kokubo Y, Nishimura K, Higashiyama A, Takegami M, et al. Development of a cardiovascular disease risk prediction model using the Suita study, a population-based prospective cohort study in Japan. J Atheroscler Thromb 2020;27:1160-75.  Back to cited text no. 12
    
13.
Jonas DE, Reddy S, Middleton JC, Barclay C, Green J, Baker C, et al. Screening for cardiovascular disease risk with electrocardiography: An evidence review for the U.S. preventive services task force. JAMA 2018;319:2315-28.  Back to cited text no. 13
    
14.
LeBlanc ES, Patnode CD, Webber EM, Redmond N, Rushkin M, O'Connor EA. Behavioral and Pharmacotherapy Weight Loss Interventions to Prevent Obesity-Related Morbidity and Mortality in Adults: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA. 2018;320:1172-1191. doi: 10.1001/jama.2018.7777. PMID: 30326501.  Back to cited text no. 14
    




 

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