Home Print this page Email this page Small font size Default font size Increase font size
Users Online: 1297
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents 
ORIGINAL ARTICLE
Year : 2020  |  Volume : 9  |  Issue : 8  |  Page : 4145-4150  

Cardiovascular risk using WHO-ISH chart among Diabetes and Hypertensive patients in a remote rural area of South India


1 Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India
2 Department of Cardiology, Pondicherry Institute of Medical Sciences, Puducherry, India
3 Department of Biochemistry, Pondicherry Institute of Medical Sciences, Puducherry, India

Date of Submission04-Apr-2020
Date of Decision26-Apr-2020
Date of Acceptance11-May-2020
Date of Web Publication25-Aug-2020

Correspondence Address:
David Gregg Smith Ponraj
Department of Community Medicine, Pondicherry Institute of Medical Sciences, Pondicherry - 605014
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jfmpc.jfmpc_538_20

Rights and Permissions
  Abstract 


Introduction: Cardiovascular diseases (CVDs) are major problems in India and many other developing and developed countries. As India is committed to provide universal health care for the population, there is a need to find out the prevalence and determinants of CVD risk among high-risk individuals (Diabetes and Hypertensive patients) in the remote rural area of India to deliver appropriate services, as they are considered as neglected population. Methods: We screened high-risk individuals (Hypertension and Diabetes patients) for CVD risk using WHO/ISH chart, in a remote rural area of south India, covering ten villages surrounding the Rural Health Training Centre (RHTC), in August–September 2017. After line-listing the participants from the electronic database of RHTC, screening with questionnaire and biochemical tests was done at village level as the first step. Thereafter, the participants were invited to the hospital on a particular day where electrocardiography (ECG) and echocardiography (ECHO) were done with special consultation. Results: Among the total of 303 individuals screened at the village level, 64 [21%(CI 17–25)] had a higher risk for CVD. 235 people attended the special consultation; among them, 212 underwent ECG and 88 underwent ECHO. Among those screened with ECHO, 18 had some cardiac pathologies. The relationship between CVD risk and other factors is shown in. After final adjustment, illiteracy [adjusted prevalence ratio (aPR) 1.8 (0.1–3.1)], anemia [aPR 1.8 (1–3.6)], and chronic renal diseases [aPR 1.8 (1.0–3.4)] were found to be associated with high risk for CVD among hypertension and diabetes groups. Conclusion: Cardiovascular disease risk assessment using WHO/ISH chart showed an association with poor education, anemia, and chronic kidney disease.

Keywords: Cardiovascular, India, ISH chart, rural


How to cite this article:
Ponraj DG, Gopikrishnan SK, Newtonraj A, Arokiaraj MC, Purty AJ, Nanda SK, Manikandan M, Vincent A. Cardiovascular risk using WHO-ISH chart among Diabetes and Hypertensive patients in a remote rural area of South India. J Family Med Prim Care 2020;9:4145-50

How to cite this URL:
Ponraj DG, Gopikrishnan SK, Newtonraj A, Arokiaraj MC, Purty AJ, Nanda SK, Manikandan M, Vincent A. Cardiovascular risk using WHO-ISH chart among Diabetes and Hypertensive patients in a remote rural area of South India. J Family Med Prim Care [serial online] 2020 [cited 2020 Sep 24];9:4145-50. Available from: http://www.jfmpc.com/text.asp?2020/9/8/4145/293047




  Introduction Top


Cardiovascular diseases (CVDs) are the leading cause of mortality at a global level as well as in India.[1] Like many other low and middle income countries (LMICs), India is also in a major epidemiological transition, where the disease burden is transforming from communicable to noncommunicable diseases.[2] Considering the burden of CVD and the huge population in India, task shifting of CVD risk assessment and communication to grass root level workers like Nurses are emerging in rural areas of India.[3] In case of risk behavior, a recent study from rural north India has shown that people with low socioeconomic position were having higher risk behavior for CVDs.[4] Another concern is even though Indians have good knowledge and attitude on CVD, they have poor practices in adopting healthy lifestyles.[5] Recent days, modern technologies like smartphones are coming to play in monitoring treatment adherence and educating the public in risk reduction.[6]

Mean time Indian health system has also taken some earnest steps to prevent CVDs. The important concern in India is providing universal healthcare to the population.[7] In this, the major challenge is reaching the rural population. In India, more than 70% of the people live in rural areas with limited access to the higher health facilities.[8] Some studies have shown that even though the rural community has a higher prevalence of cardiovascular risk, in general, they are neglected and the health care delivery system at this level is comparatively poor.[9] With this background, the government has recently taken some notable steps to provide affordable healthcare to all in need through various commitments. In National Health Policy 2017, government has committed to reverse the growing incidence of noncommunicable diseases (NCDs).[7] Through sustainable development goals, the government has committed to reduce the premature mortality due to NCDs by one-third by 2030 using various modalities of NCDs prevention and treatment.[10],[11] To support these initiatives, at present in India, NPCDCS (National Programme for Prevention and control of Cancer Diabetes Cardiovascular Diseases and Stroke) is the national health program in place at every level of health care.[12] Under NPCDCS, a particular day in a week is designated as a NCD clinic day and the patients with common NCDs visit the health center on this day for treatment and follow-up.[12],[13],[14] As per this program, front line workers and Medical Officers are supposed to assess the CVD risk among the diabetes and hypertensive patients once in year using World Health Organization/International Society of Hypertension (WHO/ISH) chart and appropriate management should be carried out. This WHO/ISH Chart is one of the most widely used and extensively studied tools to predict the cardiovascular disease for the subsequent 10 years of an individual.[15],[16],[17],[18] Charts with cholesterol predict the risk better than charts without cholesterol.[17] Even though this has been incorporated in the program, only very few studies are available on this topic, particularly in a remote rural population. Hence, we targeted the high-risk people (DM and/or HT) in a rural community in south India and assessed their risk for CVD using WHO/ISH chart and further screened the high-risk patients with electrocardiography (ECG) and echocardiography (ECHO).


  Methodology Top


This study was done in a Rural Health Training Centre (RHTC) which is functioning as per the Medical Council of India directions for teaching and training activities of MBBS students and Interns. This RHTC covers 10 villages around the health center. From the electronic database called CHIMS (Community Health Information Management System) which maintains the health-related details of these individuals belong to the ten villages, patients with diabetes and hypertension were line-listed, approached, and invited to participate in the study.[19],[20],[21] after getting consent, data was collected in the concerned villages of the participants by setting up a camp with a pretested proforma. Plasma and serum were collected at fasting state and transferred to the main campus situated 30 km away, which has the NABL accredited labs (National Accreditation for Testing and Calibration Laboratories). A special consultation was offered to all those participated in the initial screening, on the day of World Heart Day (29th September 2017) at the RHTC. All those who attended were further screened with ECG and then with ECHO on that day and Cardiologist, Diabetologist, Ophthalmologist, and Neurologist consultations were provided. All the investigations, consultations, and medications were provided free of cost as a service to the rural community. A team of eight interns, three Medical Social Workers (MSWs), and one lab technician were trained for 2 days and the data were collected under the supervision of two Assistant Professors from Department of Community Medicine qualified as MD in Community Medicine. Data entry and analysis: Data was double entered using Epidata software V.3.1 and analyzed using SPSS Version 22.0. Prevalence ratio (PR) and adjusted prevalence ratio (aPR) were calculated using STATA 14.[22],[23] aPR was calculated for independent variables with significant P value < 0.2. WHO/ISH chart uses sex, age, smoking status, blood pressure, and cholesterol as a tool to predict the next 10-year risk of CVDs and these variables and the variables related to cholesterol were omitted for final aPR analysis. A detailed description of screening procedures is described elsewhere.[24]

Operational Definitions: High risk for CVD—risk ≥20% using WHO ISH chart.[25] Abdominal obesity: waist circumference of ≥90 cm for men and ≥80 cm for women. Normal waist-hip ratio:<0.85 for women and <0.95 for men.[12] High salt intake per day: per capita intake of >5 g. Current smoker: smoking of any smoke form of tobacco products in the last 1 year. Current alcoholic: consumed alcohol at least once in the last 1 year.[26] Dyslipidemia: Defined based on National Cholesterol Education Programme (NCEP) guidelines [27],[28] where Hypercholesterolemia as serum cholesterol levels ≥200 mg/dl (≥5.2 mmol/l), Hypertriglyceridemia as serum triglyceride levels ≥150 mg/dl (≥1.7 mmol/l), Low HDL cholesterol as HDL cholesterol levels <40 mg/dl (<1.04 mmol/l) for men and <50 mg/dl (<1.3 mmol/l) for women, High LDL cholesterol as LDL cholesterol levels ≥130 mg/dl (≥3.4 mmol/l), Isolated hypercholesterolemia as serum cholesterol ≥200 mg/dl and triglycerides <150 mg/dl, Isolated hypertriglyceridemia as serum triglycerides ≥150 mg/dl and cholesterol <200 mg/dl, Isolated low HDL-C as HDL-C ≤40 mg/dl (male) and ≤50 mg/dl (female) without hypertriglyceridemia or hypercholesterolemia, Metabolic syndrome based on International Diabetes Federation Global Consensus Definition: central obesity (waist circumference ≥90 cm for men and ≥80 cm for women) with two or more of the following four criteria: (i) Triglycerides 150 mg/dl or greater, (ii) HDL-cholesterol <40 mg/dl in men and <50 mg/dl in women, (iii) BP 130/85 mmHg or greater, and (iv) fasting glucose 100 mg/dl or greater.[29],[30] Estimated glomerular filtration rate (eGFR) was calculated using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and the participants having eGFR ≤60 ml/min/1.73 m 2 were classified as CKD.[31] Based on American Thyroid Association and American Association of Clinical Endocrinologists (ATA/AACE) guideline, hypothyroidism was classified as TSH (Thyroid Stimulating Hormone) level (TSH>=10 - Overt hypothyroidism, 4.5–9.0 - highly abnormal TSH, 2.5–4.4 - intermediate abnormal, and <2.5 as normal).[32] Anemia: hemoglobin <13 mg/dl for men and <12 mg/dl for women (WHO criteria).[33]


  Results Top


Among the total 303 screened at the village level, 64 [21% (CI 17–25)] were identified as having a high risk for CVD. Relationship between CVD risk and other factors is shown in [Table 1]. After final adjustment, illiteracy [aPR 1.8 (0.1–3.1)], anemia [aPR 1.8 (1.0–3.6)], CKD [aPR 1.8 (1.0–3.4)] were found to have associated with high risk for CVD among DM and HT individuals. Among the total of 303, 235 people attended the special consultation and 212 underwent ECG and 88 underwent ECHO, where 18 of them diagnosed to have some cardiac pathologies [Table 2].
Table 1: Association between WHO/ ISH cardiovascular risk and other factors in a remote rural area of south India, in August and September 2017

Click here to view
Table 2: Echocardiographic finding at the end of screening after risk assessment using WHO/ ISH chart in a remote rural area of South India in August and September 2017.

Click here to view



  Discussion Top


Important findings in our study were that first, the CVD risk was found to be two times higher among this high-risk group when compared to the general population.[17] Second, the CVD risk among the high-risk group was significantly associated with illiteracy, anemia, and CKD.

Association of CVD with anemia is a well-reported fact and one of the reviews done by Kaiafa et al. has found that there is a positive correlation between anemia and CVDs and the pathophysiology might be due to a complex interaction of iron deficiency, cytokine production, and impaired renal function.[34] Another review done by Zalunardo and Levin found that anemia contributes to cardiovascular disease in CKD patients.[35] Like our study where illiterate people seem to be at a higher risk for cardiovascular diseases, other studies also demonstrated people with lower educational status have a higher risk for CVD.[36],[37] Finally, our study also shows that people with chronic kidney disease (CKD) are at a higher risk to get cardiovascular disease. According to a study done by Ballew and Matsushita, CKD management measures such as estimated GFR and albuminuria improve cardiovascular risk prediction beyond traditional risk factors.[38] The study done by Ma et al. postulates that arterial stiffness (AS) is associated with CVD and CKD. It also indicates that arterial stiffness may contribute to the development of CVD in CKD patients suggesting that the pathophysiology indicates a mechanism linking CVD to CKD.[39] A study done by Liu et al. suggests that fibroblast–23, a bone-derived hormone, could be the missing link between CVD and CKD.[40]

Ours is one of the very few studies done in the rural area of south India, especially among the high-risk individuals for cardiovascular disease. Our study used the standard WHO/ISH chart to predict the CVD risk as recommended by National Guideline. Including cholesterol as one of the indicators strengthened our evidence. Moreover, our study represents the population attributes as we collected the sample having population as base, not hospital based. This study adds more evidence to the association between CVD risk and anemia, poor education, and CKD. This study provides valuable information on promoting screening in the rural areas which have poor accessibility to health care services and also stresses on the importance of strengthening Primary Health Care in the remote rural areas. This study will help the primary care physicians to have an insight on early prediction of CVD risk in rural area. After performing the initial clinical and biochemical screening, ECG and ECHO screenings were performed and consultation was obtained. By targeting the high-risk individuals, we were able to diagnose more people with CVD with a limited resource [Table 2].

This study has few limitations: first, we have assessed CVD risk among the high-risk individual (DM and/or HT patients) only. Screening was done in a population surrounding a particular healthcare setting; hence, generalization should be done cautiously. Providing ECHO and ECG services incurred high cost, which may not be replicable in a resource-constrained setting even though it is fruitful. Finally, only those who showed some changes in the ECG underwent ECHO.

Key message: It is worthwhile to do CVD risk assessment using WHO/ISH chart in clinical and community practice.


  Conclusion Top


Cardiovascular disease risk assessment using WHO/ISH chart showed an association with poor education, anemia, and chronic kidney disease.

Ethics Committee approval was obtained from PIMS ethical committee (Reference Number - RC-18/55).

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form, the patients have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
World Health Organization. Non Communicable Diseases country Profile 2018 [Internet]. 2018 [cited 2019 Aug 02]. Available from: https://www.who.int/nmh/publicatio ns/ncd-profiles-2018/en/.  Back to cited text no. 1
    
2.
Fowkes FGR, Aboyans V, Fowkes FJI, McDermott MM, Sampson UKA, Criqui MH. Peripheral artery disease: Epidemiology and global perspectives. Vol. 14, Nature Reviews Cardiology. Nature Publishing Group; 2017. p. 156-70.  Back to cited text no. 2
    
3.
Kavita, Thakur JS, Vijayvergiya R, Ghai S. Task shifting of cardiovascular risk assessment and communication by nurses for primary and secondary prevention of cardiovascular diseases in a tertiary health care setting of Northern India. BMC Health Serv Res 2020;20:10.  Back to cited text no. 3
    
4.
Agarwal A, Jindal D, Ajay VS, Kondal D, Mandal S, Ghosh S, et al. Association between socioeconomic position and cardiovascular disease risk factors in rural north India: The SOLAN surveillance study. PLoS One 2019;14:e0217834.  Back to cited text no. 4
    
5.
Verma A, Mehta S, Mehta A, Patyal A. Knowledge, attitude and practices toward health behavior and cardiovascular disease risk factors among the patients of metabolic syndrome in a teaching hospitalin India. J Fam Med Prim Care 2019;8:178.  Back to cited text no. 5
    
6.
Peiris D, Praveen D, Mogulluru K, Ameer MA, Raghu A, Li Q, et al. Smarthealth India: A stepped-wedge, cluster randomised controlled trial of a community health worker managed mobile health intervention for people assessed at high cardiovascular disease risk in rural India. PLoS One 2019;14:e0213708.  Back to cited text no. 6
    
7.
Ministry of Helath and Family Welfare-Government of India. National Health Policy 2017 [Internet]. 2018 [cited 2019 Dec 21]. Available from: https://mohfw.gov.in/sites/defa ult/files/9147562941489753121.pdf.  Back to cited text no. 7
    
8.
Office of the registrar general and census commissioner I. Census Info India 2011 [Internet]. [cited 2018 Oct 31]. Available from: http://censusindia.gov.in/.  Back to cited text no. 8
    
9.
Swaminathan K, Veerasekar G, Kuppusamy S, Sundaresan M, Velmurugan G, Palaniswami N. Noncommunicable disease in rural India: Are we seriously underestimating the risk? the Nallampatti noncommunicable disease study. Indian J Endocrinol Metab 2017;21:90-5.  Back to cited text no. 9
    
10.
The United Nations Development Programme. sustainable Development goals [Internet]. 2015 [cited 2020 Mar 11]; Available from: https://www.undp.org/content/dam/undp/library/corporate/brochure/SDGs_Booklet_Web_En.pdf.  Back to cited text no. 10
    
11.
Organization WH. World health statistics 2016: Monitoring health for the SDGs sustainable development goals. World Health Organization; 2016.  Back to cited text no. 11
    
12.
Ministry of Helath and Family Welfare G of I. National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke. New Delhi; 2017.  Back to cited text no. 12
    
13.
Sunkara N, Ahsan HC. Hypertension in diabetes and the risk of cardiovascular disease. Vol. 6, Cardiovascular Endocrinology. Lippincott Williams and Wilkins; 2017. p. 33-8.  Back to cited text no. 13
    
14.
Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Vol. 34, Canadian Journal of Cardiology. Elsevier Inc.; 2018. p. 575-84.  Back to cited text no. 14
    
15.
Raghu A, Praveen D, Peiris D, Tarassenko L, Clifford G. Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India. Mukhopadhyay P, editor. PLoS One [Internet]. 2015 Aug 19 [cited 2018 Aug 2];10:e0133618. Available from: http://dx.plos.org/10.1371/journal.pone.0133618.  Back to cited text no. 15
    
16.
Rajanandh MG, Suresh S, Manobala K, Nandhakumar R, Jaswanthi G, Neha S. Prediction of cardiovascular risk in cancer patients of South India using WHO/ISH risk prediction charts and Framingham score-A prospective study. J Oncol Pharm Pract [Internet]. 2018 Jul 28 [cited 2018 Aug 2];24:354-8.  Back to cited text no. 16
    
17.
Ghorpade AG, Shrivastava SR, Kar SS, Sarkar S, Majgi SM, Roy G. Estimation of the cardiovascular risk using World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts in a rural population of South India. Int J Heal Policy Manag [Internet]. 2015 Apr 21 [cited 2019 Aug 24];4:531-6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26340393.  Back to cited text no. 17
    
18.
Singh RB, Beegom R, Ghosh S, Niaz MA, Rastogi V, Rastogi SS, et al. Epidemiological study of hypertension and its determinants in an urban population of North India. J Hum Hypertens [Internet]. 1997 Oct [cited 2018 Aug 2];11:679-85. Available from: http://www.ncbi.nlm.nih.gov/pubmed/9400911.  Back to cited text no. 18
    
19.
Newtonraj A, Arun S, Bazroy J, Tovia S. Lay perspectives on causes and complications of hypertension; and barrier to access health care by known hypertensive patients: A qualitative study from a rural area of South India. Int J Community Med Public Health 2017;4:704-7.  Back to cited text no. 19
    
20.
Vincent A, Keerthana K, Damotharan K, Newtonraj A, Bazroy J, Manikandan M. Health care seeking behaviour of women during pregnancy in rural south India: A qualitative study. Int J Community Med Public Health 2017;4:3636-9.  Back to cited text no. 20
    
21.
Newtonraj A, Vincent A, Gowtham PJ, Haritha S, Ilaveyini S. Level of insufficient physical activity among adults in a rural area of South India: A population-based cross-sectional study. J Curr Res Sci Med 2019;5:105.  Back to cited text no. 21
  [Full text]  
22.
Epidata Association. Epidata Software [Internet]. 2014 [cited 2019 Aug 08]. Available from: https://www.epidata.dk/about.htm#about.  Back to cited text no. 22
    
23.
Stata Corp LLC. Stata data analysis and statistical Software [Internet]. [cited 2020 Mar 10];Available from: https://www.stata.com/company/.  Back to cited text no. 23
    
24.
Newtonraj A, Selvaraj K, Purty AJ, Nanda SK, Arokiaraj MC, Vincent A, et al. Feasibility and outcome of community-based screening for cardiovascular disease risk factors in a remote rural area of South India: The Chunampet rural–Cardiovascular health assessment and management program. Indian J Endocrinol Metab 2019;23:628.  Back to cited text no. 24
    
25.
World Health Organization. WHO/ISH Cardiovascular Diseases Risk Prediction Charts [Internet]. 2007 [cited 2020 Mar 17]. Available from: https://www.who.int/ncds/manage ment/WHO_ISH_Risk_Prediction_Charts.pdf?ua=1.  Back to cited text no. 25
    
26.
World Health Organization. The STEPS Instrument and Support Material [Internet]. 2016 [cited 2019 Jun 25]. Available from: http://www.who.int/chp/steps/instrument/en/.  Back to cited text no. 26
    
27.
Joshi SR, Anjana RM, Deepa M, Pradeepa R, Bhansali A, Dhandania VK, et al. Prevalence of dyslipidemia in urban and rural India: The ICMR–INDIAB study. PLoS One 2014;9:e96808.  Back to cited text no. 27
    
28.
Expert Panel on Detection and Treatment of High Blood Cholesterol in Adults E. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA [Internet] 2001;285:2486–97. Available from: https://doi.org/10.1001/jama.285.19.2486.  Back to cited text no. 28
    
29.
International Diabetes Federation. The IDF consensus worldwide definition of Metabolic Syndrome [Internet]. 2006 [cited 2019 Nov 12]. Available from: https://www.idf.org/component/attachments/attachments.html?id=705 &task=download.  Back to cited text no. 29
    
30.
Parikh R, Mohan V. Changing definitions of metabolic syndrome. Indian J Endocrinol Metab 2012;16:7.  Back to cited text no. 30
    
31.
Levey AS, Stevens LA, Schmid CH, Zhang Y (Lucy), Castro AF, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate. Ann Intern Med [Internet]. 2009;150:604. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19414839  Back to cited text no. 31
    
32.
Garber JR, Cobin RH, Gharib H, Hennessey JV, Klein I, Mechanick JI, et al. Clinical practice guidelines for hypothyroidism in adults: Cosponsored by the American Association of Clinical Endocrinologists and the American Thyroid Association. Thyroid 2012;22:1200-35.  Back to cited text no. 32
    
33.
Organization WH. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. World Health Organization; 2011.  Back to cited text no. 33
    
34.
Kaiafa G, Kanellos I, Savopoulos C, Kakaletsis N, Giannakoulas G, Hatzitolios AI. Is anemia a new cardiovascular risk factor? Int J Cardiol 2015;186:117-24.  Back to cited text no. 34
    
35.
Zalunardo N, Levin A. Anemia and the heart in chronic kidney disease. Semin Nephrol 2006;26:290-5.  Back to cited text no. 35
    
36.
Panagiotakos DB, Pitsavos CE, Chrysohoou CA, Skoumas J, Toutouza M, Belegrinos D, et al. The association between educational status and risk factors related to cardiovascular disease in healthy individuals: The ATTICA study. Ann Epidemiol 2004;14:188-94.  Back to cited text no. 36
    
37.
Fraser SDS, Roderick PJ, McIntyre NJ, Harris S, McIntyre CW, Fluck RJ, et al. Socio-economic disparities in the distribution of cardiovascular risk in chronic kidney disease stage 3. Nephron Clin Pract 2013;122:58-65.  Back to cited text no. 37
    
38.
Ballew SH, Matsushita K. Cardiovascular risk prediction in CKD. Semin Nephrol 2018;38:208-16.  Back to cited text no. 38
    
39.
Ma Y, Zhou L, Dong J, Zhang X, Yan S. Arterial stiffness and increased cardiovascular risk in chronic kidney disease. Int Urol Nephrol 2015;47:1157-64.  Back to cited text no. 39
    
40.
Liu M, Li XC, Lu L, Cao Y, Sun RR, Chen S, et al. Cardiovascular disease and its relationship with chronic kidney disease. Eur Rev Med Pharmacol Sci 2014;18:2918-26.  Back to cited text no. 40
    



 
 
    Tables

  [Table 1], [Table 2]



 

Top
   
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
   Abstract
  Introduction
  Methodology
  Results
  Discussion
  Conclusion
   References
   Article Tables

 Article Access Statistics
    Viewed99    
    Printed2    
    Emailed0    
    PDF Downloaded27    
    Comments [Add]    

Recommend this journal