‘Decision support system (DSS) for prevention of cardiovascular disease (CVD) among hypertensive (HTN) patients in Andhra Pradesh, India’ – a cluster randomised community intervention trial
© Anchala et al.; licensee BioMed Central Ltd. 2012
Received: 5 May 2012
Accepted: 31 May 2012
Published: 31 May 2012
Very few studies having decision support systems as an intervention report on patient outcomes for cardiovascular disease in the Western world. The potential role of decision support system for the management of blood pressure among Indian hypertensives remains unclear. We propose a cluster randomised trial that aims to test the effectiveness and cost effectiveness of DSS among Indian hypertensive patients.
The trial design is a cluster randomised community intervention trial, in which the participants would be adult male and female hypertensive patients, in the age group of 35 to 64 years, reporting to the Primary Health Care centres of Mahabubnagar district, Andhra Pradesh, India. The objective of the study is to test the effectiveness and compare the cost effectiveness and cost utility among hypertensive subjects randomized to receive either decision support system or a chart based algorithmic support system in urban and rural areas of a district in the state of Andhra Pradesh, India (baseline versus 12 months follow up). The primary outcome would be a comparison of the systolic blood pressure at 0 and 12 months among hypertensive patients randomized to receive the decision support system or the chart based algorithmic support system. Computer generated randomisation and an investigator and analyser blinded method would be followed. 1600 participants; 800 to each arm; each arm having eight clusters of hundred participants each have been recruited between 01 August 2011 - 01 March 2012. A twelve month follow up will be completed by March 2013 and results are expected by April 2013.
This cluster randomized community intervention trial on DSS will enable policy makers to find out the effectiveness, cost effectiveness and cost utility of decision support system for management of blood pressure among hypertensive patients in India. Most of the previous studies on decision support system have focused on physician performance, adherence and on preventive care reminders. The uniqueness of the proposed study lies in finding out the effectiveness of a decision support system on patient related outcomes.
CTRI/2012/03/002476, Clinical Trial Registry - India.
Compared with all other countries, India suffers the highest loss in Potentially Productive Years of Life Lost (PPYLL), due to deaths from cardiovascular disease in people aged 35–64 years (9·2 million PPYLL in 2000, with a projected loss of 17.9 million PPYLL by 2030) . CVD accounted for 29% of deaths and 11% of all Disability Adjusted Life Years (DALYs) in India (all ages, 2005) . Hypertension is directly responsible for 57% of all stroke deaths and 24% of all coronary heart disease deaths in India . The World Health Organization rates hypertension as one of the most important causes of premature death worldwide . The global and regional burden of disease and risk factors study (2001), in a systematic analysis of population health data for attributable deaths and attributable disease burden, has ranked Hypertension in South Asia as second only to child underweight for age .
In an analysis of worldwide data for the global burden of hypertension, 20.6% of Indian males and 20.9% of Indian females were suffering from hypertension in 2005 . The rates for hypertension in % are projected to go up to 22.9 and 23.6 for Indian males and females respectively by 2025 . Recent studies from India have shown the prevalence of hypertension to be 25% in urban and 10% in rural subjects in India [3, 7–9].
However, only about 25.6% of treated patients had their blood pressure under control, in a multi center study from India on awareness, treatment and adequacy of control of hypertension .
Decision support systems have been defined as the tools that help clinicians decide on a course of action in response to an understanding of the patient’s status. These have been devised to improve the management of hypertension and to help the Physician prescribe evidence based standardized medical care that will result in achieving adequate blood pressure control among hypertensive patients. DSS is a software that helps the Physician to (1) undertake a thorough evaluation of risk factors (2) to classify the risk level (3) to follow a software prompted algorithmic guideline based drug management (which has been developed based on Indian Hypertension guidelines II  (2007) and (4) to give alerts on the counseling on lifestyle changes and adherence to medication. Mixed results have been shown for DSS in the management of hypertension in the developed world for patient outcomes, but have shown that they improve the Physician performance [12–15]. An improvement in the quality of antihypertensive treatment, concurrently leading to a considerable reduction in drug costs have been shown for DSS .
Very few studies having Decision support systems (DSS) as an intervention report on patient outcomes for cardiovascular disease in the Western world. The potential role of DSS for the management of blood pressure among Indian hypertensives remains unclear. We propose a cluster randomised trial that aims to test the effectiveness and cost effectiveness of DSS among Indian hypertensive patients.
Are Decision Support Systems effective in lowering the blood pressure (therefore the risk for CVD) and improving the Quality of Life among hypertensive subjects in a Low and Middle Income setting? Are Decision Support Systems (DSS) cost effective in hypertensive subjects for prevention of cardiovascular diseases?
The objective of the study is to test the effectiveness and compare the cost effectiveness and cost utility among hypertensive subjects randomized to receive DSS or a chart based algorithmic support system in urban and rural areas of a district in the state of Andhra Pradesh, India (baseline versus 12 months follow up).
To recruit and follow up hypertensive subjects for 12 months
To evaluate the effectiveness of DSS on CV risk reduction and quality of life scores
To compare the CEA and CUA among hypertensive subjects
Primary end point
· To compare the SBP (Systolic Blood Pressure) at 0 and 12 months among hypertensive patients randomized to receive the DSS or the chart based algorithmic support system
Secondary end point
· To compare the Quality of Life (QoL) scores at 0 and 12 months among hypertensive patients randomized to receive the DSS or the chart based algorithmic support system
· To compare the Cost effectiveness of the DSS versus the chart based algorithmic support system at the end of 12 months among hypertensive patients.
· To compare the Cost utility of the DSS versus the chart based algorithmic support system at the end of 12 months among hypertensive patients.
Adult male and female Indians in the age group of 35 – 64 years
Systolic blood pressure (SBP) of 140 mm Hg or greater and/or diastolic blood pressure (DBP) of 90 mm Hg or greater (irrespective of antihypertensive medications)
Informed written Consent form
Hypertensive subjects who have been hospitalized within the last 12 months and subjects with a history of cancer (physician certified).
Physicians from the PHCs, who have been randomised to receive the DSS arm would necessarily be trained and instructed to follow the algorithms and prompts which would arise out of the computer based software, for management of BP. DSS is a software that helps the Physician to (1) undertake a thorough evaluation of risk factors that hypertensive patients may have for developing a cardiovascular disease, (2) to classify the risk level, (3) to follow a software prompted algorithmic guideline based drug management (which would be developed based on Indian Hypertension guidelines II – 2007) and (4) to give alerts on the counselling on lifestyle changes and adherence to medication.
The chart based algorithmic support system will have the guidelines and lifestyle advices printed as a poster format which would then be pasted on the wall of the PHC clinics. The Physicians from the PHCs, who have been randomised to receive the chart based support arm, would be instructed to follow the poster based guidelines for management of BP. The risk factors that need to be specifically elicited, classification of risk among hypertensive subjects, flow chart for drug management and advice for lifestyle interventions would be included as a simple flow chart.
Blood pressure and pulse measurement
All Blood Pressure (BP) measurements taken by the Physician will be performed in a standardised way using digital blood pressure equipments (Model M5, Omron) supplied and validated for the study. BP will be measured on the right upper arm in the sitting position, after a rest of 5 minutes. Using an appropriate sized cuff (which will be recorded) connected to a digital device, and the same arm (which will be recorded), at a similar time of day (which will be recorded), two measurements will be taken at a minute interval. Instructions will be given to the Physicians to ensure that the lower edge of the bladder be placed 2-3 cm above the position of maximal pulsation of the brachial artery in the arm, just above the antecubital fossa. Care would be taken to ensure that the cuff fits firmly, comfortably and is well secured. The mean of the two readings will be used for analysis.
Pulse measurements will be recorded by the digital blood pressure equipment (Model M5, Omron), after a rest of 5 minutes. The digital BP equipment records the systolic, diastolic BP and the pulse in the same sitting, which would be recorded. The average of the two readings will be used for the analysis.
Height, weight and waist circumference measurements
The participants would be weighed in light indoor clothing with a digital weighing machine (Tanita weighing Scale) with 100 g accuracy, which would be standardized across all the centers. Height would be measured in bare feet with Seca 213 Portable Freestanding stadiometer, accurate to 1 mm. Care would be taken to ensure that all the PHCs have the same equipment make, type and validation procedures.
Data on known cardiovascular risk factors (age, sex, tobacco usage, history of diabetes mellitus, family history of premature coronary artery disease); any associated clinical conditions (physician certified cerebro vascular disease, cardiovascular disease, renal, vascular diseases); target organ damage (physician certified left ventricular hypertrophy, hypertensive retinopathy and microalbuminuria); and quality of life scores (based on WHO BREF questionnaire will be collected at 0, 6 and 12 months after randomization.
Quality of life questionnaire
World Health Organization (WHO) QoL BREF questionnaires, which have been validated in Indian settings for chronic diseases, would be utilised in this study. WHOQOL was designed as an international cross-culturally comparable quality of life assessment instrument . This 26-item questionnaire has been tested and validated in Hindi language , and tested in Indian patients suffering from chronic diseases [18–20]. The questionnaire would be translated into the local language (Telugu) and back translated by two independent translators. A third reviewer would compare the translated and back translated versions. The finalised questionnaire will be field tested in 10% of the proposed sample size for cultural appropriateness. Questions which are deemed to hurt the cultural sensitivity would be modified based on pilot test results. This modified questionnaire will then be applied to the study. Patients would be asked to read and answer the questions on their own. In case of linguistic or other difficulties, the questionnaire would be administered by interview.
Cost effectiveness analysis
A comparison of costs in monetary units with outcomes in quantitative non-monetary units would done in the Cost Effectiveness Analysis (CEA) . Resources required for intervention and values attached to those resources and effects of treatment (either benefit or harm) and values attached to those effects for the both the groups will be compared to determine the cost effectiveness ratios. Cost effectiveness ratios, that is the cost/outcome of different interventions, enable opportunity costs of each intervention to be compared.
A third party payer perspective will be employed in this project, as this would enable the tangible costs to be directly compared between both the intervention arms.
The value of all goods, services, and other resources consumed in providing health care or dealing with side effects or other current and future consequences of health care comprise the direct costs. Direct health care and direct non health costs will be measured in this study. The physician services, hospital services, drugs, costs of the DSS software development, costs for charts (posters) etc. involved in delivery of health care would comprise the direct health care costs. Transportation to and from the site of care would comprise the indirect costs. Marginal costs analysis would be done in which how outcomes change with changes in costs (e.g., relative to a comparator), would be analysed. The time horizon for the costs involved would be for a duration of a year.
A reduction or worsening of systolic BP would be compared (in non monetary terms). The time horizon would be for a duration of one year.
Thus, costs and outcomes will be discounted relative to their present value (e.g., at a rate of three - five percent per year) so as to account for the effect of time on the value of the outcomes and costs, as they may have less value in the future. Opportunity costs, which would be reflected by discounting, would also be calculated.
Since any estimation of costs and outcomes has an uncertainty element attached to it, a sensitivity analysis, which would include a change in discount rates, would be done to account for the changes in the cost-effectiveness planes of one intervention compared to another.
Cost Effectiveness Ratio (CER)
Cost utility analysis (CUA)
Measures costs in monetary units with outcomes in terms of their utility attribute [Quality Adjusted Life Years (QALYs)], i.e. cost per QALY. Cost utility would be measured by Incremental Cost Effectiveness Ratios (ICERs) between both the groups. ICER is a ratio between the differences in the cost involved between the intervention proposed (in Indian National Rupees) and the difference in QALYs between the groups.
Health Related Quality of Life (HRQoL) would be measured on a Visual Analogue Scale (VAS) preference scale with a maximum score of 100 (perfect or best imaginable health) and minimum score of 0 (a quality of life which equals worst health imagined) to measure the value of the health state. The weights for the quality will be based on the (1) preferences of the participants, (2) measured on an interval scale and (3) anchored on perfect health and death. The value of a health state would then be combined with the time spent to calculate the total QALYs. Usefulness, interpretability, and responsiveness were the determining factors to choose VAS as the preferred way to measure the utility of the present health state.
Sample size, sampling and recruitment
sample size calculations
Detectable difference in SBP between both the groups
80% power with an ICC of 0.02
90% power with an ICC of 0.02
Sample size required for individual randomization per arm
Sample size required for cluster randomization per arm (DE = 2.98)
Sample size required for cluster randomization with CV = 0.25 (DE = 3.1)
Minimum no of clusters with a cluster size of 100
Sample size required for individual randomization per arm
Sample size required for cluster randomization per arm (DE = 2.98)
Sample size required for cluster randomization with CV = 0.25 (DE = 3.1)
Minimum no of clusters with a cluster size of 100
4 mm of Hg
5 mm of Hg
6 mm of Hg
Randomly chosen 8 PHCs – 4 urban and 4 rural (homogenous in age and gender distribution) for each intervention arm from a district in the State of AP, India would form the study centers. Each PHC has an out patient load of 50-100 patients/day. 2- 4 hypertensive patients are being envisaged to be recruited per day by the Physician per PHC from the patients attending the out patient clinic in the PHC. 10- 20 patients per week per PHC, 20- 40 per month per PHC and 125 patients in 4-6 months. Study recruitment will last from June 2011 to Dec 2011.
The participants would be given sufficient time to understand the contents of the PIS (containing the study purpose, duration, study procedures, right to withdrawal from the study, details of patient confidentiality, ethical committee clearances, the study team details), and encouraged to ask questions regarding any aspects of the study procedures that they have not understood. The written informed consent form will be read out aloud in the native language to the study participants. They will be given an opportunity to seek clarifications on any issues that have not been understood. The signatures would be obtained by the Physician who would be recruiting the hypertensive patients for the study. The Physician would also sign and date the informed consent form. One copy would be given to the patients and one copy would be retained at the study site.
Hypertension exerts a substantial burden on cardiovascular health status and health care systems in India. Treatment however can be costly and health care providers are interested in both whether treatments can offer improvements in disease burden and whether they represent value for money. Economic evaluations seek to resolve this issue by producing results that can be used to inform and assist the decision maker in allocating scarce health care resources. DSS have been in place in the developed world to achieve a balance between quality and cost of chronic disease health care. To the best of the author’s knowledge, there have been no randomized studies on DSS to enable policy makers to find out the effectiveness, cost effectiveness and cost utility analysis (CUA) of DSS for hypertensive patients in India. The cluster randomised community trial aims to test a decision support system among Indian hypertensive patients. Most of the previous studies on decision support system have focused on physician performance, adherence and on preventive care reminders. The uniqueness of the proposed study lies in finding out the effectiveness of a decision support system on patient related outcomes.
There are no known risks attributable to DSS per se. If the participant is receiving DSS, the software assisting the Physician prompts him or her to prescribe a particular class of drug to manage and control blood pressure. If the participant is receiving the chart based algorithmic support system, the decision to start or continue a drug is entirely up to the Physician’s judgement. The software will be developed and pilot tested and will be based on standard Indian Hypertension society guidelines (2007), which have been endorsed by the Association of Physicians of India and the Indian Cardiological Society. Adverse events that may arise due to the drugs will be monitored and any serious adverse events will be notified to the ethical committee within 24 hours. Should any serious adverse events arise the participants would be given the necessary care in the primary health care centre and if necessary will be transferred to the Rural Hospital. Substitution of the drugs causing adverse events is left to the decision of the Physician. Blood will be drawn by a qualified and an experienced phlebotomist, with due precautions. The quality of life questionnaire would be adapted from WHO BREF questionnaire which has been validated in India. Patient confidentiality will be maintained at data entry and data analysis stage.
This cluster randomised community trial aims to test effectiveness, cost effectiveness and cost utility analysis of a decision support system among Indian hypertensive patients in comparison to a chart based algorithmic support system (that will have the guidelines and lifestyle advices printed as a poster format). The utility of DSS in management of hypertension will be tested in this pilot study. Subsequent scale up of DSS and future studies of DSS in management of different domains of cardiovascular diseases are envisaged based on the results of this pilot study.
Approved by the (1) Human Biology Research Ethics Committee, University of Cambridge, United Kingdom and (2) Institutional Ethics Committee, Public Health Foundation of India, New Delhi, India.
This work was supported by a Wellcome Trust Capacity Strengthening Strategic Award to the Public Health Foundation of India and a consortium of UK universities.
No additional data available.
- Leeder S, Raymond S, Greenberg H, Liu H, Esson K: A race against time. 2004, Columbia University, The challenge of cardiovascular disease in developing economies. New YorkGoogle Scholar
- Srinath Reddy K, Bela Shah, Cherian Varghese, Anbumani Ramadoss: Responding to the threat of chronic diseases in India. Lancet. 2005, 366: 1746-1751.View ArticleGoogle Scholar
- Gupta R: Trends in hypertension epidemiology in India. J Hum Hypertens. 2004, 18: 73-78. 10.1038/sj.jhh.1001633.View ArticlePubMedGoogle Scholar
- Mackay J, Mensah G: Atlas of Heart Disease and Stroke. 2004, World Health Organization, GenevaGoogle Scholar
- Lopez Alan D, Mathers Colin D, Majid Ezzati, Jamison Dean T, Murray Christopher J L: Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006, 367: 1747-1757. 10.1016/S0140-6736(06)68770-9.View ArticlePubMedGoogle Scholar
- Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J: Global burden of hypertension: analysis of worldwide data. Lancet. 2005, 365: 217-223.View ArticlePubMedGoogle Scholar
- Thankappan KR, Sivasankaran S, Khader SA, Sarma PS, Mini GK, Vasan RS: Prevalence, correlates, awareness, treatment and control of hypertension in Kumarakom, Kerala: baseline results of a community-based intervention program. Indian Heart J. 2006, 58: 28-33.PubMedGoogle Scholar
- Gupta R: Meta-analysis of prevalence of hypertension in India. Indian Heart J. 1997, 49: 337-338.Google Scholar
- Das SK, Sanyal K, Basu A: Study of urban community survey in India: growing trend of high prevalence of hypertension in a developing country. Int J Med Sci. 2005, 2: 70-78.View ArticlePubMedPubMed CentralGoogle Scholar
- Hypertension Study Group: Prevalence, awareness, treatment and control of hypertension among the elderly in Bangladesh and India: a multicentre study. Bull World Health Organ. 2001, 79 (6): 490-500.PubMed CentralGoogle Scholar
- Indian Hypertension guidelines II: http://www.apiindia.org (accessed on 01 Jan 2011)
- Montgomery AA, Tom F, Peters TJ, Christopher M, Sharp DJ: Evaluation of computer based clinical decision support system and risk chart for management of hypertension in primary care: randomised controlled trial. BMJ. 2000, 320:Google Scholar
- Jane A, Anderson , Pamela Willson, Nancy J, Peterson , Chris Murphy, Thomas A, Kent : Prototype to Practice Developing and Testing a Clinical Decision Support system for Secondary Stroke Prevention in a Veterans Healthcare Facility. Comput Inform Nurs. 2010, 28 (6): 353-63. 10.1097/NCN.0b013e3181f69c5b.View ArticleGoogle Scholar
- Garg AX, Adhikari NKJ, McDonald H: Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient outcomes: A Systematic Review. JAMA. 2005, 293 (10): 1223-1238. 10.1001/jama.293.10.1223.View ArticlePubMedGoogle Scholar
- Shojania KG, Jennings A, Mayhew A, Ramsay CR, Eccles MP, Grimshaw J: The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev. 2009, Art. No.: CD001096-Issue 3
- Persson M, Mjoè Rndal T, Carlberg B, Bohlin J, Lindholm IH: Evaluation of a computer-based decision support system for treatment of hypertension with drugs: retrospective, nonintervention testing of cost and guideline adherence. J Int Med. 2000, 247: 87-93. 10.1046/j.1365-2796.2000.00581.x.View ArticleGoogle Scholar
- Saxena S, Chandiramani K, Bhargava R: WHOQOL-Hindi: A questionnaire for assessing quality of life in health care settings in India. World Health Organization Quality of Life. Natl Med J India. 1998, 11 (4): 160-5.PubMedGoogle Scholar
- Mohan A, Singh P, Kumar S, Mohan C, Pathak AK, Pandey RM, Guleria R: Effect of change in symptoms, respiratory status, nutritional profile and quality of life on response to treatment for advanced non-small cell lung cancer. Asian Pac J Cancer Prev. 2008, 9 (4): 557-62.PubMedGoogle Scholar
- Sinha P, Taneja DK, Dhuria M, Saha R: Incidence of summer associated symptoms, host susceptibility and their effect on quality of life among women 18 to 40 years of age in an urban slum of Delhi. Indian J Public Health. 2008, 72 (2):
- Sathvik BS, Parthasarathi G, Narahari MG, Gurudev KC: An assessment of the quality of life in hemodialysis patients using the WHOQOL-BREF questionnaire. Indian J Nephrol. 2008, 18 (4): 141-9. 10.4103/0971-4065.45288.View ArticlePubMedPubMed CentralGoogle Scholar
- National Information Center on Health Services Research and Health Care Technology (NICHSR) - HTA101:IV.COST ANALYSIS METHODS: http://www.nlm.nih.gov/nichsr/hta101/ta10106.html (accessed on 27 Jan 2011)
- Kinra S, Bowen LJ, Lyngdoh T, Prabhakaran D, Reddy KS, Ramakrishnan L, Gupta R, Bharathi AV, Vaz M, Kurpad AV, Smith GD, Ben-Shlomo Y, Ebrahim S: Sociodemographic patterning of non-communicable disease risk factors in rural India: a cross sectional study. BMJ. 2010, 341: c4974-10.1136/bmj.c4974.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/393/prepub