Technology for Diagnosis, Treatment, and Prevention of Cardiometabolic Disease in India.

Cardiometabolic diseases (CMD) are a major cause of mortality, morbidity and disability worldwide. Among Indians, CMD onset is at a much younger age and is prevalent in all sections of the society. Prevention, control and management of CMD and its risk factors is a major public health challenge, and alternative approaches need to be explored and integrated into public health programs. Advancements in the fields of computers, electronics, telecommunication and medicine have resulted in the rapid development of health-related technology. In this paper we provide an overview of the major technological advances in diagnosis, treatment and prevention within the field of CMD in the last few decades. This non-exhaustive review focuses on the most promising technologies that the authors feel might be of relevance in the Indian context. Some of the techniques detailed include advances in imaging and mobile phone technology, surgical techniques, electronic health records, Nano medicine, telemedicine and decision support systems.

[1]  T. Payne Computer decision support systems. , 2000, Chest.

[2]  S. Prakash,et al.  Nanomedicine in cardiovascular therapy: recent advancements , 2012, Expert review of cardiovascular therapy.

[3]  K. Kostner,et al.  Protocol for a randomised blocked design study using telephone and text-messaging to support cardiac patients with diabetes: a cross cultural international collaborative project , 2013, BMC Health Services Research.

[4]  Ralf Sodian,et al.  Three-dimensional printing in cardiac surgery and interventional cardiology: a single-centre experience. , 2015, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[5]  S. Yusuf,et al.  Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study , 2004, The Lancet.

[6]  Ian J. Brown,et al.  Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. , 2014, The lancet. Diabetes & endocrinology.

[7]  Dorairaj Prabhakaran,et al.  ‘Decision support system (DSS) for prevention of cardiovascular disease (CVD) among hypertensive (HTN) patients in Andhra Pradesh, India’ – a cluster randomised community intervention trial , 2012, BMC Public Health.

[8]  Estella M. Geraghty,et al.  Using Geographic Information Systems (GIS) to Assess Outcome Disparities in Patients with Type 2 Diabetes and Hyperlipidemia , 2010, The Journal of the American Board of Family Medicine.

[9]  M. Nair,et al.  Carotid artery stenting: results and long-term follow-up. , 2006, Neurology India.

[10]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[11]  M. Wakefield,et al.  Results of a national mass media campaign in India to warn against the dangers of smokeless tobacco consumption , 2011, Tobacco Control.

[12]  David M Herrington,et al.  Genetics and genomics for the prevention and treatment of cardiovascular disease: update: a scientific statement from the American Heart Association. , 2013, Circulation.

[13]  R. Peto,et al.  Prospective Study of One Million Deaths in India: Rationale, Design, and Validation Results , 2005, PLoS medicine.

[14]  N. Santhiyakumari,et al.  Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks , 2011, Journal of Digital Imaging.

[15]  R. Haynes,et al.  Effects of Computer-based Clinical Decision Support Systems on Clinician Performance and Patient Outcome: A Critical Appraisal of Research , 1994, Annals of Internal Medicine.

[16]  A. Stiell,et al.  Prevalence of information gaps in the emergency department and the effect on patient outcomes. , 2003, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[17]  Aparajita Dasgupta,et al.  Telemedicine: A New Horizon in Public Health in India , 2008, Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine.

[18]  Adrian F Hernandez,et al.  Valvular heart disease in patients supported with left ventricular assist devices. , 2014, Circulation. Heart failure.

[19]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[20]  Huiru Zheng,et al.  Stroke patients’ utilisation of extrinsic feedback from computer-based technology in the home: a multiple case study realistic evaluation , 2014, BMC Medical Informatics and Decision Making.

[21]  Mary K Goldstein,et al.  Patient education and provider decision support to control blood pressure in primary care: a cluster randomized trial. , 2009, American heart journal.

[22]  Koushik Maharatna,et al.  An automated algorithm for online detection of fragmented QRS and identification of its various morphologies , 2013, Journal of The Royal Society Interface.

[23]  S. K. Srivatsa,et al.  Design of wearable cardiac telemedicine system , 2007, Int. J. Electron. Heal..

[24]  V. Mohan,et al.  Improving diabetes care: multi-component cardiovascular disease risk reduction strategies for people with diabetes in South Asia--the CARRS multi-center translation trial. , 2012, Diabetes research and clinical practice.

[25]  S S Deo,et al.  Design and development of a web-based application for diabetes patient data management. , 2005, Informatics in primary care.

[26]  Johan van der Lei,et al.  Cholgate - A Randomized Controlled Trial Comparing The Effect Of Automated And On-Demand Decision Support On The Management Of Cardiovascular Disease Factors In Primary Care , 2003, AMIA.

[27]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[28]  Mauro Ferrari,et al.  Emerging applications of nanomedicine for the diagnosis and treatment of cardiovascular diseases. , 2010, Trends in pharmacological sciences.

[29]  Harshal R. Patil,et al.  Lifestyle Choices Fuel Epidemics of Diabetes and Cardiovascular Disease Among Asian Indians. , 2016, Progress in cardiovascular diseases.

[30]  Neetika Garg,et al.  A content analysis of smartphone-based applications for hypertension management. , 2015, Journal of the American Society of Hypertension : JASH.

[31]  R. Whittaker,et al.  A Multimedia Mobile Phone–Based Youth Smoking Cessation Intervention: Findings From Content Development and Piloting Studies , 2008, Journal of medical Internet research.

[32]  S. Ramakrishna,et al.  Cardiogenic differentiation of mesenchymal stem cells with gold nanoparticle loaded functionalized nanofibers. , 2015, Colloids and surfaces. B, Biointerfaces.

[33]  C. Terranova,et al.  Peripheral blood mono-nuclear cells implantation in patients with peripheral arterial disease: a pilot study for clinical and biochemical outcome of neoangiogenesis , 2012, BMC Surgery.

[34]  Sukanesh Rajamony,et al.  Viable investigations and real-time recitation of enhanced ECG-based cardiac telemonitoring system for homecare applications: a systematic evaluation. , 2013, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[35]  P. Shekelle,et al.  Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care , 2006, Annals of Internal Medicine.

[36]  E. Supriyanto,et al.  Enhanced Blood Compatibility of Metallocene Polyethylene Subjected to Hydrochloric Acid Treatment for Cardiovascular Implants , 2014, BioMed research international.

[37]  D. Schillinger,et al.  Closing the loop: physician communication with diabetic patients who have low health literacy. , 2003, Archives of internal medicine.

[38]  D. Nathan,et al.  Clinical Trial of Programmable Implantable Insulin Pump For Type I Diabetes , 1992, Diabetes Care.

[39]  M. Arora,et al.  Health promotion for primordial prevention of tobacco use. , 2012, Global heart.

[40]  Nikhil Tandon,et al.  Review of Electronic Decision-Support Tools for Diabetes Care: A Viable Option for Low- and Middle-Income Countries? , 2011, Journal of diabetes science and technology.

[41]  L. Sulmasy,et al.  Policy recommendations to guide the use of telemedicine in primary care settings: an American College of Physicians position paper. , 2015, Annals of internal medicine.

[42]  V. Ajay,et al.  A Cluster-Randomized, Controlled Trial of a Simplified Multifaceted Management Program for Individuals at High Cardiovascular Risk (SimCard Trial) in Rural Tibet, China, and Haryana, India , 2015, Circulation.

[43]  Sanjay Kinra,et al.  Role of mobile phone technology in tobacco cessation interventions. , 2012, Global heart.

[44]  R. Haynes,et al.  Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. , 1998, JAMA.

[45]  G. Klintmalm,et al.  Pancreatic islet cell transplantation: update and new developments. , 2007, Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition.

[46]  K. Dontje,et al.  Electronic health record: implementation across the Michigan Academic Consortium. , 2006, Computers, informatics, nursing : CIN.

[47]  C. Ohmann,et al.  Laser-Supported CD133+ Cell Therapy in Patients with Ischemic Cardiomyopathy: Initial Results from a Prospective Phase I Multicenter Trial , 2014, PloS one.

[48]  Gari D Clifford,et al.  A multifaceted strategy using mobile technology to assist rural primary healthcare doctors and frontline health workers in cardiovascular disease risk management: protocol for the SMARTHealth India cluster randomised controlled trial , 2013, Implementation Science.

[49]  Joseph Siemienczuk,et al.  The impact of a physician-directed health information technology system on diabetes outcomes in primary care: a pre- and post-implementation study. , 2009, Informatics in primary care.

[50]  K. Cherian,et al.  Nanofiber-reinforced myocardial tissue-construct as ventricular assist device , 2014, Asian cardiovascular & thoracic annals.

[51]  V. Mohan,et al.  CARRS Surveillance study: design and methods to assess burdens from multiple perspectives , 2012, BMC Public Health.

[52]  M. Weisfeldt,et al.  Advances in the prevention and treatment of cardiovascular disease. , 2007, Health affairs.

[53]  Jean Sanderson,et al.  The Role of Decision Support System (DSS) in Prevention of Cardiovascular Disease: A Systematic Review and Meta-Analysis , 2012, PloS one.

[54]  R. Haynes,et al.  Effects of Computer-Based Clinical Decision Support Systems on Physician Performance and Patient Outcomes , 1998 .

[55]  Joyce A. Mitchell,et al.  The clinical value of computerized information services. A review of 98 randomized clinical trials. , 1996, Archives of family medicine.

[56]  A. Bahl,et al.  Congestive heart failure in Indians: How do we improve diagnosis & management? , 2010, The Indian journal of medical research.

[57]  Bernard C. K. Choi,et al.  The Past, Present, and Future of Public Health Surveillance , 2012, Scientifica.

[58]  P. Sylaja,et al.  Telestroke a Viable Option to Improve Stroke Care in India , 2014, International journal of stroke : official journal of the International Stroke Society.

[59]  Benjamin C. Tang,et al.  Managing diabetes with nanomedicine: challenges and opportunities , 2014, Nature Reviews Drug Discovery.

[60]  T. Mcconnell,et al.  Using a Telemedicine System to Decrease Cardiovascular Disease Risk in an Underserved Population: Design, Use, and Interim Results , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[61]  A. Bauman,et al.  Can population levels of physical activity be increased? Global evidence and experience. , 2015, Progress in cardiovascular diseases.