Dataset Information

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India).

ABSTRACT:

Introduction

The aim of this study is to develop practical and affordable models to (a) diagnose people with diabetes and prediabetes and (b) identify those at risk of diabetes complications so that these models can be applied to the population in low-income and middle-income countries (LMIC) where laboratory tests are unaffordable.

Methods and analysis

This statistical and economic modelling study will be done on at least 48 000 prospectively recruited participants aged 40 years or above through community screening across 20 predefined regions in India. Each participant will be tested for capillary random blood glucose (RBG) and complete a detailed health-related questionnaire. People with known diabetes and all participants with predefined levels of RBG will undergo further tests, including point-of-care (POC) glycated haemoglobin (HbA1c), POC lipid profile and POC urine test for microalbuminuria, retinal photography using non-mydriatic hand-held retinal camera, visual acuity assessment in both eyes and complete quality of life questionnaires. The primary aim of the study is to develop a model and assess its diagnostic performance to predict HbA1c diagnosed diabetes from simple tests that can be applied in resource-limited settings; secondary outcomes include RBG cut-off for definition of prediabetes, diagnostic accuracy of cost-effective risk stratification models for diabetic retinopathy and models for identifying those at risk of complications of diabetes. Diagnostic accuracy inter-tests agreement, statistical and economic modelling will be performed, accounting for clustering effects.

Ethics and dissemination

The Indian Council of Medical Research/Health Ministry Screening Committee (HMSC/2018-0494 dated 17 December 2018 and institutional ethics committees of all the participating institutions approved the study. Results will be published in peer-reviewed journals and will be presented at national and international conferences.

Trial registration number

ISRCTN57962668 V1.0 24/09/2018.

SUBMITTER: Sivaprasad S

PROVIDER: S-EPMC7735124 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India).

Sivaprasad Sobha S Raman Rajiv R Rajalakshmi Ramachandran R Mohan Viswanathan V Deepa Mohan M Das Taraprasad T Ramasamy Kim K Prevost A Toby AT Wittenberg Raphael R Netuveli Gopalakrishnan G Lingam Gopal G Hanif Wasim W Ramakrishnan Radha R Ramu Jayashree J Surya Janani J Conroy Dolores D

BMJ open 20201212 12

<h4>Introduction</h4>The aim of this study is to develop practical and affordable models to (a) diagnose people with diabetes and prediabetes and (b) identify those at risk of diabetes complications so that these models can be applied to the population in low-income and middle-income countries (LMIC) where laboratory tests are unaffordable.<h4>Methods and analysis</h4>This statistical and economic modelling study will be done on at least 48 000 prospectively recruited participants aged 40 years ...[more]

PMID: 33310798

Similar Datasets

Project description:ObjectivesTo develop a risk-stratified intervention strategy and evaluate its effect on reducing surgical complications.DesignA multicentre prospective study with preintervention and postintervention stages: period I (January to June 2015) to develop the intervention strategy and period II (January to June 2016) to evaluate its effectiveness.SettingFour academic/teaching hospitals representing major Chinese administrative and economic regions.ParticipantsAll surgical (elective and emergent) inpatients aged ≥14 years with a minimum hospital stay of 24 hours, who underwent a surgical procedure requiring an anesthesiologist.InterventionsTargeted complications were grouped into three categories (common, specific, serious) according to their incidence pattern, severity and preventability. The corresponding expert consensus-generated interventions, which focused on both regulating medical practices and managing inherent patient-related risks, were implemented in a patient-tailored way via an electronic checklist system.Primary and secondary outcomesPrimary outcomes were (1) in-hospital death/confirmed death within 30 days after discharge and (2) complications during hospitalisation. Secondary outcome was length of stay (LOS).ResultsWe included 51 030 patients in this analysis (eligibility rate 87.7%): 23 413 during period I, 27 617 during period II. Patients' characteristics were comparable during the two periods. After adjustment, the mean number of overall complications per 100 patients decreased from 8.84 to 7.56 (relative change 14.5%; P<0.0001). Specifically, complication rates decreased from 3.96 to 3.65 (7.8%) for common complications (P=0.0677), from 0.50 to 0.36 (28.0%) for specific complications (P=0.0153) and from 3.64 to 2.88 (20.9%) for serious complications (P<0.0001). From period I to period II, there was a decreasing trend for mortality (from 0.64 to 0.53; P=0.1031) and median LOS (by 1 day; P=0.8293), without statistical significance.ConclusionsImplementing a risk-stratified intervention strategy may be a target-sensitive, convenient means to improve surgical outcomes.

Project description:INTRODUCTION:Atrial fibrillation (AF), the world's most common arrhythmia, often goes undetected and untreated in low-resource communities, including India, where AF epidemiology is undefined. AF is an important risk factor for stroke, which plagues an estimated 1.6?million Indians annually. As such, early detection of AF and management of high-risk patients is critically important to decrease stroke burden in individuals with AF. This study aims to describe the epidemiology of AF in Anand District, Gujarat, India, characterise the clinical profile of individuals who are diagnosed with AF and determine the performance of two mobile technologies for community-based AF screening. METHODS:This observational study builds on findings from a previous feasibility study and leverages two novel technologies as well as an existing community health programme to perform door-to-door AF screening for 2000 people from 60 villages of Anand District, Gujarat, India using local health workers. A single-lead ECG and a pulse-based application is used to screen each individual for AF three times over a period of 5 days. Participants with suspected arrhythmias are followed up by study cardiologist who makes final diagnoses. Participants diagnosed with AF are initiated on treatment based on current anticoagulation guidelines and clinical reasoning. ANALYTICAL PLAN:Age-stratified and sex-stratified prevalence of AF in the Anand District will be calculated for sample and estimated for Anand distribution using survey design weights. Sociodemographic and clinical factors associated with AF will be evaluated using multivariable regression methods. Performance of each mobile technology in detecting AF will be evaluated using a 12-lead ECG interpretation as the gold standard. ETHICS AND DISSEMINATION:This protocol was approved separately by the Institutional Review Board of University of Massachusetts Medical School and the Human Research Ethics Committee at Charutar Arogya Mandal. The findings of this study will be disseminated through peer-reviewed journals and scientific conferences.

Dataset Information

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India).

Introduction

Methods and analysis

Ethics and dissemination

Trial registration number

Publications

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets