Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population

Rokhman, M. Rifqi; Arifin, Bustanul; Zulkarnain, Zulkarnain; Satibi, Satibi; Perwitasari, Dyah Aryani; Boersma, Cornelis; Postma, Maarten J.; Schans, Jurjen van der

doi:10.1371/journal.pone.0269853

Cited by 9 publications

(16 citation statements)

References 50 publications

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“…The authors of the articles got data from four sources. Eleven studies' data source is from community survey (50%), 4,5,6,8,9,12,16,17,20,21,23 Eight studies used data from databank (36.4%), 3,7,10,14,18,19,22,24 another two studies collected data by random sampling (9%) 11,15 and…”

Section: Fig 3: Shows Countries Where the Included Studies Were Condu...mentioning

confidence: 99%

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Haile

2023

Preprint

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BackgroundDiabetes is a severe chronic disease that needs due attention. Worldwide distribution of the disease is increasing in alarming trend marked by its pandemic nature. Numerous people are disabled and dead due to the complexity of the disease and its management. A recent CDC report revealed that in the USA, 37.3 million people have Diabetes (11.3% of the US population). Ninety-six million people aged 18 years and older have prediabetes (38.0% of the adult US population). Worldwide, the number of people with Diabetes rose from 108 million in 1980 to 422 million in 2014. WHO on its sustainable development goal (SDG) report made clear the existing diabetic patient evaluation, intervention, and monitoring method does not adequately address the problem. Paradoxically, the ambitious goal of the American Diabetes Association is: “A Future Without Diabetes.”The current Diabetes screening method is invasive, provided that scarcity of resources and high cost of test kit makes it nonpractical to frequently checkup. We must proactively engage in its prevention by making screening cheap and easily accessible. So, this review is designed to answer the question: What is an available Diabetes noninvasive screening tool for undiagnosed?MethodThe relevant Article was searched from PubMed, MEDLINE, Embase, Web of Science, SCOPUS, and the CINHAL using a combination of the following key constructs: Diabetes screening, Diabetes noninvasive screening, Diabetes AND screening, Diabetes and (“noninvasive” or “non-invasive” or “non invasive”) and “screening tool,” Diabetes AND noninvasive AND screening AND Tool, “Diabetes noninvasive Screening Tool.” The selection process was conducted based on the PRISMA framework statement. Exclusion and inclusion criteria were applied to select the final relevant literature, after which 22 relevant studies were selected.ResultThe review included studies conducted from 2013 to 2023, 36% in 2019 and 2022. The included articles are from 13 countries. The data sources for the articles are surveys, databanks, random sampling, and opportunity sampling. A total of 24 key predictors were used to develop 22 types of tools. The researchers used the logistic regression (LR) method while developing most studies, and ML in a couple of them. The Authors described the Tool’s performance using the ROC curve, and the AUC is 0.65-0.93. The developed tool predictive power of the screening Prediabetes used Sensitivity, Specificity, PPV, NPV metrics and the result is encouraging.ConclusionDiabetes noninvasive Screening tests have the potential to identify prediabetes at an early stage and, thus, a more treatable disease that, as a result, saves lives. The developed tools had promising designate ability in pre-DM case findings, as shown by their ROC curve, AUC, sensitivity/specificity, and PPV/NPV. The Tool developed by these researchers looks promising for our goal of screening Diabetes by noninvasive method to answer this review research question using non-laboratory methods, which can be applied by common people regardless of the involvement of health professional skill. This review’s limitations are that the included article’s bias assessment was not performed, and non-English language articles were not included, so this could miss some pertinent tools. Finally, a systematic review, meta-analysis, and RCT on the Tool are recommended to identify any bias during the development of the Tool and possible generalizability of the best Tool for worldwide applicability.

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Section: Fig 3: Shows Countries Where the Included Studies Were Condu...mentioning

confidence: 99%

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Haile

2023

Preprint

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“…In contrast, we used VOSviewer and Biblioshiny to show data instrument. Furthermore, the first publication discussing this topic was in 2005 22 . This finding is in line with the Word Cloud analysis (Figure 10), which shows that the word "Finland" is the word with the highest frequency of occurrence.…”

Section: Data Analysis and Visualizationmentioning

confidence: 99%

Bibliometric Analysis of the Utilisation of FINDRISC in Patients with Diabetes: 2005-2021

Rokhman

Arifin²,

Zulkarnain³

et al. 2022

Borneo J Pharm

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Research on risk factors for diabetes (DM) is growing. Identification of these risk factors aims to prevent DM as early as possible. This study intends to identify the utilization of the Finnish diabetes risk score (FINDRISC) and its development using bibliometric analysis. The keywords “FINDRISC AND Diabetes” were used to search for articles published in 2005-2021 in PubMed. A total of 249 articles were analyzed based on the number of publications per year, journals that publish the papers, number of publications by author and year of publication, number of publications by affiliation and year of publication, number of publications by country of origin of authors and year of publication, number of keywords, number of citations, types of articles, specific topics, and theme mapping. The data visualization was obtained from the Scopus database and the VOSviewer and Biblioshiny applications. Despite the increase in publications, the number of publications on FINDRISC in DM patients is still very few per year, with 92.8% being the primary study. Based on clusters of the country of origin, publications are still dominated by researchers from countries in the European region, and the researchers intensely relate to each other through citations. Research themes related to FINDRISC are not limited to DM risk factors. This study is the first study of a bibliometric analysis of the utilization of FINRISC in DM patients. The analysis results can be used to evaluate existing research gaps and identify future research opportunities.

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“…The Modified Asian FINDRISC (ModAsian FINDRISC), which applied cut-off points for waist circumference (WC) and Body Mass Index (BMI) based on Asian standards, has been proposed as a useful tool for T2DM screening in several Asian countries, such as Indonesia, 19 Malaysia, 20 and Vietnam. 21–24 However, these study populations were primarily small sample sizes or lack of representation.…”

Section: Introductionmentioning

confidence: 99%

ModAsian FINDRISC as a Screening Tool for People with Undiagnosed Type 2 Diabetes Mellitus in Vietnam: A Community-Based Cross-Sectional Study

Doan¹,

Nguyen²,

Nguyen³

et al. 2023

JMDH

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Purpose Our study aims to evaluate the risk of developing type 2 diabetes mellitus in the next 10 years using ModAsian FINDRISC and additionally explore associated factors among the Vietnam population. Participants and Methods A cross-sectional study was conducted on 2258 participants aged 25 years old or above in Thua Thien Hue Province, Vietnam. The sample size is calculated based on the estimated sensitivity, and participants were randomly selected from different geographical and socio-economic areas. All participants were thoroughly medically examined, taking blood lipid profile and fasting blood glucose, taking blood pressure, anthropometric indexes, 12-lead electrocardiogram, and behavioral factors were investigated using the Vietnamese version of the WHO STEPS toolkit. The risk of developing T2DM was made based on the ModAsian FINDRISC. Results The incidence of developing type 2 diabetes mellitus among the study population was 4.21%. The group with a high or very high risk of developing type 2 diabetes mellitus in the next 10 years accounted for 2.52%. Body mass index (AUC = 0.840, 95% CI: 0.792–0.888), waist circumference (AUC = 0.824, 95% CI: 0.777–0.871), family history of diabetes mellitus (AUC = 0.751, 95% CI = 0.668–0.833), and history of antihypertensive medication use regularly (AUC = 0.708, 95% CI: 0.632–0.784) are the most associated factors of the ModAsian FINDRISC. Residential location (OR = 5.62, 95% CI: 1.91–16.54) and occupational status (OR = 0.35, 95% CI: 0.20–0.62) were significant factors associated with a high and very high risk of developing type 2 diabetes mellitus in the next 10 year. Conclusion Screening for the risk of type 2 diabetes mellitus and implementing intervention programs focusing on controlling weight, waist circumference, and blood pressure are essential for reducing type 2 diabetes mellitus incidence and burden in Vietnam.

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Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population

Cited by 9 publications

References 50 publications

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Bibliometric Analysis of the Utilisation of FINDRISC in Patients with Diabetes: 2005-2021

ModAsian FINDRISC as a Screening Tool for People with Undiagnosed Type 2 Diabetes Mellitus in Vietnam: A Community-Based Cross-Sectional Study

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