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Article: Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review
Title | Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review |
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Authors | |
Keywords | early detection non-laboratory-based pre-diabetes risk prediction tools |
Issue Date | 29-Mar-2023 |
Publisher | MDPI |
Citation | Diagnostics, 2023, v. 13, n. 7 How to Cite? |
Abstract | Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation. |
Persistent Identifier | http://hdl.handle.net/10722/340839 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.667 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cheng, WHG | - |
dc.contributor.author | Mi, YQ | - |
dc.contributor.author | Dong, WA | - |
dc.contributor.author | Tse, ETY | - |
dc.contributor.author | Wong, CKH | - |
dc.contributor.author | Bedford, LE | - |
dc.contributor.author | Lam, CL | - |
dc.date.accessioned | 2024-03-11T10:47:41Z | - |
dc.date.available | 2024-03-11T10:47:41Z | - |
dc.date.issued | 2023-03-29 | - |
dc.identifier.citation | Diagnostics, 2023, v. 13, n. 7 | - |
dc.identifier.issn | 2075-4418 | - |
dc.identifier.uri | http://hdl.handle.net/10722/340839 | - |
dc.description.abstract | <p>Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.</p> | - |
dc.language | eng | - |
dc.publisher | MDPI | - |
dc.relation.ispartof | Diagnostics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | early detection | - |
dc.subject | non-laboratory-based | - |
dc.subject | pre-diabetes | - |
dc.subject | risk prediction tools | - |
dc.title | Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/diagnostics13071294 | - |
dc.identifier.scopus | eid_2-s2.0-85152666117 | - |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 7 | - |
dc.identifier.eissn | 2075-4418 | - |
dc.identifier.isi | WOS:000973114800001 | - |
dc.identifier.issnl | 2075-4418 | - |