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1000
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Abstract/Summary
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Background!#!The use of minimally and non-invasive monitoring systems (including continuous glucose monitoring) has increased rapidly over recent years. Up to now, it remains unclear how accurate devices can detect hypoglycaemic episodes. In this systematic review and meta-analysis, we assessed the diagnostic accuracy of minimally and non-invasive hypoglycaemia detection in comparison to capillary or venous blood glucose in patients with type 1 or type 2 diabetes.!##!Methods!#!Clinical Trials.gov, Cochrane Library, Embase, PubMed, ProQuest, Scopus and Web of Science were systematically searched. Two authors independently screened the articles, extracted data using a standardised extraction form and assessed methodological quality using a review-tailored quality assessment tool for diagnostic accuracy studies (QUADAS-2). The diagnostic accuracy of hypoglycaemia detection was analysed via meta-analysis using a bivariate random effects model and meta-regression with regard to pre-specified covariates.!##!Results!#!We identified 3416 nonduplicate articles. Finally, 15 studies with a total of 733 patients were included. Different thresholds for hypoglycaemia detection ranging from 40 to 100 mg/dl were used. Pooled analysis revealed a mean sensitivity of 69.3% [95% CI: 56.8 to 79.4] and a mean specificity of 93.3% [95% CI: 88.2 to 96.3]. Meta-regression analyses showed a better hypoglycaemia detection in studies indicating a higher overall accuracy, whereas year of publication did not significantly influence diagnostic accuracy. An additional analysis shows the absence of evidence for a better performance of the most recent generation of devices.!##!Conclusion!#!Overall, the present data suggest that minimally and non-invasive monitoring systems are not sufficiently accurate for detecting hypoglycaemia in routine use.!##!Systematic review registration!#!PROSPERO 2018 CRD42018104812.
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1000
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Hinweis
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DeepGreen-ID: 32ed5864588448e8826dffd79ee4f997 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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