Journal
JOURNAL OF MEDICAL INTERNET RESEARCH
Volume 24, Issue 3, Pages -Publisher
JMIR PUBLICATIONS, INC
DOI: 10.2196/26634
Keywords
digital health; gestational diabetes mellitus; machine learning; prediction model; prognostic model
Funding
- National Key R&D Program of China [2018YFC1314900, 2018YFC1314902]
- Nantong 226 Project, Excellent Key Teachers in the Qing Lan Project of Jiangsu Colleges and Universities, Science and Technology Project of Nantong City [MS12020037]
- Jiangsu Students' Platform for Innovation and Entrepreneurship Training Program [201910304108Y]
- Jiangsu Postgraduate Research and Innovation Program [KYCX20_2836]
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This study conducted a meta-analysis and comparison of published prognostic models for predicting the risk of gestational diabetes mellitus (GDM) and identified several predictors applicable to these models. The results showed that machine learning models are more attractive than current screening strategies for predicting GDM, but the importance of quality assessments and unified diagnostic criteria should be further emphasized.
Background: Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. Objective: The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. Methods: Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. Results: A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I-2 =99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I-2 =100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non-logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. Conclusions: Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.
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