Abstract
CONTEXT: Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.
OBJECTIVES: This work aimed to establish effective models to predict early GDM.
METHODS: Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.
RESULTS: A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).
CONCLUSIONS: We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.
| Original language | English |
|---|---|
| Pages (from-to) | e1191-e1205 |
| Number of pages | 15 |
| Journal | Journal of Clinical Endocrinology and Metabolism |
| Volume | 106 |
| Issue number | 3 |
| Early online date | 22 Dec 2020 |
| DOIs | |
| Publication status | Published - 31 Mar 2021 |
Bibliographical note
AcknowledgmentsWe thank all those who helped to collect the data and the graduate students who took part in the statistical analysis.
Financial Support: This work was supported by the National Key Research and Development Program of China (grant Nos.2018YFC1002804 and 2016YFC1000203), the National Natural Science Foundation of China (grant Nos. 81671412 and 81661128010), Program of Shanghai Academic Research Leader
(grant No. 20XD1424100), the Outstanding Youth Medical Talents of Shanghai Rising Stars of Medical Talent Youth Development Program, Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (grant No. 2019-12M-5-064), the Foundation of Shanghai Municipal Commission of Health and Family Planning (grant No. 20144Y0110), the Natural Science Foundation of Shanghai (grant Nos. 20511101900 and 20ZR1427200), the Shanghai Shenkang Hospital Development Center, the Clinical Technology Innovation Project (grant Nos. SHDC12019107), and the Clinical Skills Improvement Foundation of Shanghai Jiaotong University School of Medicine (grant No. JQ201717).
Keywords
- GDM
- early prediction
- machine learning models
- early pregnancy
- BMI
- thyroxine
- HEMOGLOBIN
- CLASSIFICATION
- RISK
- PREGNANCY
- 1ST
- DISCRIMINATION
- GLUCOSE
- INSULIN-RESISTANCE
- INTRAUTERINE EXPOSURE
- ASSOCIATION
Access to Document
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© The Author(s) 2020. Published by Oxford University Press on behalf of the Endocrine Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com