Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors

Yanqi Wu, Paul Hamelmann, Myrthe van der Ven, Sima Asvadi, M. Beatrijs van der Hout-van der Jagt, S. Guid Oei, Massimo Mischi, Jan Bergmans, Xi Long (Corresponding author)

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Samenvatting

OBJECTIVE: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

METHODS: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.

RESULTS: An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Originele taal-2Engels
Artikelnummer105
Aantal pagina's7
TijdschriftBMC Research Notes
Volume17
Nummer van het tijdschrift1
DOI's
StatusGepubliceerd - 15 apr. 2024

Financiering

We would like to thank Tineke Laurs from the M\u00E1xima Medical Center for providing administrative support regarding data acquisition and analysis. This work was performed within the framework of the Eindhoven MedTech Innovation Center (e/MTIC) and was supported by the China Scholarship Council (Grant No: 201906340168).

FinanciersFinanciernummer
China Scholarship Council201906340168

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