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Prediction of gestational diabetes based on nationwide electronic health records

Abstract

Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring14. GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes5,6. Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.

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Fig. 1: Data and cohort characteristics.
Fig. 2: Predictive model evaluation.
Fig. 3: Shapley values-based interpretation of the model.
Fig. 4: Questionnaire-based prediction, and efficiency of the predictor as a GDM screening tool.

Data availability

The data that support the findings of this study originate from Clalit Health Services. Restrictions apply to the availability of these data and they are therefore not publicly available. Due to restrictions, these data can be accessed only by request to the authors and/or Clalit Health Services.

Code availability

The code that supports the findings of this study is tailored to the data and the fields of the Clalit Health Services database, and is thus not provided since it is of no use as a standalone without access to the data per se. The algorithmic models used the standard Python code package scikit-learn, which is publicly available.

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Acknowledgements

We thank G. Barabash, E. Barkan, I. Kalka and members of the Segal group for discussions. E.S. is supported by the Crown Human Genome Center, by D. L. Schwarz, J. N. Halpern and L. Steinberg, and by grants funded by the European Research Council and the Israel Science Foundation.

Author information

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Authors

Contributions

N.S.A., S.S. and E.H. conceived the project, designed and conducted the analyses, interpreted the results and wrote the manuscript, and are listed in random order. H.R. conducted the analyses and wrote the manuscript. S.B.-H., A.B.-H., R.D.B. and B.F. interpreted the results. A.W. and E.S. conceived and directed the project and analyses, designed the analyses, interpreted the results, wrote the manuscript and supervised the project.

Corresponding authors

Correspondence to Arnon Wiznitzer or Eran Segal.

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The authors declare no competing interests.

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Peer review information Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Baseline prediction, based on Baseline Risk Score.

a: Odds ratio for the risk score composing parameters. Adjusted odds ratios were derived from a logistic regression model, both values are presented on the training set. b: Prevalence among women grouped by risk score. Error bars represent 90% confidence intervals on the train set. c: Histogram of risk scores in the training set. d: ROC curve for NIH Risk Score and for a logistic regression model trained on its constructing parameters. Results are reported on the future validation set. Logistic regression model does not suppress the Naive summation in the risk score. (n = 82,678 for all panels).

Extended Data Fig. 2 Evaluation of the model on the geographical validation set.

a: Receiver Operating Characteristic (ROC) curve, comparing our model (solid) and the Baseline Risk Score (dashed). Lighter colored lines are ROC curves of stratified partition of the validation set (not shown in ROC); bracketed values are 95% confidence intervals calculated through a normal fit of those curves. b: Precision-Recall (PR) curve, with the same properties as in A. c: The fraction of GDM-positive samples in every decile of the predicted probability. d: Predictions on different subsets of the cohort. auPR is shown for each subset, for our model (blue) and the baseline score (orange). Error bars show 95% confidence intervals, and dark blue lines show the prevalence in each subset. Shaded area is the distribution of the relevant score. e: Performance by gestational age at prediction. Every point is the evaluation score of a model built only with features available at this time point. (n = 46,002 for panels A-C. Subset sample sizes are listed in panel D).

Extended Data Fig. 3 Evaluation of the model on the geo-temporal validation set.

a: Receiver Operating Characteristic (ROC) curve, comparing our model (solid) and the Baseline Risk Score (dashed). Lighter colored lines are ROC curves of stratified partition of the validation set; bracketed values are 95% confidence intervals calculated through a normal fit of those curves. b: Precision-Recall (PR) curve, with the same properties as in A. c: The fraction of GDM-positive samples in every decile of the predicted probability. d: Predictions on different subsets of the cohort. auPR is shown for each subset, for our model (blue) and the baseline score (orange). Error bars show 95% confidence intervals, and dark blue lines show the prevalence in each subset. Shaded area is the distribution of the relevant score. e: Performance by gestational age at prediction. Every point is the evaluation score of a model built only with features available at this time point. (n = 8,540 for panels A-C. Subset sample sizes are listed in panel D).

Extended Data Fig. 4

Evaluation results in different validation sets.

Extended Data Fig. 5 Basic utility of the predictor.

a: Calibration curve, showing the fraction of positive samples per bin versus the mean predicted probability of the bin. Blue and red bars represent the ratio of negative/positive samples in the bin, respectively. b: Decision curve, showing the net benefit versus the threshold probability, for both predictor and baseline. The predictor outperforms the baseline at all thresholds. (n = 82,678 for all panels).

Extended Data Fig. 6 Additional dependence plots.

Top 20 features are shown (ordered left to right, top to bottom). In each the mean predicted relative risk is plotted versus feature value. Bands represent SD area of the population per bin, which is connected to interactions between input features. (n = 82,678).

Extended Data Fig. 7 Histogram of lab tests during pregnancy, showing the window definition of F0, F1 and F2.

The peaks showing are weekly, and represents the fact that patients tend to see a doctor in the same day of the week.

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Artzi, N.S., Shilo, S., Hadar, E. et al. Prediction of gestational diabetes based on nationwide electronic health records. Nat Med 26, 71–76 (2020). https://doi.org/10.1038/s41591-019-0724-8

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