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External validation of GDM risk prediction models using a machine learning reciprocal model-exchange framework.

Mark Germaine1, Yitayeh Belsti2, Amy O'Higgins3

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External validation of gestational diabetes mellitus (GDM) prediction models using a reciprocal exchange approach showed decreased performance. This method facilitates essential external validation, improving GDM risk prediction model generalizability.

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Area of Science:

  • Machine Learning in Healthcare
  • Predictive Modeling
  • Reproductive Health Research

Background:

  • External validation of risk prediction models is crucial but hindered by data access challenges.
  • Gestational Diabetes Mellitus (GDM) prediction models require robust validation for reliable clinical use.
  • A novel reciprocal model-exchange approach was developed to overcome data access barriers for external validation.

Purpose of the Study:

  • To assess the robustness and generalizability of two independently developed GDM risk prediction models.
  • To demonstrate the utility of a reciprocal model-exchange framework for external validation.
  • To evaluate model performance and fairness across different populations.

Main Methods:

  • Two GDM risk prediction models (Monash CatBoost and DCU logistic-regression) were externally validated using a reciprocal model-exchange.
  • Models and data pre-processors were exchanged between Monash University (Australia) and Dublin City University (Ireland).
  • Performance was evaluated using discrimination (AUC), calibration, decision curve analysis, and fairness assessments across ethnic groups, parity, and prior GDM history.

Main Results:

  • Both models experienced a significant drop in performance upon external validation (Monash AUC: 0.93 to 0.77; DCU AUC: 0.82 to 0.69).
  • Systematic risk misestimation was observed, with models over or under-predicting GDM probabilities.
  • Performance varied across ethnic groups, with lower accuracy for Southeast/Northeast Asians. Performance improved with increasing parity and in women without prior GDM.

Conclusions:

  • External validation using the reciprocal model-exchange approach revealed decreased performance for both GDM prediction models.
  • Fairness evaluations highlighted specific subgroups (ethnicities, parity, prior GDM) needing attention for model recalibration.
  • The reciprocal model-exchange framework offers a viable solution for conducting essential external validations, advancing the field of risk prediction.