Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data

2026-05-13

AI Summary

This study addresses the critical challenge of early detection of pregnancy-associated thrombotic microangiopathy (P-TMA), a rare yet life-threatening condition often masked by subtle laboratory abnormalities and physiological changes of pregnancy. For the first time, interpretable temporal machine learning is applied to predict P-TMA onset using 146 longitudinal prenatal laboratory parameters. A gradient boosting model, developed with cross-validation and stratified sampling, demonstrates strong predictive performance on an independent test set (AUROC 0.872, AUPRC 0.883, sensitivity 0.750, specificity 0.812). Global feature importance and distribution pattern analyses uncover potential early warning signals, such as cystatin C levels as early as gestational week 6, offering both high accuracy and clinical interpretability for timely intervention.

Abstract

Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentation remains challenging, as the associated laboratory abnormalities are subtle, multidimensional, and frequently masked by common physiological changes such as gestational thrombocytopenia and pregnancy-related proteinuria, thus overlapping heavily with benign obstetric and renal conditions. This complexity is poorly captured by univariate or rule-based approaches; however, it is addressable by machine learning, which can extract latent, time-dependent risk signatures from longitudinal clinical tests. Methods: This retrospective study included 300 pregnancies comprising 142 P-TMA cases and 158 controls. After exclusion of identifiers and non-informative variables, 146 longitudinal laboratory predictors were retained. Participants were divided into a training cohort (80%) and a held-out test cohort (20%) using stratified sampling. Five algorithms were evaluated: logistic regression, support vector machine with radial basis function kernel, random forest, extra trees, and gradient boosting. The final model was selected by mean cross-validated AUROC, refitted on the full training cohort, and evaluated once in the held-out test cohort. Interpretability analyses examined global feature importance and distributional patterns of leading predictors. Results: Gradient boosting was prespecified by cross-validation in the training cohort. The model achieved an AUROC of 0.872 (95% CI: 0.769-0.952) and an AUPRC of 0.883 (95% CI: 0.780-0.959) in a held-out test cohort, with sensitivity of 0.750 and specificity of 0.812. Conclusions: Longitudinal clinical laboratory tests obtained during routine care contained informative and clinically plausible signals for P-TMA risk. Notably, cystatin C at week 6 showed promise as an early monitoring indicator.

Problem

• Pregnancy-associated thrombotic microangiopathy
• Early risk prediction
• Longitudinal laboratory data
• Clinical interpretability
• Machine learning

Innovation

• interpretable machine learning
• longitudinal laboratory data
• pregnancy-associated thrombotic microangiopathy
• gradient boosting
• early risk prediction