Using Long Short-Term Memory and Causal Forest to Identify Preeclampsia Subtypes with Differential Aspirin Associations for Preterm Birth Prevention

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Abstract
Aspirin prevents preterm birth in some women with preeclampsia but not others, yet no longitudinal multi-indicator study has examined subtype differences in aspirin response. We aimed to identify subtypes most likely to benefit. In this retrospective cohort of 53,362 deliveries (4505 preeclamptic women), we applied a two-stage machine learning framework using 15 indicators (14 laboratory tests plus systolic blood pressure) from two-time windows: before 16 weeks and within 2 weeks before delivery. A long short-term memory autoencoder with K-means clustering identified subtypes, and causal forest estimated the average treatment effect of aspirin on preterm birth (n = 345), characterised by mild liver enzyme elevation and coagulation abnormalities in late pregnancy, had the highest preterm birth rate (57.7%) and ICU admission rate (9.6%), and showed the largest aspirin-associated reduction in preterm birth (ATE −0.026, 95% CI: −0.032 to −0.021), consistent across 70/30 splits. Sensitivity analyses using 34 indicators confirmed similar effects (ATE −0.027, 95% CI: −0.031 to −0.023), while no benefit was observed in non-preeclamptic women. These findings suggest that aspirin prophylaxis may be associated with a greater reduction in preterm birth in this preeclampsia subtype, but further validation is required.
Funding Information
  • Natural Science Foundation of Beijing Municipality (7262141)
  • National Natural Science Foundation of China (82072352)

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