Identifying Drugs Inducing Prematurity by Mining Claims Data with High-Dimensional Confounder Score Strategies

Pregnant women are largely exposed to medications. However, knowledge is lacking about their effects on pregnancy and the fetus.

This study sought to evaluate the potential of high-dimensional propensity scores and high-dimensional disease risk scores for automated signal detection in pregnant women from medico-administrative databases in the context of drug-induced prematurity.

We used healthcare claims and hospitalization discharges of a 1/97th representative sample of the French population. We tested the association between prematurity and drug exposure during the trimester before delivery, for all drugs prescribed to at least five pregnancies. We compared different strategies (1) for building the two scores, including two machine-learning methods and (2) to account for these scores in the final logistic regression models: adjustment, weighting, and matching. We also proposed a new signal detection criterion derived from these scores: the p value relative decrease. Evaluation was performed by assessing the relevance of the signals using a literature review and clinical expertise.

Screening 400 drugs from a cohort of 57,407 pregnancies, we observed that choosing between the two machine-learning methods had little impact on the generated signals. Score adjustment performed better than weighting and matching. Using the p value relative decrease efficiently filtered out spurious signals while maintaining a number of relevant signals similar to score adjustment. Most of the relevant signals belonged to the psychotropic class with benzodiazepines, antidepressants, and antipsychotics.

Mining complex healthcare databases with statistical methods from the high-dimensional inference field may improve signal detection in pregnant women.