Leveraging health records to identify diagnoses associated with recurrent pregnancy loss across two medical centers

Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5-6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. In this study, we aim to identify diagnoses associated with RPL and generate hypotheses about RPL etiology utilizing electronic health record (EHR) data. We implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University EHR databases. In total, our study includes 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses are significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses have higher odds ratios for patients