Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time

Abstract Background Male infertility (MI) is the sole cause of 20–30% of infertility cases, and it is a contributing factor for an additional 15–20% of cases. However, the full breadth of potential MI risk factors and adverse health outcomes has not been explored. Methods We used electronic health records (EHRs) from the University of California (UC) and Stanford to identify MI-associated comorbidities. We identified 6531 and 5551 MI patients at UC and Stanford, respectively, and 8353 and 2464 vasectomy control patients at UC and Stanford, respectively. Low-dimensional embeddings of patients’ diagnosis profiles based on MI status, demographics, or hospital utilization were compared using either Kruskal–Wallis tests followed by post hoc Dunn’s tests or Mann–Whitney U tests. We used logistic regression to identify MI-associated comorbidities prior to or after 6 months of a patient’s first MI or vasectomy-related record. Pearson correlation coefficients were used to compare primary versus sensitivity logistic regression analyses as well as UC versus Stanford logistic regression analyses. Cox regression was used to assess whether patients had a higher risk of receiving diagnoses significantly associated with MI after the 6-month cutoff at UC. Results Here, we identify 15 diagnoses that are positively associated with MI before the 6-month cutoff across both hospital systems and all analyses, including less expected comorbidities such as hypothyroidism and other anemias. Using Cox regression, we find that patients have a higher risk of receiving 11 out of 13 diagnoses positively associated with MI after the 6-month cutoff at UC. Conclusions Our findings can set the groundwork for future studies to clarify the relationship between less expected comorbidities and MI.