Azathioprine-Induced Comorbidity Network Reveals Patterns and Predictors of Nephrotoxicity and Neutrophilia

We sought to examine the frequencies and patterns of nephrotoxicity and neutrophilia due to azathioprine (AZA), and to develop a prototype method for using large de-identified electronic health record (EHR) data to aid in post-market drug surveillance. We leveraged a de-identified database of over 10 million patient EHRs to construct a network of comorbidities induced by administration of AZA, where comorbidities were defined by baseline-controlled laboratory values. To gauge the significance of the identified disease patterns, we calculated the relative risk of developing a comorbidity pair relative to a control cohort of patients taking one of 12 other anti-rheumatic agents. Nephrotoxicity as gauged by elevations in creatinine was present in 11% of patients taking AZA, and this frequency was significantly higher than in patients taking other anti-rheumatic agents (RR: 1.2, 95% CI: 1.04-1.43). Neutrophilia was highly prevalent (45%) in the population and was also unique to AZA (RR: 1.2, 95% CI: 1.17-1.28). Using a comorbidity network analysis, we hypothesized that the joint consideration of anemia (hemoglobin 190 IU/L) may serve as a predictor of impending renal dysfunction. Indeed, these two laboratory values provide approximately 100% sensitivity in predicting subsequent elevations in creatinine. Furthermore, the predictive power is unique to AZA, for jointly considering anemia and an elevated LDH provides only 50% sensitivity in predicting creatinine elevations with other anti-rheumatic agents. Our work demonstrates that the construction of comorbidity networks from de-identified EHR data sets can provide both sufficient insight and statistical power to uncover novel patterns and predictors of disease.