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

Abstract

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.