Response to ‘Use of an algorithm for identifying hidden drug–drug interactions in adverse event reports’ by Gooden et al

Critical evaluation of the results of clinical studies is vital to the continued progress of medicine. We appreciate the work performed by Gooden and colleagues¹ to evaluate the clinical significance of a drug interaction between paroxetine, a selective serotonin reuptake inhibitor, and pravastatin, a cholesterol-lowering statin, that we published previously.² Our results demonstrated a 18.5 mg/dl increase in glucose levels in individuals without diabetes, and a 48 mg/dl increase in glucose level for diabetes patients using three electronic medical record systems. In the study, Gooden et al¹ did not find a difference in the development of type 2 diabetes using administrative data. We agree that retrospective risk estimates such as ours may be influenced by selection biases, such as confounding by indication. However, in our replication and validation study³ we did not see increased glucose measurements for patients on other combinations of selective serotonin reuptake inhibitors and statins or for the two classes generally—patients who are expected to have the same comorbidities. We were also not able to identify any clinical reason for the existence of clinical confounders for this particular combination of drugs alone. Moreover, we note that prediabetic mice clearly showed a positive biological result and would not be subject to the same possible confounders as the human studies.³

The authors correctly point out that an increase in non-fasting blood glucose measurements may not lead to a clinically significant event, such as type 2 diabetes mellitus (T2DM). It is possible that the increase in random glucose is not sufficiently large result in a patient being newly diagnosed with diabetes. Moreover, our findings were for near-term changes in glucose; it is possible that over the longer term, glucose falls back to normal. This would require further investigation. Finally, patients with T2DM may have the disease for some time before a diagnosis is made. It is possible that the patients enrolled in the study by Gooden et al¹ had not been observed long enough to note the development of diabetes if in fact such an observation does exist.

To assess the clinical significance of the drug interaction Gooden et al¹ evaluated the onset of new T2DM in all patients 18 years or older using claims data. Although administrative data constitute a powerful tool for evaluating disease, accrual of a single billing code for T2DM can falsely label patients as having diabetes (false positives) as well as also falsely excluding others as not having the disease (false negatives). For this reason, Ritchie et al⁴ and Kho et al⁵ both used phenotype algorithms for T2DM including laboratory values, medications, and diagnosis billing codes (also see PheKB.org). Using claims data alone may introduce too much noise and undermine the interpretation of the authors’ analysis.

Gooden et al¹ correctly point out that non-fasting glucose values have high variance and are not uniformly collected for all patients. For this reason we performed a paired analysis that required a patient to have glucose laboratory tests run both before and after they began combination treatment with paroxetine and pravastatin.³ We found flat glucose measurements for the single-drug-only groups, which indicate that the variability in glucose laboratory tests is not enough to explain the divergence we see in patients on the combination.³

We fully agree with the authors closing sentiment that there should be careful separation of hypothesis generation (in our case an analysis of the US Food and Drug Administration's adverse event reporting system) and hypothesis testing (in our case replication in three electronic health record systems and validation in a mouse model). It is clear that evaluating the clinical significance of this interaction between these two commonly used drugs will require a deeper understanding of its mechanism, as well as the long-term consequences of exposure.