Do Proton Pump Inhibitors increase the risk of Myocardial Infarction? -- insights from mining clinical notes



The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions. As adoption of electronic health records increases, we present an approach based on analyzing the unstructured clinical notes, which can enable rapid pharmacovigilance. Using this approach we find that proton pump inhibitors (PPIs) as a class appear strongly associated with major adverse cardiovascular events, increasing the risk of myocardial infarction by 20-50% depending upon the individual PPI. The association of PPIs with such events was hypothesized based on experimental results that show that PPIs, as a class, elevate plasma levels of asymmetric dimethylarginine, a disease marker and an independent predictor of major adverse cardiovascular events.

We show that it is possible to investigate adverse drug event associations with high accuracy (72% sensitivity, 83% specificity) by analyzing textual notes in a clinical data warehouse using ontology-driven methods. We examine suspected associations for confounding via stratification and propensity score matching. We find that such an analysis of textual clinical notes could detect adverse drug events 2 years before the official alert. We argue that data-mining of unstructured clinical notes may expand meaningful use of the electronic health records for post-marketing drug surveillance and for rapid retrospective analysis of adverse event risk elucidated by experimental methods, such as in our case study on proton pump inhibitors.


Dr. Nigam H. Shah is an Assistant Professor of Medicine (Biomedical Informatics) at the Stanford School of Medicine. Dr. Shah's research is focused on developing applications of bio-ontologies, specifically building novel approaches to annotate, index, integrate and analyze diverse information types available in biomedicine. Dr. Shah holds an MBBS from Baroda Medical College, India, a PhD from Penn State University, USA and completed post-doctoral training at the Stanford Medical School.