Srinivasan V Iyer

Proton Pump Inhibitor Usage and the Risk of Myocardial Infarction in the General Population.

Background and Aims Proton pump inhibitors (PPIs) have been associated with adverse clinical outcomes amongst clopidogrel users after an acute coronary syndrome. Recent pre-clinical results suggest that this risk might extend to subjects without any prior history of cardiovascular disease. We explore this potential risk in the general population via data-mining approaches. Methods Using a novel approach for mining clinical data for pharmacovigilance, we queried over 16 million clinical documents on 2.9 million individuals to examine whether PPI usage was associated with cardiovascular risk in the general population. Results In multiple data sources, we found gastroesophageal reflux disease (GERD) patients exposed to PPIs to have a 1.16 fold increased association (95% CI 1.09–1.24) with myocardial infarction (MI). Survival analysis in a prospective cohort found a two-fold (HR = 2.00; 95% CI 1.07–3.78; P = 0.031) increase in association with cardiovascular mortality. We found that this association exists regardless of clopidogrel use. We also found that H2 blockers, an alternate treatment for GERD, were not associated with increased cardiovascular risk; had they been in place, such pharmacovigilance algorithms could have flagged this risk as early as the year 2000. Conclusions Consistent with our pre-clinical findings that PPIs may adversely impact vascular function, our data-mining study supports the association of PPI exposure with risk for MI in the general population. These data provide an example of how a combination of experimental studies and data-mining approaches can be applied to prioritize drug safety signals for further investigation.

Pharmacovigilance Using Clinical Notes

With increasing adoption of electronic health records (EHRs), there is an opportunity to use the free‐text portion of EHRs for pharmacovigilance. We present novel methods that annotate the unstructured clinical notes and transform them into a deidentified patient–feature matrix encoded using medical terminologies. We demonstrate the use of the resulting high‐throughput data for detecting drug–adverse event associations and adverse events associated with drug–drug interactions. We show that these methods flag adverse events early (in most cases before an official alert), allow filtering of spurious signals by adjusting for potential confounding, and compile prevalence information. We argue that analyzing large volumes of free‐text clinical notes enables drug safety surveillance using a yet untapped data source. Such data mining can be used for hypothesis generation and for rapid analysis of suspected adverse event risk.

Mining clinical text for signals of adverse drug-drug interactions

BACKGROUND AND OBJECTIVE Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs. METHODS We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods. RESULTS Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus. CONCLUSIONS It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.

Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes

BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data—in particular the clinical notes—it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records.

Toward personalizing treatment for depression: predicting diagnosis and severity

Objective Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment. Materials and methods We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute. Results Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy. Conclusions It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.

Practice-Based Evidence: Profiling the Safety of Cilostazol by Text-Mining of Clinical Notes

Background Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF). Methods and Results We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1∶5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients. Conclusions This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover ‘natural experiments’ such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

Automated Detection of Off-Label Drug Use

Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drugs FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications. Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks. Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets. Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.