Detection of Pharmacovigilance-Related Adverse Events Using Electronic Health Records and Automated Methods

Pharmacovigilance is an essential component of pharmaceutical safety.1 Traditional sources of information used for detecting new, rare, and serious adverse drug reactions (ADRs) are clinical trials, pharmaceutical industry reports, and adverse-event spontaneous reporting databases.2-4 Electronic health records (EHRs) contain occurrences of ADRs, yet few current techniques exist to directly extract these potential pharmacovigilance signals. Even with multiple layers of research and regulation, cases of medications requiring withdrawal from the market secondary to serious adverse reactions continue to emerge, confirming the need for new, complementary methods of detection. The mainstay for pharmacovigilance has been spontaneous reporting systems (SRSs), such as the US Food and Drug Administration’s Adverse Event Reporting System and VigiBase, the World Health Organization’s global Individual Case Safety Reports database.5,6 Limited information is available in standardized spontaneous reports.7 In addition, SRSs require the use of algorithms to estimate statistical measures of reporting frequency.8 Only a fraction of adverse drug events are identified and reported.9 The inherent limitations in SRSs, including underreporting, biased reporting rates, incomplete patient information, and indeterminate population exposure, creates a need for complementary data sources and methods.10 Others have advocated developing methods for pharmacovigilance signal detection from longitudinal observational databases such as EHRs.11,12 An advantage of EHR data over SRSs is the availability of more comprehensive medical information obtained during the usual course of care.13 EHRs typically contain elements of interest for pharmacovigilance such as the timing of medication administration, symptom development, and a detailed clinical history. Events are captured as part of standard of care, and it is therefore unnecessary to estimate a reporting frequency; this may result in a better estimation of the prevalence of ADRs. Current approaches to pharmacovigilance are beginning to recognize the advantage of utilizing supplementary sources of information such as EHRs; at the same time, the adoption of EHRs is increasing throughout the United States, potentially providing more data.14,15 EHRs need to be explored in a systematic way to augment and complement the information in spontaneous reporting databases. Extracting signals from EHR data for phamacovigilance requires methods to assist with the unique issues that arise from working with non-case report data.16 A paper by Ramirez et al. in Clinical Pharmacology & Therapeutics illustrated the preponderance of serious adverse drug reactions (SADRs) that can be identified in electronic medical records.17 The authors utilized abnormal laboratory signals (ALS), such as those consistent with a diagnosis of agranulocytosis, as a method of extracting data relating to patients with potential SADRs. However, because the majority of abnormal lab values resulted from the underlying disease condition in the patients rather than from their medications, intensive manual chart review was required to identify the occurrences of SADRs. Expert manual review is expensive in terms of manpower and time.18 The work by Ramirez et al. is an important demonstration of the use of EHRs for pharmacovigilance purposes. However, the method could be substantially improved if the abnormal lab values that are likely to have been caused by disease- or patient-related conditions rather than medication-related conditions are filtered out. For example, musculoskeletal trauma, convulsions, or arterial ischemia may cause rhabdomyolysis with no drug being involved in these events.19 In utilizing EHR data for signal detection, if an abnormal finding such as rhabdomyolysis is the basis for extracting data on potential patients, the SADR cases must be separated from those arising from other causes. Ramirez and colleagues’ study illustrated the preponderance of non-SADRs in the EHRs; they found that, in 70% of patients, an alternative cause (non-SADR) was responsible for the ALS.17 In terms of pharmacovigilance, this means that 70% of the cases that a knowledge expert spends time reviewing can clearly be disregarded as being non-SADRs. One approach for extracting comprehensive data from unstructured clinical notes, like those in EHRs, is to use natural-language processing (NLP). NLP systems can extract medication information, patient problem lists, and comprehensive clinical information.20-22 We used the Medical Language Extraction and Encoding System (MedLEE), a clinical NLP system developed at Columbia University.23 It identifies semantic structures and concepts, as well as modifiers such as time and certainty. MedLEE has been used previously for such applications as extracting adverse events from discharge summaries, assessing quality of care in cardiovascular disease, and performing automated knowledge acquisition.24-26 Our research was aimed at developing a method to substantially reduce the need for manual review and thereby increase the efficiency of the manual review process. The approach involves an automated method that combines NLP with an expert-generated knowledge source that we are creating, called the related disease identifier (RDI). This will enable researchers to quickly filter out, either prospectively or retrospectively, recurrent abnormal signals that are disease-related so that identification efforts can focus on finding potential ADRs. In the present study, we focused on patients with an ALS consistent with rhabdomyolysis or agranulocytosis. We then applied our automated method and assessed the sensitivity and specificity of the method by comparing the classification results to a gold standard generated by expert manual review by two physicians. In addition, we applied the method retrospectively to a 5-year time period. We present the generalized findings, including identification of several medications and combinations of medications that resulted in either of the two ADRs: rhabdomyolysis or agranulocytosis.