Development of a detection algorithm for statin-induced myopathy using electronic medical records

Abstract

What is known and Objective: Demonstration of the utility of electronic medical records (EMRs) for pharmacovigilance (PV) has been highly anticipated. Analysis using appropriately selected EMRs should enable accurate estimation of adverse drug event (ADE) frequencies and thus promote appropriate regulatory actions. Statin-induced myopathy (SIM) is a clinically important ADE, but pharmacoepidemiological methodology for detecting this ADE with high predictability has not yet been established. This study aimed to develop a detection algorithm, highly selective for SIM using EMRs. Methods: We collected EMRs on prescriptions, laboratory tests, diagnoses and medical practices from the hospital information system of Kobe University Hospital, Japan, for a total of 5109 patients who received a statin prescription from April 2006 to March 2009. The current algorithm for extracting SIM-suspected patients consisted of three steps: (i) event detection: increase in creatine kinase (CK) and subsequent statin discontinuation, (ii) filtration by exclusion factors (disease diagnosis/medical practices) and (iii) refinement by the time course of CK values (baseline, event and recovery). A causal relationship between the event and statin prescription (probable/possible/unlikely) was judged by review of patient medical charts by experienced pharmacists. The utility of the current algorithm was assessed by calculating the positive predictive value (PPV). In a comparative analysis, subjects screened in step 1 were extracted by the diagnostic term/code for 'myopathy/rhabdomyolysis', and the PPV of this diagnostic data approach was also estimated. Results and Discussion: Five subjects with suspected SIM were identified using our proposed algorithm, giving a frequency of 0Á1% for the adverse event. Review of the medical charts revealed that the causal association of SIM with statin use was judged as 'Likely (probable/possible)' for all five suspected patients; thus, the PPV was estimated as 100% (95% confidence interval: 56Á6-100%). The higher utility of the current algorithm compared with the diagnostic data approach was also shown by assessing the PPV (100 vs. 33Á3%). What is new and Conclusion:We report on a detection algorithm with high predictability for SIM using EMRs. Combined use of exclusion criteria for disease, medical practice data and time course of CK values contributes to better prediction of SIM. The utility of the proposed algorithm should be further confirmed in a larger study.

Development of a novel algorithm for detecting glucocorticoid-induced diabetes mellitus using a medical information database

“…On the other hand, in the last 10 years, medical information databases (MIDs) or electronic medical records (EMRs), based on hospital information systems, have been widely used in many countries for PMS studies to identify adverse events. Detection algorithms for adverse reactions using MIDs have been developed by many researchers . Haerian et al .…”

“…developed an automated method to accurately detect two serious adverse events, rhabdomyolysis and agranulocytosis from EMR data. We previously developed a detection algorithm for statin‐induced myopathy, heparin‐induced thrombocytopenia and drug‐induced liver injury . Hence, MIDs are becoming an important tool for pharmacovigilance, and the development of accurate methods for detecting adverse reactions using MIDs is important in PMS, particularly for often overlooked adverse reactions such as GIDM.…”

Validity of diagnostic codes and laboratory measurements to identify patients with idiopathic acute liver injury in a hospital database

“…Search algorithms are more and more used for this purpose and can be highly effective in detecting patients. Such algorithms can be based on diagnostic codes which are generally recorded at hospital discharge using the International Classification of Diseases (ICD). Accuracy and completeness of the diagnostic codes within databases are crucial but depend on multiple factors, such as coding quality and information in the patient records .…”

Phenotype Standardization for Statin-Induced Myotoxicity

“…In addition, electronic medical records linked to biological repositories or to individual patients have been useful in identifying and recruiting patients with drug-induced toxicities. 6,8,9,10,11,12 The Phenotype Standardization Project was started a few years ago to facilitate such multicenter research collaborations on various types of serious adverse drug reactions (ADRs). Phenotype consensus papers on drug-induced liver injury, skin injury, and torsade de pointes have been published by multidisciplinary groups of international experts.…”