A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

Abstract

Phenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms.

A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury

“…Therefore, additional studies in other EMR databases are needed to confirm the optimal search strategy. The results of our study are similar to those of Overby et al who used a more complex computerized algorithm involving hierarchical searching methods 11 . In their study, a series of liver injury terms that were initially developed in a retrospective dataset of EMR's were shown to perform with a high degree of sensitivity and specificity for DILI cases in a prospective manner.…”

“…Investigators have used a variety of methods to search computerized EMR and administrative databases for potential DILI cases with varying success 8-11 . In the current study, a novel text searching method was developed to peruse a large number of outpatient and ER encounters contained in a widely used commercial EMR product (EPIC) at a large tertiary care center.…”

“…We previously demonstrated that using common ICD-9 codes for general liver injury such as “jaundice” (782.4) and “toxic hepatitis” (573.3) that were cross-linked with a text search of the EMR for specific drugs has a poor sensitivity and specificity for identifying patients with idiosyncratic DILI 10 . Other studies using more sophisticated search algorithms of dictated text in the EMR for liver injury terms in combination with specific drug names, diagnosis codes, and laboratory values improved the accuracy of identifying bonafide DILI cases 11 . However, these approaches required extensive computer software development that may not be generalizable.…”

Is Making a Prognosis for Patients With Drug-induced Liver Injury Putting the Cart Before the Horse?

“…16 The incorporation of laboratory, pharmacy, and pathology data hold even greater promise in improving the yield of these electronic searching strategies. 17 In the study by Re et al, 7 a series of surrogate ICD-9 codes for presumed DILI were used followed by an "expert opinion Q10 " review of 298 individual charts that met the laboratory criteria for potential ALF. Of note, the authors previously reported on the poor performance of various ICD-9 codes to identify ALF patients in the same data set.…”

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

“…Examples of the combined use of standard NLP and text-and data-mining are found in [139][140][141] where cTAKES is used with Boolean logic to perform phenotyping and to extract drug-side effects. MedLEE was applied for: 1) adverse drug reaction (ADR) signaling, where the association between a drug and an ADR was obtained by using disproportionality analysis [142,143] or Boolean logic [144], or by building and analyzing statistical distributions of concepts (i.e., diseases, symptoms, medications) extracted from the narrative text [145]; 2) EHR-data driven phenotyping using Boolean logic on MedLEE-extracted concepts [136,146]; 3) automated classification of outcomes from the analysis of emergency department computed tomography imaging reports using machine learning methods, such as decision trees [147]. MetaMap has been used with logistic regression in [148] to discover inappropriate use of emergency room based on information on drugs, psychological characteristics, diagnoses, and symptoms.…”