Background Idiosyncratic drug induced liver injury (DILI) is an uncommon but important cause of liver disease that is challenging to diagnose and identify in the electronic medical record (EMR). Aim To develop an accurate, reliable, and efficient method of identifying patients with bonafide DILI in an EMR system. Methods 527,000 outpatient and ER encounters in an EPIC-based EMR were searched for potential DILI cases attributed to 8 drugs. A searching algorithm that extracted 200 characters of text around 14 liver injury terms in the EMR were extracted and collated. Physician investigators reviewed the data outputs and used standardized causality assessment methods to adjudicate the potential DILI cases. Results A total of 101 DILI cases were identified from the 2,564 potential DILI cases that included 62 probable DILI cases, 25 possible DILI cases, 9 historical DILI cases, and 5 allergy only cases. Elimination of the term “liver disease” from the search strategy improved the search recall from 4% to 19% while inclusion of the 4 highest yield liver injury terms further improved the positive predictive value to 64% but reduced the overall case detection rate by 47%. RUCAM scores of the 57 probable DILI cases were generally high and concordant with expert opinion causality assessment scores. Conclusions A novel text searching tool was developed that identified a large number of DILI cases from a widely used EMR system. A computerized extraction of dictated text followed by the manual review of text snippets can rapidly identify bonafide cases of idiosyncratic DILI.

Purpose: The authors sought to examine relationships between CT metrics derived via an automated method and clinical parameters of extraocular muscle changes in thyroid eye disease (TED). Methods: CT images of 204 orbits in the setting of TED were analyzed with an automated segmentation tool developed at the institution. Labels were applied to orbital structures of interest on the study images, which were then registered against a previously established atlas of manually indexed orbits derived from 35 healthy individuals. Point-wise correspondences between study and atlas images were then compared via a fusion algorithm to highlight metrics of interest where TED orbits differed from healthy orbits. Results: Univariate analysis demonstrated several correlations between CT metrics and clinical data. Metrics pertaining to the extraocular muscles—including average diameter, maximum diameter, and muscle volume—were strongly correlated (p < 0.05) with the presence of ocular motility deficits with regards to the superior, inferior, and lateral recti (with exception of superior rectus motility deficits being mildly correlated with muscle volume [p = 0.09]). Motility defects of the medial rectus were strongly correlated with muscle volume, and only weakly correlated with average and maximum muscle diameter. Conclusions: The novel method of automated imaging metrics may provide objective, rapid clinical information which may have utility in prevention and recognition of visual impairments in TED before they reach an advanced or irreversible stage and while they are able to be improved with immunomodulatory treatments.