Search

Objective To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions. Materials and Methods A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU). Results The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4). Conclusion NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

Background Antimicrobial regimens evolve with changing recommendations and emerging practice patterns. We sought to explore the diversity of these patterns and to identify which inpatient regimens may be emerging in US Veterans Affairs medical centers (VAMC). Methods We extracted antimicrobial use and admission data from all acute care VA medical centers between 2005 and 2016. A regimen was defined as all unique antimicrobials and their routes given in a day to a single patient. We applied smoothing to account for intended discontinuation and intermittent dosing due to clearance. We described the distribution of regimens among VAMCs using the Gini index (a Gini index of 0 would mean all regimens were equally frequent and 1 would mean that one regimen dominated all others). We calculated the rank percentile of all regimens. We also used the absolute change in rank percentile between years 2005 and 2016 of the regimen used to describe emerging and declining regimens. Results There were 55,767 distinct regimens. Table 1 describes the Gini index and its decomposition among VAMCs. Overlap accounts for most of the inequality present because regimens are shared between VAMCs. Approximately 20% of the inequality present can be accounted for by variation between VAMCs. Table 2 describes the top 10 rising and the top 10 declining regimens. Conclusion While there was a large number of distinct regimens, there was a relative handful of antimicrobial regimens dominated—most of which were commonly present among VAMCs (as manifest by the Gini “overlap” percent). Most regimens in the top 10 were broad-spectrum IV agents, with PO levofloxacin and doxycycline being notable standouts. IV vancomycin, which was the single most common regimen in 2005, decreased markedly. Linezolid and mixed PO metronidazole agents appear to be on the decline. Disclosures V. Stevens, Pfizer, Inc.: Grant Investigator, Research grant.

Background Historically, Zika virus infection presented as a mild disease. However, more severe disease was reported during recent outbreaks in French Polynesia and more recently within southern regions of North America as well as Central and South America. It is still unclear what predicts more severe manifestations. Here we report on potential predictors of severe Zika virus infection within VA, selected for their role in immunological status. Methods We extracted the first positive Zika visit for a patient between February 9, 2016 and April 1, 2017. Each visit was classified by acuity (no ED visit, ED only, observation, ward, ICU [in this order of severity]). Diagnoses were extracted by ICD-9-CM and ICD-10-CM codes. Predictors included history of hepatitis C virus (HCV; a flavivirus) by laboratories, dengue diagnosis, immunocompromising condition diagnosis, gender, age, and history of exposure to dengue endemic region (either through birth, travel, or residency). These predictors were used in a generalized ordered logit model, relaxing the proportional odds assumption, to estimate odds ratios for a higher level of visit acuity over the current or lower levels of acuity. Robust covariance estimates were used. Results There were 748 unique patient visits meeting criteria. Distribution of predictors among the patient sample are shown in Table 1. As expected, most were males with a majority only visiting the ED. Wards and ICU were combined due to the small number of ICU visits. Table 2 shows results of model for predictors of higher acuity visits. Age was generally associated with higher levels of acuity. Odds ratios could not be computed for HCV and immunocompromised predictors. Conclusion There may be an increased risk of Zika disease severity based on age. We could not rule out associations with other predictors due to the size of our study. Further larger studies are needed to investigate these and other predictors. Disclosures All Authors: No reported disclosures.