Provider-specific quality measurement for ERCP using natural language processing

Background and Aims Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measures for colonoscopy. There are no systematic methods by which to track adherence to quality measures for ERCP, the highest risk endoscopic procedure widely used in practice. Our aim was to demonstrate the feasibility of using NLP to measure adherence to ERCP quality indicators across individual providers. Methods ERCPs performed by 6 providers at a single institution from 2006 to 2014 were identified. Quality measures were defined using society guidelines and from expert opinion, and then extracted using a combination of NLP and data mining (eg, ICD9-CM codes). Validation for each quality measure was performed by manual record review. Quality measures were grouped into preprocedure (5), intraprocedure (6), and postprocedure (2). NLP was evaluated using measures of precision and accuracy. Results A total of 23,674 ERCPs were analyzed (average patient age, 52.9 ± 17.8 years, 14,113 were women [59.6%]). Among 13 quality measures, precision of NLP ranged from 84% to 100% with intraprocedure measures having lower precision (84% for precut sphincterotomy). Accuracy of NLP ranged from 90% to 100% with intraprocedure measures having lower accuracy (90% for pancreatic stent placement). Conclusions NLP in conjunction with data mining facilitates individualized tracking of ERCP providers for quality metrics without the need for manual medical record review. Incorporation of these tools across multiple centers may permit tracking of ERCP quality measures through national registries

Lower provider volume is associated with higher failure rates for endoscopic retrograde cholangiopancreatography

BACKGROUND: Among physicians who perform endoscopic retrograde cholangiopancreatography (ERCP), the relationship between procedure volume and outcome is unknown. OBJECTIVE: Quantify the ERCP volume-outcome relationship by measuring provider-specific failure rates, hospitalization rates, and other quality measures. RESEARCH DESIGN: Retrospective cohort. SUBJECTS: A total of 16,968 ERCPs performed by 130 physicians between 2001 and 2011, identified in the Indiana Network for Patient Care. MEASURES: Physicians were classified by their average annual Indiana Network for Patient Care volume and stratified into low (<25/y) and high (≥25/y). Outcomes included failed procedures, defined as repeat ERCP, percutaneous transhepatic cholangiography or surgical exploration of the bile duct≤7 days after the index procedure, hospitalization rates, and 30-day mortality. RESULTS: Among 15,514 index ERCPs, there were 1163 (7.5%) failures; the failure rate was higher among low (9.5%) compared with high volume (5.7%) providers (P<0.001). A second ERCP within 7 days (a subgroup of failure rate) occurred more frequently when the original ERCP was performed by a low-volume (4.1%) versus a high-volume physician (2.3%, P=0.013). Patients were more frequently hospitalized within 24 hours when the ERCP was performed by a low-volume (28.3%) versus high-volume physician (14.8%, P=0.002). Mortality within 30 days was similar (low=1.9%, high=1.9%). Among low-volume physicians and after adjusting, the odds of having a failed procedure decreased 3.3% (95% confidence interval, 1.6%-5.0%, P<0.001) with each additional ERCP performed per year. CONCLUSIONS: Lower provider volume is associated with higher failure rate for ERCP, and greater need for postprocedure hospitalization

Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED)

Background: Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). Methods: We used a nested case-control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. Results: After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79-0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8-0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2. Conclusions: Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk

Genotype and Gene Expression Associations with Immune Function in Drosophila

It is now well established that natural populations of Drosophila melanogaster harbor substantial genetic variation associated with physiological measures of immune function. In no case, however, have intermediate measures of immune function, such as transcriptional activity of immune-related genes, been tested as mediators of phenotypic variation in immunity. In this study, we measured bacterial load sustained after infection of D. melanogaster with Serratia marcescens, Providencia rettgeri, Enterococcus faecalis, and Lactococcus lactis in a panel of 94 third-chromosome substitution lines. We also measured transcriptional levels of 329 immune-related genes eight hours after infection with E. faecalis and S. marcescens in lines from the phenotypic tails of the test panel. We genotyped the substitution lines at 137 polymorphic markers distributed across 25 genes in order to test for statistical associations among genotype, bacterial load, and transcriptional dynamics. We find that genetic polymorphisms in the pathogen recognition genes (and particularly in PGRP-LC, GNBP1, and GNBP2) are most significantly associated with variation in bacterial load. We also find that overall transcriptional induction of effector proteins is a significant predictor of bacterial load after infection with E. faecalis, and that a marker upstream of the recognition gene PGRP-SD is statistically associated with variation in both bacterial load and transcriptional induction of effector proteins. These results show that polymorphism in genes near the top of the immune system signaling cascade can have a disproportionate effect on organismal phenotype due to the amplification of minor effects through the cascade