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

Formal verification of CNL health recommendations

This research is partially supported by EPSRC grant EP/M014290/1.Clinical texts, such as therapy algorithms, are often described in natural language and may include hidden inconsistencies, gaps and potential deadlocks. In this paper, we propose an approach to identify such problems with formal verification. From each sentence in the therapy algorithm we automatically generate a parse tree and derive case frames. From the case frames we construct a state-based representation (in our case a timed automaton) and use a model checker (here UPPAAL) to verify the model. Throughout the paper we use an example of the algorithm for blood glucose lowering therapy in adults with type 2 diabetes to illustrate our approach.Postprin

Beta defensin-2 is reduced in central but not in distal airways of smoker COPD patients

Background: Altered pulmonary defenses in chronic obstructive pulmonary disease (COPD) may promote distal airways bacterial colonization. The expression/activation of Toll Like receptors (TLR) and beta 2 defensin (HBD2) release by epithelial cells crucially affect pulmonary defence mechanisms. Methods: The epithelial expression of TLR4 and of HBD2 was assessed in surgical specimens from current smokers COPD (s-COPD; n = 17), ex-smokers COPD (ex-s-COPD; n = 8), smokers without COPD (S; n = 12), and from non-smoker non-COPD subjects (C; n = 13). Results: In distal airways, s-COPD highly expressed TLR4 and HBD2. In central airways, S and s-COPD showed increased TLR4 expression. Lower HBD2 expression was observed in central airways of s-COPD when compared to S and to ex-s-COPD. s-COPD had a reduced HBD2 gene expression as demonstrated by real-time PCR on micro-dissected bronchial epithelial cells. Furthermore, HBD2 expression positively correlated with FEV1/FVC ratio and inversely correlated with the cigarette smoke exposure. In a bronchial epithelial cell line (16 HBE) IL-1β significantly induced the HBD2 mRNA expression and cigarette smoke extracts significantly counteracted this IL-1 mediated effect reducing both the activation of NFkB pathway and the interaction between NFkB and HBD2 promoter. Conclusions: This study provides new insights on the possible mechanisms involved in the alteration of innate immunity mechanisms in COPD. © 2012 Pace et al

A shared role for RBF1 and dCAP-D3 in the regulation of transcription with consequences for innate immunity

Previously, we discovered a conserved interaction between RB proteins and the Condensin II protein CAP-D3 that is important for ensuring uniform chromatin condensation during mitotic prophase. The Drosophila melanogaster homologs RBF1 and dCAP-D3 co-localize on non-dividing polytene chromatin, suggesting the existence of a shared, non-mitotic role for these two proteins. Here, we show that the absence of RBF1 and dCAP-D3 alters the expression of many of the same genes in larvae and adult flies. Strikingly, most of the genes affected by the loss of RBF1 and dCAP-D3 are not classic cell cycle genes but are developmentally regulated genes with tissue-specific functions and these genes tend to be located in gene clusters. Our data reveal that RBF1 and dCAP-D3 are needed in fat body cells to activate transcription of clusters of antimicrobial peptide (AMP) genes. AMPs are important for innate immunity, and loss of either dCAP-D3 or RBF1 regulation results in a decrease in the ability to clear bacteria. Interestingly, in the adult fat body, RBF1 and dCAP-D3 bind to regions flanking an AMP gene cluster both prior to and following bacterial infection. These results describe a novel, non-mitotic role for the RBF1 and dCAP-D3 proteins in activation of the Drosophila immune system and suggest dCAP-D3 has an important role at specific subsets of RBF1-dependent genes

Tissue-specific inducible expression of antimicrobial peptide genes in Drosophila surface epithelia

The production of antimicrobial peptides is an important aspect of host defense in multicellular organisms. In Drosophila, seven antimicrobial peptides with different spectra of activities are synthesized by the fat body during the immune response and secreted into the hemolymph. Using GFP reporter transgenes, we show here that all seven Drosophila antimicrobial peptides can be induced in surface epithelia in a tissue-specific manner. The imd gene plays a critical role in the activation of this local response to infection. In particular, drosomycin expression, which is regulated by the Toll pathway during the systemic response, is regulated by imd in the respiratory tract, thus demonstrating the existence of distinct regulatory mechanisms for local and systemic induction of antimicrobial peptide genes in Drosophila

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

A New Immortalized Human Alveolar Epithelial Cell Model to Study Lung Injury and Toxicity on a Breathing Lung-On-Chip System

The evaluation of inhalation toxicity, drug safety and efficacy assessment, as well as the investigation of complex disease pathomechanisms, are increasingly relying on in vitro lung models. This is due to the progressive shift towards human-based systems for more predictive and translational research. While several cellular models are currently available for the upper airways, modelling the distal alveolar region poses several constraints that make the standardization of reliable alveolar in vitro models relatively difficult. In this work, we present a new and reproducible alveolar in vitro model, that combines a human derived immortalized alveolar epithelial cell line ((AX)iAEC) and organ-on-chip technology mimicking the lung alveolar biophysical environment ((AX)lung-on-chip). The latter mimics key features of the in vivo alveolar milieu: breathing-like 3D cyclic stretch (10% linear strain, 0.2 Hz frequency) and an ultrathin, porous and elastic membrane. (AX)iAECs cultured on-chip were characterized for their alveolar epithelial cell markers by gene and protein expression. Cell barrier properties were examined by TER (Transbarrier Electrical Resistance) measurement and tight junction formation. To establish a physiological model for the distal lung, (AX)iAECs were cultured for long-term at air-liquid interface (ALI) on-chip. To this end, different stages of alveolar damage including inflammation (via exposure to bacterial lipopolysaccharide) and the response to a profibrotic mediator (via exposure to Transforming growth factor β1) were analyzed. In addition, the expression of relevant host cell factors involved in SARS-CoV-2 infection was investigated to evaluate its potential application for COVID-19 studies. This study shows that (AX)iAECs cultured on the (AX)lung-on-chip exhibit an enhanced in vivo-like alveolar character which is reflected into: 1) Alveolar type 1 (AT1) and 2 (AT2) cell specific phenotypes, 2) tight barrier formation (with TER above 1,000 Ω cm(2)) and 3) reproducible long-term preservation of alveolar characteristics in nearly physiological conditions (co-culture, breathing, ALI). To the best of our knowledge, this is the first time that a primary derived alveolar epithelial cell line on-chip representing both AT1 and AT2 characteristics is reported. This distal lung model thereby represents a valuable in vitro tool to study inhalation toxicity, test safety and efficacy of drug compounds and characterization of xenobiotics

Immune pathways and defence mechanisms in honey bees Apis mellifera

Social insects are able to mount both group-level and individual defences against pathogens. Here we focus on individual defences, by presenting a genome-wide analysis of immunity in a social insect, the honey bee Apis mellifera. We present honey bee models for each of four signalling pathways associated with immunity, identifying plausible orthologues for nearly all predicted pathway members. When compared to the sequenced Drosophila and Anopheles genomes, honey bees possess roughly one-third as many genes in 17 gene families implicated in insect immunity. We suggest that an implied reduction in immune flexibility in bees reflects either the strength of social barriers to disease, or a tendency for bees to be attacked by a limited set of highly coevolved pathogens