Citations

AbstractImpedance has traditionally been employed in esophageal disease as a means to assess bolus flow and reflux episodes. Recent and ongoing research has provided new and novel applications for this technology. Measurement of esophageal mucosal impedance, via either multichannel intraluminal impedance catheters or specially designed endoscopically deployed impedance catheters, provides a marker of mucosal integrity. Mucosal impedance has been shown to segregate gastroesophageal reflux disease (GERD) and eosinophilic esophagitis from non‐GERD controls and may play a role in predicting response to reflux intervention. More data are needed with regard to other esophageal subgroups, outcome studies, and functional disease. Our paper reviews the history of impedance in esophageal disease, the means of assessing baseline and mucosal impedance, data with regard to the newly developed mucosal impedance probes, the clinical utility of mucosal impedance in specific clinical conditions, and limitations in our existing knowledge, along with suggestions for future studies.

AbstractIntroductionElectronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping.MethodsWe selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.ResultsCQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms.ConclusionsCQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.