Citations

AbstractObjectiveTo identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.Materials and MethodsUsing EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.ResultsUsing the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.ConclusionComputationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.

The functional lumen imaging probe (FLIP) measures luminal dimensions using impedance planimetry, performed most often during sedated upper endoscopy. Mechanical properties of the esophageal wall and opening dynamics of the esophagogastric junction (EGJ) can be objectively evaluated in esophageal motor disorders, eosinophilic esophagitis, esophageal strictures, during esophageal surgery and in postsurgical symptomatic states. Distensibility index, the ratio of EGJ cross sectional area to intraballoon pressure, is the most useful FLIP metric. Secondary peristalsis from balloon distension can be displayed topographically as repetitive anterograde or retrograde contractile activity in the esophageal body, similar to high-resolution manometry. Real-time interpretation and postprocessing of FLIP metadata can complement the identification of esophageal outflow obstruction and achalasia, especially when findings are inconclusive from alternate esophageal tests in symptomatic patients. FLIP can complement the diagnosis of achalasia when manometry and barium studies are inconclusive or negative in patients with typical symptoms. FLIP can direct adequacy of disruption of the EGJ in achalasia when used during and immediately after myotomy and pneumatic dilation. Lumen diameter measured using FLIP in eosinophilic esophagitis and in complex strictures can potentially guide management. An abbreviated modification of the Grading of Recommendations Assessment, Development, and Evaluation was used to determine the quality of available evidence and recommendations regarding FLIP utilization. FLIP metrics that are diagnostic or suggestive of an abnormal motor pattern and metrics that define normal esophageal physiology were developed by consensus and are described in this review.