Ray, Jordan

Objectives: Pulmonary arterial hypertension (PAH) is a deadly disease without any effective cure. Identification of the PAH-associated gut microbiome may shift conceptual paradigms of the disease that could lead to new therapeutic advancement. We tested the hypothesis that PAH patients have distinct gut microbiome composition contributing to PAH that predicts the disease. Methods: A total of 71 participants were recruited. Fecal microbiomes of 18 type 1 PAH patients (mean pulmonary arterial pressure [mPAP] 57.4±16.7 mmHg) and 13 reference subjects were compared by shotgun metagenomics methods. Plasma from 22 PAH patients (mPAP 48.3± 18.4 mmHg) and 18 reference subjects was used in untargeted metabolomic analysis. Results: Significant taxonomic and functional changes in the microbiome of the PAH cohort were observed. PAH patients had increased gut barrier dysfunction and inflammation as evidenced by plasma markers and metagenomics. A random forest model predicted PAH from gut microbiome composition with 82.6% accuracy. Changes in the gut microbiome were associated with purine metabolism, arginine biosynthesis, trimethylamine production and tryptophan biosynthesis, all previously associated with PAH. Area under the curve of the multi-variate receiver operating characteristics (ROC) plot for metabolites of these pathways had a predictive accuracy of 0.965. Conclusions: The gut microbiome/metabolites of PAH patients predict disease in random forest models and ROC analyses. The unique PAH gut microbiome influences carnitine, arginine and purine metabolisms contributing to pathophysiology. These highlight previously unknown roles of gut bacteria and their metabolites in PAH that could lead to new therapeutic or management paradigms.

Introduction: Our study assesses the utility of telemetry in identifying decompensation in patients with documented cardiopulmonary arrest. Methods: A retrospective review of inpatients who experienced a cardiopulmonary arrest from May 1, 2008, until June 30, 2014, was performed. Telemetry records 24 hours prior to and immediately preceding cardiopulmonary arrest were reviewed. Patient subanalyses based on clinical demographics were made as well as analyses of survival comparing patients with identifiable rhythm changes in telemetry to those without. Results: Of 242 patients included in the study, 75 (31.0%) and 110 (45.5%) experienced telemetry changes at the 24-hour and immediately preceding time periods, respectively. Of the telemetry changes, the majority were classified as nonmalignant (n = 50, 66.7% and n = 66, 55.5% at 24 hours prior and immediately preceding, respectively). There was no difference in telemetry changes between intensive care unit (ICU) and non-ICU patients and among patients stratified according to the American Heart Association telemetry indications. There was no difference in survival when comparing patients with telemetry changes immediately preceding and at 24 hours prior to an event (n = 30, 27.3% and n = 15, 20.0%) to those without telemetry changes during the same periods (n = 27, 20.5% and n = 42, 25.2%; P = .22 and .39). Conclusion: Telemetry has limited utility in predicting clinical decompensation in the inpatient setting.

Syncope is common representing approximately 3% of ED visits and up to 6% of hospital admissions, with a cost close to 2 billion dollars per year. Diagnostic testing is often poorly sensitive and evaluations commonly lack a standardized approach. A mindful and systematic approach can increase sensitivity and improve diagnostic accuracy. A thorough history and physical exam is paramount, as conclusions drawn from the history and exam will guide further assessment. Developing a strategy for the first and, if necessary, subsequent tests will improve the accuracy of identifying the etiology of syncope and reduce cost. Although syncope has a favorable prognosis, identification of patients with structural heart disease is critical, as these patients are at greatest risk for mortality. Several risk scoring systems have been developed to help separate high risk from low risk patients.