Introduction Ehlers-Danlos syndrome (EDS), specifically the hypermobility type (hEDS), is associated with a variety of gastrointestinal (GI) conditions. This study aims to evaluate the prevalence of and factors associated with gut dysmotility in patients with hEDS. Methods This is a retrospective study of hEDS patients conducted at the Cleveland Clinic's Center for Personalized Genetic Healthcare between January 2007 and December 2017. Demographics, GI motility testing, endoscopic, and imaging data were extracted from the patients' charts. Results A total of 218 patients with hEDS were identified. Among them, 136 (62.3%) patients had at least one GI symptom at the time of EDS diagnosis. Motility testing was performed and reported in 42 (19.2%) patients. Out of them, five (11.9%) had esophageal dysmotility, 18 (42.8%) had gastroparesis, five (11.9%) had small bowel/colon altered transit time, and four (9.5%) had global dysmotility. In univariable analysis, patients with postural orthostatic tachycardia syndrome (POTS) [
Background: Recent guidelines have recommended screening for nonalcoholic fatty liver disease (NAFLD) and case finding of advanced disease with fibrosis in patients with type-2 diabetes (T2D). The aim of this study is to assess the accuracy of commonly used noninvasive scores to predict the presence of advanced fibrosis (AF) in a large cohort of diabetics in real-life settings. Patients and Methods: Using International Classification of Diseases, Ninth Revision (ICD-9) codes, all patients with the diagnosis of T2D who had a liver biopsy for suspected NAFLD between January 2000 and December 2015, were identified and analyzed. Patients with secondary causes of hepatic steatosis were excluded. AST/ALT ratio, aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4) index, and Nonalcoholic fatty liver disease Fibrosis Score (NFS) were calculated to predict advanced disease. Sensitivity, specificity, and area under the receiver operator curve were calculated and compared with liver biopsies to predict the overall accuracy of each score. Results: A total of 1319 patients with T2D underwent liver biopsy for suspected NAFLD. After exclusions, 1,157 subjects were included in the final analysis. Our cohort consisted of 64.6% females and 88.4% were whites. Overall, 85% of the population was overweight or obese (body mass index>25 kg/m2). Liver biopsy showed 31.7% with AF [Nonalcoholic Steatohepatitis Clinical Research Network (NASH-CRN) stage 3 to 4]. In comparison to liver biopsy, for the diagnosis of AF, AST/ALT>1.4, APRI>1.5, FIB-4>2.67, and NFS>0.676 had reasonable specificities of 84.2%, 97.4%, 69.9%, and 93% but poor sensitivities of 27.4%, 16.5%, 6.7%, and 44.1%, respectively. Even at lower cutoff values of AST/ALT≥1, APRI≥1, and FIB-4≥1.45 sensitivities remained low at 60.7%, 27.9%, and 72.6%, respectively, except for NFS ≥−1.455 with sensitivity of 94.6%, but at this cutoff, its specificity decreased to 16.9%. The area under the receiver operator curve to detect AF was 0.62, 0.74, 0.77, and 0.72, respectively. Conclusions: In this large cohort of adult patients with T2D and NAFLD, commonly used fibrosis scores had reasonable specificity, but poor sensitivity for detecting AF in diabetics. The development of reliable biomarkers for NAFLD/NASH in diabetics is urgently needed.
SARS-CoV2 is a novel coronavirus responsible for causing COVID-19, first identified in the city of Wuhan, China and officially declared a pandemic by the World Health Organization. SARS-CoV2 expresses high affinity to human ACE2 receptors, including within the gastrointestinal tract. Patients with COVID-19 exhibit a wide spectrum of GI symptoms including anorexia, nausea, vomiting, abdominal pain, and abnormal liver function tests. Pathogenesis behind gastrointestinal symptoms caused by SARS-CoV2 has been postulated to be multifactorial including disruption of the intestinal mechanical barrier integrity, alteration of the gut microbiome and systemic inflammatory response to the virus. SARS-CoV-2 RNA has also been found in stool samples of infected patients for a significantly longer period than in nasopharyngeal samples, though the implication of this finding is unclear at this time. Liver injury in patients with COVID-19 is usually mild, stemming from immune-mediated damage, drug induced hepatotoxicity, or ischemia from sepsis. Patients with pre-existing liver disease may be at a higher risk for hospitalization and mortality. Given the high degree of infectivity of this disease, healthcare providers will need to remain watchful for resurgence of this virus. Strict protocols should be implemented regarding hand hygiene, isolation, personal protective equipment, and appropriate disposal of waste. It is also imperative to identify patients with gastrointestinal symptoms at an early stage as these patients may have a prolonged course between symptom onset and viral clearance.
OBJECTIVES: To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers. DESIGN: Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality. SETTING: Emergency departments from four medical centers. PATIENTS: Patients diagnosed with COVID-19 based on International Classification of Diseases, 10th Revision codes and laboratory test results. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 (n = 332), with higher levels of albumin and bicarbonate, and profile 2 (n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort (n = 192), which demonstrated similar outcome differences. HTE was observed (p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54). CONCLUSIONS: In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.
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