Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

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Existing guidelines performed suboptimally for predicting choledocholithiasis in our patient population, similar to other validation studies. These findings further underscore the importance of developing alternate risk stratification tools for choledocholithiasis, aiming to minimize unnecessary diagnostic ERCP.

Our large multicenter study indicates that LA-ERCP in patients with RYGB is feasible with a high procedure success rate comparable with that of standard ERCP in patients with normal anatomy. The ERCP-related adverse events rate is comparable with conventional ERCP, but the overall adverse event rate was higher because of the added laparoscopy-related events.

To discover whether tamoxifen is able to extend the survival of patients with advanced hepatocellular carcinoma, we included 80 patients with cirrhosis and advanced hepatocellular carcinoma in a multicenter, double-blind, placebo-controlled trial in order to analyze the influence of treatment with tamoxifen on survival. The patients were randomized to receive tamoxifen, 40 mg/day (group 1), or placebo (group 2). Both groups were similar in age, sex, etiology of cirrhosis, biochemical, hematologic and hormonal parameters, morphology of the tumor (nodular vs multinodular or massive), Child-Pugh's score, and Okuda's stage. The 1-year survival rate was similar in both groups (30% in group 1 vs 37.8% in group 2; p = 0.31). Tamoxifen treatment was well tolerated by the patients. We conclude that tamoxifen does not extend the survival of patients with cirrhosis and advanced hepatocellular carcinoma.

SUMMARY BackgroundAfter variceal bleeding, cirrhotic patients should receive secondary prophylaxis.

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Background: There are no effective biomarkers for the management of bronchopulmonary carcinoids (BPC). We examined the utility of a neuroendocrine multigene transcript “liquid biopsy” (NETest) in BPC for diagnosis and monitoring of the disease status. Aim: To independently validate the utility of the NETest in diagnosis and management of BPC in a multicenter, multinational, blinded study. Material and Methods: The study cohorts assessed were BPC (n = 99), healthy controls (n = 102), other lung neoplasia (n = 101) including adenocarcinomas (ACC) (n = 41), squamous cell carcinomas (SCC) (n = 37), small-cell lung cancer (SCLC) (n = 16), large-cell neuroendocrine carcinoma (LCNEC) (n = 7), and idiopathic pulmonary fibrosis (IPF) (n = 50). BPC were histologically classified as typical (TC) (n = 62) and atypical carcinoids (AC) (n = 37). BPC disease status determination was based on imaging and RECIST 1.1. NETest diagnostic metrics and disease status accuracy were evaluated. The upper limit of normal (NETest) was 20. Twenty matched tissue-blood pairs were also evaluated. Data are means ± SD. Results: NETest levels were significantly increased in BPC (45 ± 25) versus controls (9 ± 8; p < 0.0001). The area under the ROC curve was 0.96 ± 0.01. Accuracy, sensitivity, and specificity were: 92, 84, and 100%. NETest was also elevated in SCLC (42 ± 32) and LCNEC (28 ± 7). NETest accurately distinguished progressive (61 ± 26) from stable disease (35.5 ± 18; p < 0.0001). In BPC, NETest levels were elevated in metastatic disease irrespective of histology (AC: p < 0.02; TC: p = 0.0006). In nonendocrine lung cancers, ACC (18 ± 21) and SCC (12 ± 11) and benign disease (IPF) (18 ± 25) levels were significantly lower compared to BPC level (p < 0.001). Significant correlations were evident between paired tumor and blood samples for BPC (R: 0.83, p < 0.0001) and SCLC (R: 0.68) but not for SCC and ACC (R: 0.25–0.31). Conclusions: Elevated ­NETest levels are indicative of lung neuroendocrine neoplasia. NETest levels correlate with tumor tissue and imaging and accurately define clinical progression.