A Prospective Multicentre Study of the Epidemiology and Outcomes of Bloodstream Infection in Cirrhotic Patients

OBJECTIVE: The aim of this study was to describe the current epidemiology of BSI in patients with cirrhosis, analyse predictors of 30-day mortality, and risk factors for antibiotic resistance. METHODS: Cirrhotic patients developing a BSI episode were prospectively included at 19 centres in five countries from September 2014 to December 2015. The discrimination of mortality risk scores for 30-day mortality were compared by area under the receiver operator-risk and Cox-regression models. Risk factors for multidrug-resistant organisms (MDRO) were assessed with a logistic regression model. RESULTS: We enrolled 312 patients. Gram-negative bacteria, Gram-positive bacteria and Candida spp. were the cause of BSI episodes in 53%, 47% and 7% of cases, respectively. The 30-day mortality rate was 25% and best predicted by the SOFA and CLIF-SOFA score. In a Cox-regression model, delayed (>24h) antibiotic treatment [HR 7.58 (95%CI 3.29-18.67), P<.001], inadequate empirical therapy [HR 3.14 (95%CI 1.93-5.12), P<.001] and CLIF-SOFA score [HR 1.35 (95%CI 1.28-1.43), P<.001] were independently associated with 30-day mortality. Independent risk factors for MDRO (31% of BSIs) were previous antimicrobial exposure [OR 2.91 (95%CI 1.73-4.88), P<.001] and previous invasive procedures [OR 2.51 (95%CI 1.48-4.24), P=.001], whereas spontaneous bacterial peritonitis as BSI source was associated with a lower odds of MDRO [OR 0.30 (95%CI 0.12-0.73), P=.008). CONCLUSIONS: MDRO account for nearly one-third of BSI in cirrhotic patients and often resulting in delayed or inadequate empirical antimicrobial therapy and increased mortality rates. Our data suggest that improved prevention and treatment strategies for MDRO are urgently needed in the liver cirrhosis patients

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development