RESUMEN
We aimed to describe the characteristics and outcomes of biliary source bloodstream infections (BSIs) in oncological patients. Secondarily, we analyzed risk factors for recurrent BSI episodes. All episodes of biliary source BSIs in oncological patients were prospectively collected (2008-2019) and retrospectively analyzed. Logistic regression analyses were performed. A rule to stratify patients into risk groups for recurrent biliary source BSI was conducted. Four hundred biliary source BSIs were documented in 291 oncological patients. The most frequent causative agents were Escherichia coli (42%) and Klebsiella spp. (27%), and 86 (21.5%) episodes were caused by multidrug-resistant Gram-negative bacilli (MDR-GNB). The rates of MDR-GNB increased over time. Overall, 73 patients developed 118 recurrent BSI episodes. Independent risk factors for recurrent BSI episodes were prior antibiotic therapy (OR 3.781, 95% CI 1.906-7.503), biliary prosthesis (OR 2.232, 95% CI 1.157-4.305), prior admission due to suspected biliary source infection (OR 4.409, 95% CI 2.338-8.311), and BSI episode caused by an MDR-GNB (OR 2.857, 95% CI 1.389-5.874). With these variables, a score was generated that predicted recurrent biliary source BSI with an area under the receiver operating characteristic (ROC) curve of 0.819. Inappropriate empirical antibiotic treatment (IEAT) was administered in 23.8% of patients, and 30-d mortality was 19.5%. As a conclusion, biliary source BSI in oncological patients is mainly caused by GNB, with high and increasing MDR rates, frequent IEAT, and high mortality. Recurrent BSI episodes are frequent. A simple score to identify recurrent episodes was developed to potentially establish prophylactic strategies. IMPORTANCE This study shows that biliary source bloodstream infections (BSIs) in oncological patients are mainly caused by Gram-negative bacilli (GNB), with high and increasing rates of multidrug resistance. Importantly, recurrent biliary source BSI episodes were very frequent and associated with delays in chemotherapy, high rates of inappropriate empirical antibiotic therapy, and high 30-d mortality (19.5%). Using the variable independently associated with recurrent BSI episodes, a score was generated that predicted recurrent biliary source BSI with high accuracy. This score could be used to establish prophylactic strategies and lower the risk of relapsing episodes and the associated morbidity and mortality.
RESUMEN
BACKGROUND: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. METHODS: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. RESULTS: The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. CONCLUSION: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.
