Search

(Background) Esophagectomy (EPG) presents high morbidity and mortality. Omega-3 fatty acids (ω-3FA) are a pharmaconutrient with benefits for postoperative morbidity. Studies of ω-3FA administered parenterally after esophagectomy are scarce. This study proposes to investigate the effect of combining fish oil lipid emulsions (LE) administered parenterally with enteral nutrition support. (Methods) Randomization was 1:1:1 in three groups: Group A received a LE mixture of 0.4 g/kg/day of fish oil and 0.4 g/kg/day of LCT/MCT 50:50, Group B received 0.8 g/kg/day of fish oil LE, and Group C received 0.8 g/kg/day of LCT/MCT 50:50. Variables were measured at recruitment time and day +1, +3, and +5. Inflammatory variables studied were Interlukin-6, C-reactive protein (CRP), tumoral necrosis factor-α (TNF-α), IL-10, IL-8 and CD25s. Safety, nutritional parameters and complications were analyzed. (Results) Administration of ω-3LE in the immediate postoperative period did not modulate the earlier inflammatory response. Statistically significant differences were found in IL-6 and CRP overall temporal evolution but were not found when studying the type of LE administered or in patients needing critical care. Administration of ω-3 resulted in safe and improved hypertriglyceridemia, depending on the dose. (Conclusions) ω-3FA has no impact on the early inflammatory postoperative response assessed for a short period but was safe. More studies for longer periods are needed. [ABSTRACT FROM AUTHOR]

Background: Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. Objectives: To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings: 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. Conclusions: Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible. [ABSTRACT FROM AUTHOR]

Aims: Therapeutic drug monitoring of infliximab can guide clinical decisions in patients with loss of response and in those who can benefit from a de‐intensification. The aim of this study was to determine the impact of therapeutic drug monitoring combined with Bayesian forecasting methodology on clinical response in a real‐world dataset of patients suffering from inflammatory bowel disease. Methods: We performed a single‐centre prospective study with one‐group pre‐test/post‐test design in 108 adult inflammatory bowel disease patients treated with model‐based dosing of infliximab maintenance treatment. We recorded clinical activity scores (Harvey‐Bradshaw index and partial Mayo) and inflammatory biomarkers per patient. Results: The initial infliximab regimen was maintained in 49 (45.4%) patients and was adjusted in 59 (54.6%) patients (34 treatment intensifications, 9 de‐intensifications and 16 treatment discontinuations or therapy replacements). The median time from intervention to index measurement was 126 (103–160) days. The overall proportion of patients in clinical remission increased from 65.7% to 80.4% (P <.0001) and the median infliximab trough concentrations increased from 3.21 (0.99–5.45) to 5.13 mg/L (3.57–6.53) (P <.0001). In the intensified group, the remission rate increased from 35.3% to 61.8% (P =.001) and the percentage of patients in clinical remission or with mild symptoms increased from 76.5% to 94.1%. In the de‐intensification cohort, no patients experienced an increase in the Harvey‐Bradshaw index or partial Mayo scores, and all patients maintained an infliximab trough concentration of >5 mg/L. Conclusion: In our cohort of inflammatory bowel disease patients, Bayes‐based optimized dosing improved the short‐term efficacy of infliximab treatment. [ABSTRACT FROM AUTHOR]