Daily Sepsis Research Analysis

BACKGROUND: Sepsis remains a leading cause of mortality in intensive care units (ICU), but optimal energy delivery strategies remain unclear. This post hoc analysis examines the association between early energy delivery and 28-day all-cause mortality in ICU patients with sepsis. METHODS: This post hoc analysis of the multicentre NEED trial (ISRCTN12233792) included ICU patients with sepsis and ≥ 7-day stays. Early energy delivery (first 7 days) was calculated as a percentage of the target 25 kcal/kg ideal body weight. Patients were stratified by mNUTRIC score (<5 vs ≥5). Associations with 28-day mortality were assessed using Cox models and Kaplan-Meier analysis, with subgroup and spline analyses exploring effect modification and nonlinearity. RESULTS: This analysis included 1162 sepsis patients (median age 66.0 years, 66.3 % male), with 183 (15.7 %) patients deceased within 28-day after ICU admission. The optimal energy delivery thresholds were identified as 100 % of the target for low-risk and 60 % for high-risk patients. Cox proportional hazards models further confirmed that in the high-risk group, achieving≥60 % of the target was associated with a lower 28-day mortality risk (hazard ratio = 0.588, 95 % conficence interval: 0.388-0.891), whereas no benefit was observed in low-risk patients. Additionally, no significant interactions were found in subgroup analyses. Restricted cubic spline analysis suggested a downward trend in mortality risk with increasing energy delivery in high-risk patients (P-nonlinear = 0.063). CONCLUSIONS: Early energy delivery ≥60 % of target is linked to lower 28-day mortality in high-risk sepsis patients, with no clear benefit in low-risk groups. These results support risk-based nutritional strategies in sepsis care. TRIALS REGISTRATION: ISRCTN 12233792, registered on November 24, 2017.

BACKGROUND: Urinary tract infections (UTI) in oncological patients can lead to bacteraemia (bUTI), increasing morbidity and mortality. This study assessed the characteristics, outcomes and recurrence of bUTI in oncological patients. METHODS: A retrospective cohort study was conducted at Hospital Clinic, Barcelona, from 2008 to 2019. All episodes of bUTI in oncological patients were analysed. Multivariable regression models identified independent risk factors for multidrug-resistant (MDR) Gram-negative bacilli (GNB), recurrent bUTI and related mortality. RESULTS: A total of 561 bUTI episodes were identified in 478 oncological patients. Urinary tract involvement due to neoplasm was present in 62.2%, and 59.4% had urinary tract instrumentation. Prior UTI-related admission without bacteraemia was reported in 63.8%. Following bUTI, oncological treatment was delayed in 47% and stopped in 33.6% of cases. GNB caused 87.3% of episodes, with Escherichia coli and Klebsiella spp. being the most common pathogens. Enterococcus spp. and Pseudomonas aeruginosa were frequent, particularly in patients with urinary instrumentation. MDR-GNB caused 19.6% of episodes, and 23.4% of cases received inappropriate empirical antibiotic therapy (IEAT). Recurrent bUTI occurred in 14.0% of patients. A simple predictive score efficiently identified patients at high risk of recurrence. Thirty-day mortality was 15.3%, and bUTI-related mortality was 10.7%, with absence of fever, septic shock and carbapenemase-producing Enterobacterales linked to higher related mortality. CONCLUSION: bUTI in oncological patients is predominantly caused by GNB, with high rates of MDR isolates and high mortality. IEAT is common, and recurrence is significant, highlighting the need for targeted preventive strategies and optimized empirical therapy.

BACKGROUND: Patients undergoing hemodialysis (HD) face a significantly elevated risk of cardiovascular mortality, with sudden events during treatment posing a critical threat to survival. These risks are particularly pronounced in high-risk populations, such as patients recovering from cardiovascular surgery or those being treated for sepsis. Therefore, the development of effective preventive strategies is essential for improving patient outcomes. This study aimed to develop a machine learning model that uses pretreatment patient characteristics to predict sudden adverse events during HD and within 24 h after treatment in high-risk inpatients at acute care hospitals. METHODS: His retrospective study analyzed data from 739 patients who underwent HD at Hirosaki University Hospital between 2018 and 2021. Sudden events were defined as fatal arrhythmia, refractory intradialytic hypotension, or respiratory arrest. A logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD-related information). RESULTS: Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre-HD covariates and achieved an area under the receiver operating characteristic curve (AUC) of 0.889. Key covariates included emergency hospitalization (present in 71% of patients with sudden events), recent surgery (76%), shorter HD history, elevated pre-HD heart rate, lower serum albumin levels, and higher C-reactive protein concentrations. CONCLUSIONS: Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.