Daily Sepsis Research Analysis

Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and accurate prediction remains challenging due to the lack of reliable biomarkers. Urinary small-molecule metabolomics offers a promising approach to capture the dynamic metabolic changes during the progression of S-AKI. In this two-center prospective observational study, we enrolled 360 children from both centers. Urine samples were collected within 24h after hospitalized children diagnosed with sepsis, stored at -80 °C, and analyzed using gas chromatography-mass spectrometry (GC-MS). Based on urinary metabolic fingerprints (U-MF), we developed and validated a machine learning model for early prediction of S-AKI. The Shapley Additive Explanations (SHAP) algorithm was applied to visually explain the optimal model. A panel of 10 metabolites was selected as common discriminative features. Among the 4 machine learning models evaluated, the support vector machine (SVM) demonstrated the best performance in both the discovery cohort (AUC 0.94, 95% CI: 0.91-0.98) and the external validation cohort (AUC 0.89, 95% CI: 0.82-0.96), enabling early prediction of S-AKI within 24 h. Furthermore, the U-MF panel was integrated into an open-access online platform to facilitate clinical translation. Our findings suggest that U-MF combined with machine learning holds promise as a robust and noninvasive approach with potential utility for early prediction of S-AKI in pediatric patients.

BACKGROUND: Serious bacterial infections (SBIs) are major contributors to neonatal morbidity and mortality in low-income countries. We describe the aetiology and risk factors for neonatal bacteraemia and in-hospital mortality in eastern Ethiopia, focusing on Pantoea dispersa, a rarely studied pathogen, and Klebsiella pneumoniae. METHODS: Prospective surveillance was conducted at Hiwot Fana Comprehensive Specialized Hospital (HFCSH), Harar, from December 2021 to November 2023. Blood for culture was drawn from neonates admitted with the WHO clinical definition of possible-SBI (pSBI). Isolates were identified using API kits and antimicrobial susceptibility tested by Kirby-Bauer method. FINDINGS: Among 1375 neonates with pSBI, blood was cultured from 1335 (97%), and 356 (27%) cultured pathogens. The commonest infections were Pantoea species (n = 145, 40.7%) and K. pneumoniae (n = 63, 17.7%); 128 Pantoea isolates were identified as P. dispersa by Matrix-Assisted Laser Desorption Ionization-Time of Flight. Case-fatality-ratios were 25% (32/128), 19% (9/47) and 19% (30/160) for P. dispersa, K. pneumoniae and other monomicrobial infections, respectively. P. dispersa showed resistance to ampicillin (99%) and cefotaxime (85%) but was otherwise broadly susceptible, while K. pneumoniae showed resistance to cefotaxime (100%) and gentamicin (89%), remaining susceptible only to amikacin and meropenem. Compared to non-bacteraemic admissions, P. dispersa bacteraemia was associated with health-facility delivery outside HFCSH (aOR 1.9 [95% CI, 1.21-2.97]), low birth weight (aOR 2.1 [95% CI,1.22-3.47]), and the dry season (aOR 9.7, [95% CI, 4.61-20.31]). K. pneumoniae bacteraemia was associated with health-facility delivery outside HFCSH (aOR, 2.2, [95% CI, 1.09-4.27]) alone. Among admissions with pSBI, death was associated with low birth weight and P. dispersa bacteraemia. INTERPRETATION: Bacteraemia was prevalent among neonates admitted with pSBI to HFCSH. P. dispersa and K. pneumoniae predominated and both had high mortality risks. Rigorous diagnostics and epidemiological associations support the interpretation of P. dispersa as a pathogen, necessitating local investigation into transmission and infection control interventions.

PURPOSE: Given the limited evidence, this study aimed to determine the impact of an electronic health record (EHR)-based sepsis alert on the incidence of sepsis-associated acute kidney injury (SA-AKI), adherence to the 3-h sepsis bundle, and other clinical outcomes. METHODS: This single-center, pre/post-implementation study analyzed adult patients who were admitted from the Emergency Department to the intensive care unit with sepsis at a tertiary hospital in the United States from January 2021 to December 2023. A total of 7137 patients were included in our analysis. We used interrupted time series models, adjusted for seasonality, to assess changes following the implementation of a sepsis screening and alert system at Emergency Department on July 1, 2022. The primary outcome was the incidence of SA-AKI. RESULTS: After implementation, there was no significant immediate change in SA-AKI incidence (0.26%; 95% CI, -4.02 to 4.55), but a significant decreasing monthly trend was observed (-0.47% per month; 95% CI, -0.87 to -0.07). Adherence to the 3-h sepsis bundle showed a significant immediate increase (3.78%; 95% CI, 1.38 to 6.18). However, no significant changes were observed in in-hospital mortality or non-recovery from SA-AKI. CONCLUSIONS: The sepsis alert tool was associated with a progressive reduction in SA-AKI incidence and improved bundle adherence but was not associated with changes in mortality or renal recovery.