Daily Sepsis Research Analysis

OBJECTIVES: Globally, the burden of sepsis is highest in malaria endemic areas of sub-Saharan Africa. The influence of malaria on biological heterogeneity inherent to sepsis in this setting is poorly understood. We sought to determine shared and distinct features of the host response in malarial and non-malarial sepsis in sub-Saharan Africa. DESIGN AND SETTING: Analysis of Olink proteomic data from prospective observational cohort studies of sepsis conducted at public hospitals in Uganda (discovery cohort [Entebbe, urban], n = 238; validation cohort [Tororo, rural], n = 253). PATIENTS: Adults (age ≥ 18 yr) hospitalized with sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The frequency of malaria-associated (malarial) sepsis was 20% in the discovery cohort and 28% in the validation cohort. In both cohorts, a shared host response was predominant, with less than or equal to 8% of proteins differentially expressed (Benjamini-Hochberg-adjusted p ≤ 0.05) between malarial and non-malarial sepsis, after adjustment for demographic variables and HIV and tuberculosis coinfection. In both cohorts, malarial sepsis was associated with increased expression of immunosuppressive proteins (interleukin-10, leukocyte immunoglobulin-like receptor B1, killer cell immunoglobulin-like receptor 3DL1), including those associated with Tcell exhaustion and apoptosis (lymphocyte activation gene 3, T cell immunoglobulin and mucin domain containing 4). A classifier model including these immunosuppressive proteins showed reasonable discrimination (area under the receiver operating characteristic curves, 0.73 [95% CI, 0.65-0.81] and 0.72 [0.65-0.79]) and calibration (Brier scores 0.14 and 0.18) for stratification of malarial sepsis in the discovery and validation cohorts, respectively. CONCLUSIONS: Host responses are largely conserved in malarial and non-malarial sepsis but may be distinguished by a signature of relative immunosuppression in the former.

BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a frequent complication in patients with sepsis and is associated with high mortality. Therefore, early recognition of SA-AKI is essential for administering supportive treatment and preventing further damage. This study aimed to identify and validate metabolite biomarkers of SA-AKI to assist in early clinical diagnosis. METHODS: Untargeted renal proteomic and metabolomic analyses were performed on the renal tissues of LPS-induced SA-AKI and sepsis mice. Glomerular filtration rate (GFR) monitoring technology was used to evaluate real-time renal function in mice. To elucidate the distinctive characteristics of SA-AKI, a multi-omics Spearman correlation network was constructed integrating core metabolites, proteins, and renal function. Subsequently, metabolomics analysis was used to explore the dynamic changes of core metabolites in the serum of SA-AKI mice at 0, 8, and 24 h. Finally, a clinical cohort (28 patients with SA-AKI vs. 28 patients with sepsis) serum quantitative metabolomic analysis was carried out to build a diagnostic model for SA-AKI via logistic regression (LR). RESULTS: Thirteen differential renal metabolites and 112 differential renal proteins were identified through a multi-omics study of SA-AKI mice. Subsequently, a multi-omics correlation network was constructed to highlight five core metabolites, i.e., 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, creatine, myristic acid, and inosine, the early changes of which were then observed via serum time series experiments of SA-AKI mice. The levels of 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, and creatine increased significantly at 24 h, myristic acid increased at 8 h, while inosine decreased at 8 h. Ultimately, based on the identified core metabolites, we recruited 56 patients and constructed a diagnostic model named IC3, using inosine, creatine, and 3-hydroxybutyric acid, to early identify SA-AKI (AUC = 0.90). CONCLUSIONS: We proposed a blood metabolite model consisting of inosine, creatine, and 3-hydroxybutyric acid for the early screening of SA-AKI. Future studies will observe the performance of these metabolites in other clinical populations to evaluate their diagnostic role.

OBJECTIVES: Evaluate prediction models designed or used to identify patients with sepsis in the prehospital setting. DESIGN: Nested case-control study. SETTING: Four emergency departments (EDs) in Utah. PATIENTS: Adult nontrauma patient with available prehospital care records who received ED treatment during 2018 after arrival via ambulance. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 16,620 patients arriving to a study ED via ambulance, 1,037 (6.2%) met Sepsis-3 criteria in the ED. Complete prehospital care data was available for 434 case patients with sepsis and 434 control patients without sepsis. Model discrimination for the outcome of meeting Sepsis-3 criteria in the ED was quantified using the area under the precision-recall curve (AUPRC), which yields a value equal to outcome prevalence for a noninformative model. Of 21 evaluated prediction models, only the Prehospital Early Sepsis Detection (PRESEP) model (AUPRC, 0.33 [95% CI, 0.27-0.41) outperformed unaided infection assessment by emergency medical services (EMS) personnel (AUPRC, 0.17 [95% CI, 0.13-0.23]) for prehospital prediction of patients who would meet Sepsis-3 criteria in the ED ( p < 0.001). PRESEP also outperformed the quick Sequential Organ Failure Assessment score (AUPRC, 0.13 [95% CI, 0.11-0.16]; p < 0.001). Among 28 evaluated dichotomous predictors of ED sepsis, sensitivity ranged from 6% to 91% and positive predictive value 8-100%. PRESEP exhibited modest sensitivity (60%) and positive predictive value (20%). CONCLUSIONS: PRESEP was the only evaluated prediction model that demonstrated better discrimination than unaided EMS infection assessment for the identification of ambulance-transported adult patients who met Sepsis-3 criteria in the ED.