Daily Sepsis Research Analysis

BACKGROUND/OBJECTIVES: Septic cardiomyopathy (SCM) is a severe cardiac complication of sepsis, characterized by cardiac dysfunction with limited effective treatments. This study aimed to identify repurposable drugs for SCM by integrated multi-omics and network analyses. METHODS: We generated a mouse model of SCM induced by lipopolysaccharide (LPS) and then obtained comprehensive metabolic and genetic data from SCM mouse hearts using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and RNA sequencing (RNA-seq). Using network proximity analysis, we screened for FDA-approved drugs that interact with SCM-associated pathways. Additionally, we tested the cardioprotective effects of two drug candidates in the SCM mouse model and explored their mechanism-of-action in H9c2 cells. RESULTS: Network analysis identified 129 drugs associated with SCM, which were refined to 14 drug candidates based on strong network predictions, proven anti-infective effects, suitability for ICU use, and minimal side effects. Among them, acetaminophen and pyridoxal phosphate significantly improved cardiac function in SCM moues, as demonstrated by the increased ejection fraction (EF) and fractional shortening (FS), and the reduced levels of cardiac injury biomarkers: B-type natriuretic peptide (BNP) and cardiac troponin I (cTn-I). In vitro assays revealed that acetaminophen inhibited prostaglandin synthesis, reducing inflammation, while pyridoxal phosphate restored amino acid balance, supporting cellular function. These findings suggest that both drugs possess protective effects against SCM. CONCLUSIONS: This study provides a robust platform for drug repurposing in SCM, identifying acetaminophen and pyridoxal phosphate as promising candidates for clinical translation, with the potential to improve treatment outcomes in septic patients with cardiac complications.

BACKGROUND: In critically ill patients with acute kidney injury (AKI), a fluid balance (FB) > 2 L at 72 h after AKI diagnosis is associated with adverse outcomes. Identification of patients at high-risk for such fluid accumulation may help prevent it. METHODS: We used Australian electronic medical record (EMR)-based clinical data to develop the "AKI-FB risk score", validated it in a British cohort and used it to predict a positive FB >2 L at 72 h after AKI diagnosis. RESULTS: We developed the AKI-FB score in 32,030 patients with a median age of 63 years and a median APACHE 2 score of 16. We validated it in 4465 patients, with significant differences in admission diagnoses and interventions. The key score variables were admission after trauma, sepsis or septic shock, and, on the day of AKI diagnosis, highest creatinine, daily cumulative FB, mechanical ventilation, noradrenaline use, noradrenaline equivalent dose >0.07 μg/kg/min, lactate ≥2 mmol/L, transfusion, and nutritional support. A score threshold of 32 had a sensitivity of 75 % and a specificity of 72 % for predicting a > 2 L positive FB with an AUC-ROC of 0.805; 95 % CI 0.799 to 0.810. External validation demonstrated an AUC of 0.761 (95 % CI 0.746 to 0.775). CONCLUSION: We developed and validated the "AKI-FB risk score" to predict patients who developed a positive FB >2 L within 72 h of AKI diagnosis. This prediction score was robust and facilitated the identification of high-risk AKI patients who could be the tarted for preventive measures and be included in future clinical trials of FB management.

BACKGROUND: Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients with BSI receive timely, effective, and targeted treatment and improve their survival. This study was based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Extremely Randomized Trees (ERT) models were constructed to classify carbapenem-resistant Escherichia coli (CREC) and carbapenem-resistant Klebsiella pneumoniae (CRKP). Bacterial species were identified by MALDI-TOF MS in positive blood cultures isolated via the serum isolation gel method, and E. coli and K. pneumoniae in positive blood cultures were collected and placed into machine learning models to predict susceptibility to carbapenems. The aim of this study was to provide rapid detection of CREC and CRKP in blood cultures, to shorten the turnaround time for laboratory reporting, and to provide a basis for early clinical intervention and rational use of antibiotics. RESULTS: The collected MALDI-TOF MS data of 640 E. coli and 444 K. pneumoniae were analysed by machine learning algorithms. The area under the receiver operating characteristic curve (AUROC) for the diagnosis of E. coli susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.95, 1.00, 0.99, 0.99, and 1.00, respectively, and the accuracy in predicting 149 E. coli-positive blood cultures were 0.89, 0.92, 0.90, 0.92, and 0.86, respectively. The AUROC for the diagnosis of K. pneumoniae susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.78, 0.95, 0.93, 0.90, and 0.95, respectively, and the accuracy in predicting 127 K. pneumoniae-positive blood cultures were 0.76, 0.86, 0.81, 0.80, and 0.76, respectively. CONCLUSIONS: Machine learning models constructed by MALDI-TOF MS were able to directly predict the susceptibility of E. coli and K. pneumoniae in positive blood cultures to carbapenems. This rapid identification of CREC and CRKP reduces detection time and contributes to early warning and response to potential antibiotic resistance problems in the clinic. CLINICAL TRIAL NUMBER: Not applicable.