Background: Chronic inflammation and oxidant/antioxidant imbalance are two main pathological features associated with lipopolysaccharide (LPS)-induced acute lung injury (ALI). The following study investigated the protective role of hydrogen (H₂), a gaseous molecule without known toxicity, in LPS-induced lung injury in mice and explored its potential molecular mechanisms. Methods: Mice were randomly divided into three groups: H₂ control group, LPS group, and LPS + H₂ group. The mice were euthanized at the indicated time points, and the specimens were collected. The 72 h survival rates, cytokines contents, pathological changes, expression of Toll-like receptor 4 (TLR4), and oxidative stress indicators were analyzed. Moreover, under different culture conditions, RAW 264.7 mouse macrophages were used to investigate the potential molecular mechanisms of H₂ in vitro. Cells were divided into the following groups: PBS group, LPS group, and LPS + H₂ group. The cell viability, intracellular ROS, cytokines, and expression of TLR4 and nuclear factor kappa-B (NF-κB) were observed. Results: Hydrogen inhalation increased the survival rate to 80%, reduced LPS-induced lung damage, and decreased inflammatory cytokine release in LPS mice. Besides, H₂ showed remarked anti-oxidative activity to reduce the MDA and NO contents in the lung. In vitro data further indicated that H₂ down-regulates the levels of ROS, NO, TNF-α, IL-6, and IL-1β in LPS-stimulated macrophages and inhibits the expression of TLR4 and the activation of nuclear factor kappa-B (NF-κB). Conclusion: Hydrogen gas alleviates lipopolysaccharide-induced acute lung injury and inflammatory response most probably through the TLR4-NF-κB pathway.

The relationship between baseline hemoglobin levels and in-hospital mortality in septic patients remains unclear. This study aimed to clarify this association in critically ill patients with sepsis. Patients with sepsis were retrospectively identified from the Medical Information Mart for Intensive Care-IV (MIMIC-IV 2.2) and eICU Collaborative Research Database (eICU-CRD). Multivariate logistic regression analysis and restricted cubic spline regression were used to investigate the association between hemoglobin and the risk of in-hospital mortality. Additionally, a two-part linear regression model was used to determine threshold effects. Stratified analyses were also performed. A total of 21,946 patients from MIMIC-IV and 15,495 patients from eICU-CRD were included in the study. In-hospital mortality was 14.95% in MIMIC-IV and 17.40% in eICU-CRD. Multivariate logistic regression showed that hemoglobin was significantly and nonlinearly associated with the risk of in-hospital mortality after adjusting for other covariates. Furthermore, we found a nonlinear association between hemoglobin and in-hospital mortality, with mortality plateauing at 10.2 g/dL. The risk of mortality decreased with increasing hemoglobin levels below 10.2 g/dL but increased when hemoglobin levels exceeded 10.2 g/dL. These findings were validated in the eICU-CRD dataset. A nonlinear correlation between hemoglobin levels and in-hospital mortality was observed in patients with sepsis, with a threshold of 10.2 g/DL. These findings suggested that hemoglobin levels below or above the threshold may be associated with worse outcomes, warranting further investigation in prospective studies.

Background: Cardiopulmonary bypass (CPB) triggers a strong inflammatory response in cardiovascular surgery patients during the perioperative period. This article mainly focuses on the perioperative application of novel inflammatory biomarkers in cardiovascular surgeries involving CPB.Methods: Patients were divided into a CPB group and a non-CPB group according to whether they underwent CPB during cardiovascular surgery. Novel inflammatory biomarkers and clinical results were recorded. The neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), platelet × neutrophil/lymphocyte ratio (SII), and monocyte × platelet × neutrophil/lymphocyte ratio (PIV) were calculated. The primary outcomes were perioperative prognosis between the CPB and non-CPB groups. The secondary outcomes included perioperative alterations of novel inflammatory biomarkers in the CPB group and predictive values of novel inflammatory biomarkers for postoperative infection and acute kidney injury.Results: A total of 332 patients were initially included in the study. Before propensity score matching (PSM), there were 96 patients in the CPB group and 236 patients in the non-CPB group. After PSM, both groups included 58 patients each. Compared with the non-CPB group, the CPB group had a higher proportion of intraoperative transfusion of blood products (63.79% vs. 6.90%, P < 0.001), specifically for red blood cells (58.62% vs. 3.45%, P < 0.001) and plasma (41.38% vs. 1.72, P < 0.001), exhibited a higher drainage fluid volume within 24 h [380 (200–550) ml vs. 200 (24–330) ml, P = 0.002], and required longer durations of mechanical ventilation [14.3 (6.6–21.3) h vs. 5.75 (4.08–10.1) h, P < 0.001] and ICU stay [48.78 (44.92–89.38) h vs. 27.16 (21.67–46.25) h, P < 0.001]. After surgery, NLR [14.00 (9.93–23.08) vs. 11.55 (7.38–17.38), P = 0.043] was higher in the CPB group, while the PIV, PLR, and SII in the CPB group were lower than those in the non-CPB group on the first day after surgery.Conclusions: Cardiovascular surgeries involving CPB exhibit a poorer prognosis compared to non-CPB procedures. Novel inflammatory biomarkers, including PLR, PIV, and SII, may offer valuable insights into the degree of postoperative inflammation, with NLR emerging as a potentially reliable prognostic indicator.

Background: The HALP (Hemoglobin, Albumin, Lymphocyte, and Platelet) score integrates key parameters reflecting nutritional and immune status. However, its prognostic value for in-hospital mortality in sepsis patients remains underexplored. Objective: To investigate the association between HALP score and in-hospital mortality in sepsis patients using two large critical care databases. Methods: We conducted a retrospective cohort study including adult patients with Sepsis-3 from the eICU Collaborative Research Database (derivation cohort, n = 12,899) and the MIMIC-IV database (validation cohort, n = 3,726). HALP was calculated as (hemoglobin × albumin × lymphocyte count)/platelet count, using first available values upon ICU admission. Restricted cubic spline (RCS) models assessed nonlinear relationships between HALP and mortality. Kaplan–Meier survival curves and multivariable Cox regression models, adjusted for demographics, comorbidities, laboratory values, and Acute Physiology Score III, evaluated survival differences between low- and high-HALP groups. Segmented Cox regression examined associations below and above RCS-derived thresholds. Results: A total of 16,625 patients were analyzed. RCS analysis demonstrated significant nonlinear associations between HALP and in-hospital mortality in both cohorts (overall P < 0.001; nonlinearity P < 0.001 in eICU, P = 0.002 in MIMIC-IV), with an inflection point near 12.45. Below this threshold, each unit increase in HALP was associated with a 3% reduction in mortality risk (eICU: HR 0.97, 95% CI 0.95–0.99, P = 0.002; MIMIC-IV: HR 0.97, 95% CI 0.94–0.99, P = 0.008). Kaplan–Meier analyses showed significantly higher survival in the high HALP group (eICU: log-rank P = 0.005, HR 0.882, 95% CI 0.808–0.962; MIMIC-IV: log-rank P < 0.001, HR 0.723, 95% CI 0.607–0.862). Multivariable Cox regression confirmed that high HALP remained independently protective after full adjustment (eICU: HR 0.90, 95% CI 0.82–0.98, P = 0.017; MIMIC-IV: HR 0.85, 95% CI 0.74–0.98, P = 0.028). Conclusion: The HALP score demonstrates robust prognostic value for predicting in-hospital mortality in sepsis patients, with consistent nonlinear relationships validated across two large databases. Its simplicity and reliance on routine laboratory parameters support potential clinical application in sepsis risk stratification.

For critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the discontinuation of continuous renal replacement therapy (CRRT). This study aims to explore factors associated with successful CRRT discontinuation in AKI patients and to develop predictive models for successful discontinuation. We conducted a retrospective study on adult patients with AKI who received CRRT, sourced from the Medical Information Mart for Intensive Care (MIMIC-IV) database. Successful discontinuation of CRRT was defined as no CRRT requirement within 72 h after stopping CRRT. Predictive factors for successful discontinuation of CRRT were analyzed. Additionally, we utilized machine learning algorithms to develop predictive models, including logistic regression (LR), decision tree (DT), random forest (RF), XGBoost, and K-nearest neighbor (KNN). A total of 599 patients were included, of whom 475 (79.3%) successfully discontinued CRRT. Urine output, non-renal SOFA score, bicarbonate, systolic blood pressure, and blood urea nitrogen were identified as risk factors for successful CRRT discontinuation. The KNN model exhibited the highest area under the receiver operating characteristic curve (AUC) (0.870), followed by LR (0.739), DT (0.691), RF (0.847), and XGBoost (0.830). When incorporating all available variables, the AUCs for the LR, DT, RF, XGBoost, and KNN models were 0.708, 0.674, 0.875, 0.866, and 0.816, respectively. Considering the performance of the models in both scenarios, the ensemble learning models (RF and XGBoost) were demonstrated superior performance. Our results identified factors associated with successful discontinuation of CRRT in AKI patients. Additionally, we developed promising machine learning models which provided a reference for future research.

Model for end-stage liver disease (MELD) is an effective predictive marker for renal, hepatic, and cardiac dysfunctions. In this study, we explore the correlation between MELD scores and the outcomes of patients undergoing cardiac valve surgery. We conducted a retrospective analysis of clinical data from patients who underwent cardiac valve surgery, encompassing procedures on the aortic valve, mitral valve, and tricuspid valve, using the Informative Surgical Patient dataset for Innovative Research Environment (INSPIRE) database, we conducted receiver operating characteristic (ROC) analyses on the study participants and chose MELD as the primary scoring tool for our study due to its optimal area under the curve (AUC), patients were stratified into high (MELD ≥18) and low (MELD <18) groups based on the determined cutoff value. The perioperative clinical data of the two groups were compared. The analysis revealed 751 patients in the low MELD group (75.5%) and 244 patients (24.5%) in the high MELD group. Patients in the high MELD group exhibited a lower body mass index (BMI) compared to those in the low MELD group. In comparison to the low MELD group, the high MELD group exhibited a higher rate of emergency surgery (10.66% vs. 5.99%, P=0.01), along with prolonged anesthesia time, surgery time, and cardiopulmonary bypass (CPB) time. Regarding clinical prognosis, the high MELD group demonstrated a higher 28-day mortality rate (10.66% vs. 0.8%, P<0.001), as also observed in the analysis of three valve subgroups. Additionally, the high MELD group experienced longer hospitalization and intensive care unit (ICU) stay, and a higher proportion of patients requiring mechanical circulatory support, including intra-aortic balloon pump (IABP) assist (14.75% vs. 3.86%, P<0.001), extracorporeal membrane oxygenation (ECMO) assist (7.38% vs. 0.8%, P<0.001), and continuous renal replacement therapy (CRRT) (27.87% vs. 1.46%, P<0.001) post-surgery. The Kaplan-Meier survival curves illustrated a significantly lower mortality rate in the low MELD group compared to the high MELD group, with highly significant statistical differences (P<0.001). The MELD score demonstrates a robust predictive value for clinical outcomes following cardiac valve surgery, underscoring its utility as a viable metric for disease stratification research.