SHOCK最新文献

Background: Accumulating evidence supports the use of the prothrombin time-international normalized ratio-to-albumin ratio (PTAR) as a prognostic biomarker in various diseases. This study evaluated whether PTAR, an easily measurable index, predicts short- and long-term all-cause mortality in cancer patients with sepsis (cancer sepsis).

Methods: This retrospective cohort study used data from 1,584 adult cancer-sepsis patients in the ICU from the MIMIC-IV database (v3.1) and 82 patients from a Chinese tertiary oncology hospital. Mortality at 28 and 365 days was analyzed using multivariable Cox models, restricted cubic splines, Kaplan-Meier curves, and subgroup analyses. Discriminatory performance was assessed with the area under the receiver operating characteristic curve (AUC), comparing PTAR with the Sequential Organ Failure Assessment (SOFA) score, international normalized ratio (INR), and serum albumin (ALB). AUC differences were tested using DeLong's test.

Results: In the MIMIC-IV cohort, patients in the highest PTAR quartile (Q4) had significantly higher risks of 28-day (adjusted HR = 2.038) and 365-day mortality (adjusted HR = 1.615). PTAR showed superior discrimination compared to the SOFA score and INR at both time points. In the Chinese cohort, PTAR also predicted 28-day mortality (adjusted HR = 4.682), with better AUC (0.721) than SOFA (0.564).

Conclusions: PTAR at ICU admission independently predicts 28-day mortality in cancer patients with sepsis in both the MIMIC-IV and Chinese cohorts, outperforming SOFA and INR. The 365-day mortality was validated only in the MIMIC-IV cohort, where PTAR demonstrated stronger predictive performance compared to INR and SOFA. Therefore, PTAR can serve as a complementary biomarker to be used in conjunction with existing clinical scoring systems and laboratory indicators, providing additional risk assessment information for cancer-sepsis.

Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) can occur following sepsis when there is an overwhelming systemic inflammatory response that leads to multiple organ dysfunction, significant morbidity, and chronic critical illness. Despite the increasing recognition of this clinical problem, there are no interventions that can reverse this response. This study aims to characterize the immune responses in a murine PICS model using cecal ligation and puncture (CLP) and assess the efficacy of various immunomodulatory agents, including G-CSF, GM-CSF, and anti-PD-L1 antibody, in reversing the immune dysfunction when given after day four, once the typical acute response to sepsis starts to subside. Our results showed that anti-PD-L1 therapy showed greater efficacy than G-CSF and GM-CSF in reversing immunosuppression post-CLP, as evidenced by improved immune cell numbers, immune function, MHC-II and CD11b expression on innate immune cells, and INFγ production from T cells. However, while improving certain immune parameters, anti-PD-L1 treatment did not allow for effective bacterial clearance when the mice were inoculated with Pseudomonas aeruginosa eight days after CLP. Interestingly, despite this decreased bacterial clearance, anti-PD-L1 therapy did not affect survival for CLP mice (100% survival) compared with untreated CLP mice (60% survival). These findings suggest that anti-PD-L1 therapy may improve some of the immune dysfunction seen after CLP and allow for improved survival towards secondary bacterial infections.

Background: Accurate prediction of 28-day mortality in intensive care unit (ICU) patients represents a critical challenge in modern critical care medicine, with profound implications for clinical decision-making, resource optimization, and patient-centered care. Traditional severity scoring systems and conventional machine learning approaches have consistently demonstrated limitations in discriminative performance, calibration accuracy, and generalizability across heterogeneous patient populations.

Methods: This retrospective cohort study employed comprehensive data from 654 consecutively admitted adult ICU patients at a tertiary academic medical center between August 2022 and August 2024.We developed and rigorously validated four distinct prediction methodologies: Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and an innovative Multi-Model Fusion (MMF) framework incorporating probability averaging from constituent models. Our evaluation framework encompassed exhaustive discrimination metrics (AUC-ROC, sensitivity, specificity, F1-score), sophisticated calibration assessment (Brier score, calibration curves, Hosmer-Lemeshow test), detailed subgroup analyses across clinically relevantpatient strata, and multi-dimensional feature importance evaluation.

Results: The Multi-Model Fusion paradigm demonstrated statistically superior performance with an AUC-ROC of 0.862 (95% CI: 0.821-0.903), significantly outperforming all individual models (LR: ΔAUC=0.121, p<0.001; RF: ΔAUC=0.034, p=0.018; GB: ΔAUC=0.027, p=0.032). The fusion model achieved an optimal equilibrium between sensitivity (84.6%) and specificity (81.0%) while maintaining exceptional calibration characteristics (Brier score: 0.140; Hosmer-Lemeshow test: χ²=6.28, p=0.616). Remarkable performance consistency was observed across all patient subgroups (AUC-ROC range: 0.815-0.875), encompassing diverse age strata, disease severity spectra, and intervention requirements. SOFA score, APACHE II score, and Glasgow Coma Scale emerged as consistently robust predictors across all feature importance methodologies. Clinically meaningful risk stratification delineated three distinct mortalitycategories: low-risk (<0.3 probability, 5.6% observed mortality), moderate-risk (0.3-0.7, 31.7%), and high-risk (>0.7, 83.3%).

Conclusion: The Multi-Model Fusion framework establishes a clinically actionable and methodologically sophisticated paradigm for 28-day mortality prediction in ICU patients, effectively addressing fundamental limitations of conventional approaches through enhanced discriminative performance, exemplary calibration accuracy, and consistent generalizability across diverse patient populations. These compelling findings strongly advocate for its integration intoICU clinical decision support ecosystems to advance prognostic precision and guide personalized therapeutic strategies.

Background: Fluid resuscitation in septic shock lacks bedside tools that simultaneously evaluate fluid distribution and cellular function, often leading to fluid overload and increased mortality. Bioelectrical Impedance Vector Analysis (BIVA) is a non-invasive bedside technique for comprehensive assessment of body composition and hydration status, but its prognostic value and application criteria in septic shock remain undefined.

Methods: A prospective observational study was conducted in an intensive care unit (ICU) of a university-affiliated hospital. Consecutive adult patients meeting Sepsis-3.0 criteria for septic shock were enrolled. BIVA parameters (whole-body and segmental PhA, ECW/ICW ratio, tolerance ellipses), hemodynamic indices, and serum lactate were dynamically monitored at ICU admission (within 24h of diagnosis), 6 hours post-resuscitation (T1), 24 hours (T2), and then daily until ICU discharge or death. Receiver operating characteristic (ROC) curve analysis determined prognostic cutoffs. Spearman's correlation and linear mixed models analyzed BIVA-lactate relationships. Passive leg raising (PLR) test served as the reference for fluid responsiveness, with predictive performance analyzed for mean arterial pressure (MAP) gradient (ΔMAP) between bilateral upper arms at different lateral decubitus positions combined with BIVA parameters.

Results: Among 128 enrolled patients, multivariate Cox regression identified initial PhA ≤ 3.10° (Hazard Ratio [HR] = 3.85, 95% Confidence Interval [CI]: 2.12-6.99) and BIVA-derived hydration fraction >74% (HR = 2.41, 95% CI: 1.32-4.39) as independent predictors of 30-day mortality. The Extracellular Water/Intracellular Water ECW/ICW ratio strongly correlated with lactate levels (r = 0.71, p < 0.001), and their combination predicted acute kidney injury more effectively (Area Under the Curve [AUC] = 0.92) than either parameter alone. In 95 patients undergoing positional testing, ΔMAP≥8.00 mmHg at 40°lateral decubitus position predicted fluid responsiveness with 85.7% sensitivity and 84.2% specificity; combining this with upper-arm BIVA parameters further enhanced predictive value (AUC = 0.93).

Conclusion: BIVA provides an effective individualized assessment tool for metabolic and fluid management in septic shock. PhA and hydration status are robust prognostic indicators; the ECW/ICW-lactate correlation reveals the pathophysiological link between tissue edema and hypoperfusion; and positional BIVA testing with upper-arm pressure gradient offers an innovative method for non-invasive assessment of fluid responsiveness.

Background: Heat stroke (HS) is a life-threatening condition marked by hyperthermia, central nervous system dysfunction, and multi-organ injury. Exertional heatstroke (EHS), which commonly affects healthy young people, typically follows intensity training or heavy physical labor in high temperature and high humidity environment. However, the molecular events driving the progression from mild to severe EHS remain poorly understood. This study integrated clinical data with serum proteomics to identify key pathways and biomarkers involved in EHS progression.

Methods: From June 2022 to October 2023, serum samples were collected from heat stroke, including 7 mild and 7 severe cases, with the remaining as controls. Demographic data, laboratory results, and Sequential Organ Failure Assessment (SOFA) scores were recorded. Serum proteins were analyzed using label-free mass spectrometry. Differentially expressed proteins (DEPs) were identified and analyzed using protein-protein interaction (PPI) networks, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Comparisons between mild vs. control, severe vs. control, and severe vs. mild groups were performed with Benjamini-Hochberg correction. Receiver operating characteristic analysis was used to evaluate discriminative ability; proteins with area under the curve (AUC) ≥0.85 and a lower bound of the 95% confidence interval ≥0.50 were selected. ELISA was used for further validation.

Results: Compared with patients with mild EHS, patients with severe EHS exhibited significantly higher markers of inflammation (WBC), coagulation abnormalities (PT, APTT, fibrinogen, D-dimer), organ injury (CK-MB, AST, LD, Creatinine, Uric Acid), and higher SOFA scores (P < 0.05). Proteomic profiling identified 51 shared DEPs across comparisons. PPI analysis highlighted proteins in the complement and coagulation cascades. Enrichment results indicated significant activation of complement-coagulation pathways, nuclear factor kappa-B (NF-κB) signaling, metabolic dysregulation, and immune responses. Key biomarkers-such as von Willebrand factor (vWF), mannose-binding lectin serine protease 1 (MASP1), coagulation factor VIII (F8), and protein C (PROC)-demonstrated strong discriminative performance and were associated with disease severity. Meanwhile, serum levels of HSPA13 (HSP70) and HSP90AA1/HSP90B1 (HSP90) were elevated in both mild and severe EHS relative to controls, with no significant differences between severity groups.

Conclusions: Complement-coagulation activation and metabolic dysregulation appear to drive the transition from mild to severe EHS. The proteins MASP-1, F8, PROC, and vWF represent promising biomarkers and potential therapeutic targets. These findings offer mechanistic insights for early risk stratification and targeted intervention in EHS.

Background: Pediatric hemophagocytic lymphohistiocytosis (HLH) patients who develop disseminated intravascular coagulation (DIC) experience rapid disease progression and substantially elevated mortality. Currently, no validated early identification strategies exist for this life-threatening complication. We aimed to develop a prediction model for early DIC detection in pediatric HLH patients.

Methods: This retrospective cohort study included patients from the Hunan Children's Hospital HLH database (January 2018-August 2024). Data imbalance was addressed through combined oversampling and undersampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE). The cohort was divided into training and validation sets (7:3 ratio). A LASSO-logistic regression model was developed and validated internally, with external validation using prospectively collected cases (January-August 2025). Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.

Results: Of 265 included patients, 217 cases were analyzed after SMOTE application. DIC incidence was 42.1% (64/152) and 44.6% (29/65) in training and validation cohorts, respectively. LASSO regression (lambda.1se=0.08) identified six potential predictors: C-reactive protein (CRP), globulin, cholesterol, high-density lipoprotein cholesterol, prothrombin time (PT), and interferon-γ (IFN-γ). Multivariable logistic regression confirmed three independent predictors: CRP, PT, and IFN-γ (all P<0.05). The model demonstrated robust discriminative performance with area under the receiver operating characteristic curve (AUROC) of 0.865 (95% CI: 0.809-0.922) in the training cohort and bootstrap-corrected C-index of 0.857 (95% CI: 0.828-0.886). Internal validation yielded AUROC of 0.797 (95% CI: 0.686-0.908), whereas external validation achieved an AUROC of 0.950. Decision curve analysis showed positive net benefit across threshold probabilities of 0%-99% in training and 0%-88% in validation sets.

Conclusion: The LASSO-logistic prediction model, incorporating three readily available biomarkers, demonstrated promising discriminative ability for predicting DIC risk in pediatric HLH. This tool may facilitate early risk stratification and timely therapeutic interventions to improve clinical outcomes.

Background: Postextubation airway obstruction represents a significant complication in critical care, potentially necessitating reintubation and prolonging intensive care unit (ICU) stay. The cuff leak test (CLT) is commonly used to predict postextubation stridor and reintubation risk, but its clinical utility remains controversial. This study evaluated the relationship between CLT results and reintubation risk in a large cohort of critically ill patients.

Methods: This single-center, retrospective, descriptive study analyzed 742 adult patients admitted to the medical and surgical ICUs who underwent mechanical ventilation for ≥24 h between January 2020 and December 2024. The primary outcome was reintubation within 48 h of planned extubation. Quantitative cuff leak volume measurements were performed preextubation, with leak volume expressed as absolute values and percentage of tidal volume. Multivariable logistic regression was used to identify independent predictors of reintubation, including CLT results and patient characteristics.

Results: Of the 742 patients studied, 68 (9.2%) required reintubation within 48 h. Patients with a cuff leak volume <110 mL or <15% of tidal volume had significantly higher reintubation rates (18.7% vs. 7.1%, P  < 0.001). After adjusting for confounding variables, a positive CLT (defined as cuff leak volume <110 mL) remained independently associated with reintubation (adjusted odds ratio, 2.86; 95% confidence interval [CI], 1.65-4.97; P  < 0.001). Other significant independent predictors included prolonged intubation (>7 days), female sex, body mass index >30 kg/m 2 , and traumatic or difficult intubation. Combining CLT results with these clinical risk factors improved prediction accuracy (area under the curve, 0.82; 95% CI, 0.76-0.87).

Conclusion: A positive CLT is independently associated with increased reintubation risk in critically ill patients. The predictive accuracy is enhanced when CLT results are combined with clinical risk factors. These findings suggest that CLT should be incorporated into extubation decision-making, particularly for patients with additional risk factors for postextubation airway complications.