Group-Based Trajectory Modeling of Fluid Balance in Elderly Patients with Acute Ischemic Stroke: Analysis from Multicenter ICUs.

INTRODUCTION: Acute ischemic stroke (AIS) significantly contributes to severe disability and mortality among the elderly. This study aims to explore the association between longitudinal fluid balance (FB) trajectories and clinical outcomes in elderly patients with AIS. Our hypothesis posits the existence of multiple latent trajectories of FB in patients with AIS during the initial 7 days following ICU admission. METHODS: Patients (age ≥ 65 years) with AIS and continuous FB records were identified from two large databases. Group-based trajectory modeling identified latent groups with similar 7-day FB trajectories. Subsequently, multivariable logistic and quasi-Poisson regression were employed to evaluate the relationship between trajectory groups and outcomes. Additionally, nonlinear associations between maximum fluid overload (FO) and outcomes were analyzed using restricted cubic spline models. To further validate our findings, subgroup and sensitivity analysis were conducted. RESULTS: A total of 1146 eligible patients were included in this study, revealing three trajectory patterns were identified: low FB (84.8%), decreasing FB (7.2%), and high FB (7.9%). High FB emerged as an independent risk factor for in-hospital mortality. Compared with those without FO, patients with FO had a 1.57-fold increased risk of hospital mortality (adjusted odd ratio (OR) 1.57, 95% confidence interval (CI) 1.08-2.27), 2.37-fold increased risk of adverse kidney event (adjusted OR 2.37, 95% CI 1.56-3.59), and 1.33-fold increased risk of prolonged ICU stay (adjusted incidence rate ratio (IRR) 1.33, 95% CI 1.19-1.48). The risk of hospital mortality and adverse kidney event increased linearly with rising maximum FO (P for non-linearity = 0.263 and 0.563, respectively). CONCLUSION: Daily FB trajectories were associated with adverse outcomes in elderly patients with AIS. Regular assessment of daily fluid status and restriction of FO are crucial for the recovery of critically ill patients.

Systemic inflammation response index as a prognostic factor for patients with sepsis-associated acute kidney injury: a retrospective observational study.

OBJECTIVE: To assess the association between the systemic inflammation response index (SIRI) and the prognosis in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: In this observational study, adult patients with SA-AKI were categorized into three groups based on SIRI tertiles. Survival outcomes were compared across the three groups using Kaplan-Meier survival curves. Various Cox proportional hazards regression models were developed to determine the association between the SIRI and mortality in patients with SA-AKI. Subgroup analyses were also performed to explore the association between different SIRI tertiles and all-cause mortality. RESULTS: After adjusting for several confounders, the second SIRI tertile (2.5 < SIRI < 7.6) was found to be an independent risk factor for 30-day mortality [hazard ratio (95% confidence interval): 1.19 (1.01-1.40)], 90-day mortality [1.22 (1.06-1.41)], and 365-day mortality [1.24 (1.09-1.40)]. Furthermore, high SIRI values were associated with increased risks of 30-day, 90-day, and 365-day mortality in patients with SA-AKI across all three models. The third tertile showed a significant association with adverse outcomes in most subgroups. CONCLUSIONS: The SIRI serves as a comprehensive biomarker for predicting all-cause mortality of critically ill patients with SA-AKI.

A semiautomated radiomics model based on multimodal dual-layer spectral CT for preoperative discrimination of the invasiveness of pulmonary ground-glass nodules.

Background: In recent years, spectral computed tomography (CT) has shown excellent performance in the diagnosis of ground-glass nodules (GGNs) invasiveness; however, no research has combined spectral multimodal data and radiomics analysis for comprehensive analysis and exploration. Therefore, this study goes a step further on the basis of the previous research: to investigate the value of dual-layer spectral CT-based multimodal radiomics in accessing the invasiveness of lung adenocarcinoma manifesting as GGNs. Methods: In this study, 125 GGNs with pathologically confirmed preinvasive adenocarcinoma (PIA) and lung adenocarcinoma were divided into a training set (n=87) and a test set (n=38). Each lesion was automatically detected and segmented by the pre-trained neural networks, and 63 multimodal radiomic features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select target features, and a rad-score was constructed in the training set. Logistic regression analysis was conducted to establish a joint model which combined age, gender, and the rad-score. The diagnostic performance of the two models was compared by the receiver operating characteristic (ROC) curve and precision-recall curve. The difference between the two models was compared by the ROC analysis. The test set was used to evaluate the predictive performance and calibrate the model. Results: Five radiomic features were selected. In the training and test sets, the area under the curve (AUC) of the radiomics model was 0.896 (95% CI: 0.830-0.962) and 0.881 (95% CI: 0.777-0.985) respectively, and the AUC of the joint model was 0.932 (95% CI: 0.882-0.982) and 0.887 (95% CI: 0.786-0.988) respectively. There was no significant difference in AUC between the radiomics model and joint model in the training and test sets (0.896 vs. 0.932, P=0.088; 0.881 vs. 0.887, P=0.480). Conclusions: Multimodal radiomics based on dual-layer spectral CT showed good predictive performance in differentiating the invasiveness of GGNs, which could assist in the decision of clinical treatment strategies.

Establishment and external validation of a nomogram for predicting 28-day mortality in patients with skull fracture.

Background: Skull fracture can lead to significant morbidity and mortality, yet the development of effective predictive tools has remained a challenge. This study aimed to establish and validate a nomogram to evaluate the 28-day mortality risk among patients with skull fracture. Materials and methods: Data extracted from the Medical Information Mart for Intensive Care (MIMIC) database were utilized as the training set, while data from the eICU Collaborative Research Database were employed as the external validation set. This nomogram was developed using univariate Cox regression, best subset regression (BSR), and the least absolute shrinkage and selection operator (LASSO) methods. Subsequently, backward stepwise multivariable Cox regression was employed to refine predictor selection. Variance inflation factor (VIF), akaike information criterion (AIC), area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to assess the model's performance. Results: A total of 1,527 adult patients with skull fracture were enrolled for this analysis. The predictive factors in the final nomogram included age, temperature, serum sodium, mechanical ventilation, vasoactive agent, mannitol, extradural hematoma, loss of consciousness and Glasgow Coma Scale score. The AUC of our nomogram was 0.857, and C-index value was 0.832. After external validation, the model maintained an AUC of 0.853 and a C-index of 0.829. Furthermore, it showed good calibration with a low Brier score of 0.091 in the training set and 0.093 in the external validation set. DCA in both sets revealed that our model was clinically useful. Conclusion: A nomogram incorporating nine features was constructed, with a good ability in predicting 28-day mortality in patients with skull fracture.