Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis.

BACKGROUND: Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations. METHODS: Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis. RESULTS: This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram. CONCLUSIONS: The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

Development and validation of potential phenotypes of serum electrolyte disturbances in critically ill patients and a Web-based application.

BACKGROUND: Electrolyte disturbances are highly heterogeneous and severely affect the prognosis of critically ill patients. Our study was to determine whether data-driven phenotypes of seven electrolytes have prognostic relevance in critically ill patients. METHODS: We extracted patient information from three large independent public databases, and clustered the electrolyte distribution of ICU patients based on the extreme value, median value and coefficient of variation of electrolytes. Three plausible clinical phenotypes were calculated using K-means clustering algorithm as the basic clustering method. MIMIC-IV was considered a training set, and two others have been designated as verification set. The robustness of the model was then validated from different angles, providing dynamic and interactive visual charts for more detailed characterization of phenotypes. RESULTS: 15,340, 12,445 and 2147 ICU patients with electrolyte records during early ICU stay in MIMIC-IV, eICU-CRD and AmsterdamUMCdb were enrolled. After clustering, three reasonable and interpretable phenotypes are defined as α, ß and γ according to the order of clusters. The α and γ phenotype, with significant differences in electrolyte distribution and clinical variables, higher 28-day mortality and longer length of ICU stay (p < 0.001), was further demonstrated by robustness analysis. The α phenotype has significant kidney injury, while the ß phenotype has the best prognosis. In addition, the assignment methods of the three phenotypes were developed into a web-based tool for further verification and application. CONCLUSIONS: Three different clinical phenotypes were identified that correlated with electrolyte distribution and clinical outcomes. Further validation and characterization of these phenotypes is warranted.

Establishing an artificial intelligence-based predictive model for long-term health-related quality of life for infected patients in the ICU.

Objective: To develop a model using a Chinese ICU infection patient database to predict long-term health-related quality of life (HRQOL) in survivors. Methods: A patient database from the ICU of the Fourth People's Hospital in Zigong was analyzed, including data from 2019 to 2020. The subjects of the study were ICU infection survivors, and their post-discharge HRQOL was assessed through the SF-36 survey. The primary outcomes were the physical component summary (PCS) and mental component summary (MCS). We used artificial intelligence techniques for both feature selection and model building. Least absolute shrinkage and selection operator regression was used for feature selection, extreme gradient boosting (XGBoost) was used for model building, and the area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Results: The study included 917 ICU infection survivors. The median follow-up was 507.8 days. Their SF-36 scores, including PCS and MCS, were below the national average. The final prognostic model showed an AUROC of 0.72 for PCS and 0.63 for MCS. Within the sepsis subgroup, the predictive model AUROC values for PCS and MCS were 0.76 and 0.68, respectively. Conclusions: This study established a valuable prognostic model using artificial intelligence to predict long-term HRQOL in ICU infection patients, which supports clinical decision making, but requires further optimization and validation.

Correlation between early intracranial pressure and cerebral perfusion pressure with 28-day intensive care unit mortality in patients with hemorrhagic stroke.

INTRODUCTION: Hemorrhagic stroke may cause changes in intracranial pressure (ICP) and cerebral perfusion pressure (CPP), which may influence the prognosis of patients. The aim of this study was to investigate the relationship between early ICP, CPP, and 28-day mortality in the intensive care unit (ICU) of patients with hemorrhagic stroke. PATIENTS AND METHODS: A retrospective study was performed using the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD), including hemorrhagic stroke patients in the ICU with recorded ICP monitoring. The median values of ICP and CPP were collected for the first 24 h of the patient's monitoring. The primary outcome was 28-day ICU mortality. Multivariable Cox proportional hazards models were used to analyze the relationship between ICP, CPP, and 28-day ICU mortality. Restricted cubic regression splines were used to analyze nonlinear relationships. RESULTS: The study included 837 patients with a 28-day ICU mortality rate of 19.4%. Multivariable analysis revealed a significant correlation between early ICP and 28-day ICU mortality (HR 1.08, 95% CI 1.04-1.12, p < 0.01), whereas early CPP showed no correlation with 28-day ICU mortality (HR 1.00, 95% CI 0.98-1.01, p = 0.57), with a correlation only evident when CPP < 60 mmHg (HR 1.99, 95% CI 1.14-3.48, p = 0.01). The study also identified an early ICP threshold of 16.5 mmHg. DISCUSSION AND CONCLUSION: Early ICP shows a correlation with 28-day mortality in hemorrhagic stroke patients, with a potential intervention threshold of 16.5 mmHg. In contrast, early CPP showed no correlation with patient prognosis.

Shenfu injection alleviate gut ischemia/reperfusion injury after severe hemorrhagic shock through improving intestinal microcirculation in rats.

Background: Shenfu (SF) injection, a traditional Chinese medication, would improve microcirculation in cardiogenic shock and infectious shock. This study was aimed to explore the therapeutic potential of the SF injection in gut ischemia-reperfusion (I/R) injury after severe hemorrhagic shock (SHS) and resuscitation. Furthermore, we also investigated the optimal adm？ inistration timing. Methods: Twenty-four male SD rats were randomly divided into four groups: Sham group (sham, n = 6), Control group (n = 6), SF injection group (SF, n = 6), and Delayed Shenfu injection administration group (SF-delay, n = 6). In SHS and resuscitation model, rats were induced by blood draw to a mean arterial pressure (MAP) of 40 ± 5 mmHg within 1 h and then maintained for 40 min; HR, MAP 'were recorded, microcirculation index [De Backer score, perfused small vessel density (PSVD), total vessel density (TVD), microcirculation flow index score (MFI), flow heterogeneity index (HI)] were analyzed. The blood gas index was detected, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), diamine oxidase (DAO), malondialdehyde (MDA) were measured by ELISA; ZO-1, and claudin-1 were measured by Western blotting. In addition, hematoxylin-eosin (HE) and periodic acid schiff (PAS) staining pathological sections of the intestinal mucosal tissues were also performed. Results: SF injection increased the MAP, relieved the metabolic acidosis degree associated with the hypoperfusion, and improved the intestinal microcirculatory density and perfusion quality after I/R injury. The expression of DAO, MDA in intestinal tissue, and plasma IL-6, TNF-α significantly decreased in the SF injection group compared to the control group. The concentration of ZO-1 and claudin-1 is also higher in the SF injection group. In addition, the HE and PAS staining results also showed that SF injection could decrease mucosal damage and maintain the structure. In the SF-delay group, the degree of intestinal tissue damage was intermediate between that of the control group and SF injection group. Conclusions: SF injection protect the intestine from I/R injury induced by SHS and resuscitation, the mechanism of which might be through improving intestinal microcirculation, reducing the excessive release of inflammatory factors and increasing intestinal mucosal permeability. Furthermore, the protection effect is more pronounced if administration during the initial resuscitation phase.

Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis.

Background: Septic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis. Methods: We extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness. Results: The study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786-0.834), 0.794 (95 % CI 0.756-0.831), and 0.723 (95 % CI 0.647-0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets. Conclusion: The constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.

l-Carnosine loaded on carboxymethyl cellulose hydrogels for promoting wound healing.

Wound management remains a challenge in clinical practice. Nowadays, patients have an increasing demand for wound repair with enhanced speed and quality; therefore, there is a great need to seek therapeutic strategies that can promote rapid and effective wound healing. In this study, we developed a carboxymethyl cellulose hydrogel loaded with l-carnosine (CRN@hydrogel) for potential application as a wound dressing. In vitro experiments confirmed that CRN@hydrogel can release over 80% of the drug within 48 h and demonstrated its favorable cytocompatibility and blood compatibility, thus establishing its applicability for safe utilization in clinical practice. Using a rat model, we found that this hydrogel could promote and accelerate wound healing more effectively. These results indicate that the novel hydrogel can serve as an efficient therapeutic strategy for wound treatment.

Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.

OBJECTIVE: Sepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC. METHODS: In this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results. RESULTS: A total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model. CONCLUSIONS: We developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

Canagliflozin attenuates post-resuscitation myocardial dysfunction in diabetic rats by inhibiting autophagy through the PI3K/Akt/mTOR pathway.

This study investigated the effects of canagliflozin on myocardial dysfunction after cardiac arrest and cardiopulmonary resuscitation in diabetic rats and the underlying mechanisms. Male rats with type 2 diabetes mellitus (T2DM) were subjected to a modified epicardial fibrillation model. Pretreatment with canagliflozin (10 mg/kg/day) for four weeks improved ATP levels, post-resuscitation ejection fraction, acidosis, and hemodynamics. Canagliflozin also reduced myocardial edema, mitochondrial damage and, post-resuscitation autophagy levels. In vitro analyses showed that canagliflozin significantly reduced reactive oxygen species and preserved mitochondrial membrane potential. Using the PI3K/Akt pathway inhibitor Ly294002, canagliflozin was shown to attenuate hyperautophagy and cardiac injury induced by high glucose and hypoxia-reoxygenation through activation of the PI3K/Akt/mTOR pathway. This study highlights the therapeutic potential of canagliflozin in post-resuscitation myocardial dysfunction in diabetes, providing new insights for clinical treatment and experimental research.