Background: Sepsis-associated encephalopathy (SAE) is related to increased short-term mortality in patients with sepsis. We aim to establish a user-friendly nomogram for individual prediction of 30-day risk of mortality in patients with SAE. Methods: Data were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) open source clinical database. SAE was defined by Glasgow Coma Score (GCS) < 15 or delirium at the presence of sepsis. Prediction model with a nomogram was constructed in the training set by logistic regression analysis and then undergone internal validation and sensitivity analysis. Results: SAE accounted for about 50% in patients with sepsis and was independently associated with the 30-day mortality of sepsis. Variables eligible for the nomogram included patient’s age and clinical parameters on the first day of ICU admission including the GCS score, lactate, bilirubin, red blood cell distribution width (RDW), mean value of respiratory rate and temperature, and the use of vasopressor. Compared with Sequential Organ Failure Assessment (SOFA) and Logistic Organ Dysfunction System (LODS), the nomogram exhibited better discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.763 (95%CI 0.736–0.791, p < 0.001) and 0.753 (95%CI 0.713–0.794, p < 0.001) in the training and validation sets, respectively. The calibration plot revealed an adequate fit of the nomogram for predicting the risk of 30-day mortality in both sets. Regarding to clinical usefulness, the DCA of the nomogram exhibited greater net benefit than SOFA and LODS in both of the training and validation sets. Besides, the nomogram exhibited acceptable discrimination, calibration, and clinical usefulness in sensitivity analysis. Conclusions: SAE is related to increased 30-day mortality of patients with sepsis. The nomogram presents excellent performance in predicting 30-day risk of mortality in SAE patients, which can be used to evaluate the prognosis of patients with SAE and may be more beneficial once specific treatments towards SAE are developed.

Background: Both protective and therapeutic functions of insulin-like growth factor-1 (IGF-1) in brain injury have been reported, but its effects on cognitive sequelae after septic encephalopathy (SE) remain unclear. Materials and methods: This study was divided into three parts, and a septic model was built by cecal ligation and puncture (CLP). First, survival analysis was performed, and IGF-1’s effects on long-term cognition and depressive emotion were assessed. Second, the characteristics of IGF-1 function in cognition were evaluated. Finally, cytochrome C, caspase-9, tumor necrosis factor receptor (TNFR), and caspase-8 levels as well as cell apoptosis in the hippocampus were evaluated. Results: IGF-1 did not reduce mortality or alleviate depressive symptoms in septic rats, but improved the memory of noxious stimulation and spatial learning and memory. These effects were observed only when IGF-1 was administered within 0–6 hours after CLP. Moreover, cytochrome C and caspase-9 expression levels were increased at 6 hours after CLP in the hippocampus, while TNFR and caspase-8 amounts were not increased until 12 hours after CLP. Cell apoptosis increased at 12 hours after CLP, but was inhibited by IGF-1. Conclusion: Cognitive impairment in rats recovering from SE is associated with cell apoptosis in the hippocampus. Supplementation of IGF-1 reduces cell apoptosis by preventing the over-expression of cytochrome C and TNFR, and results in improved cognitive function. However, improvement only occurs when IGF-1 is administered at the early stage (within 6 hours) of sepsis. As cytochrome C activation occurs earlier than that of TNFR in this study, cytochrome C may be the main factor inducing apoptosis in early SE.

Hepatoma‑derived growth factor (HDGF) regulates various cellular processes involved in the onset and development of tumors. To evaluate the role of HDGF in human gliomas, western blotting analysis, immunohistochemistry staining and reverse transcription‑quantitative polymerase chain reaction were performed to detect HDGF protein and mRNA expression levels in glioma and intractable epileptic brain tissue. Various clinicopathological characteristics, including age, gender, World health Organization grade, HDGF expression level, Karnofsky performance Status (KPS) and Ki‑67 index were obtained from medical records. The correlation between HDGF expression and these clinicopathological characteristics was statistically evaluated. Following this, multivariate liner regression was used to evaluate their effect on patient survival time. HDGF expression, at the protein and mRNA levels, was observed to be more upregulated in glioma tissues compared with intractable epileptic brain tissue without tumor. Furthermore, the level of HDGF expression was positively associated with the grade of malignancy [grades Ⅱ~Ⅳ, Ki‑67 index ≥20% or KPS <80 (P<0.05)] and poor prognosis in glioma patients. Notably, the univariate survival analysis identified a negative correlation between HDGF‑expression and survival time (P<0.01) and multivariate liner regression demonstrated that HDGF expression is an independent prognostic factor for gliomas (P=0.01). Overall, HDGF upregulation may be a crucial step in the development and invasion of glioma. Further survival analysis highlighted its prognostic value for this malignancy, implying its potential as a promising therapeutic target for gliomas.

The correlation between the triglyceride-glucose index (TyG) and the prognosis of ischemic stroke has been well established. This study aims to assess the influence of the TyG index on the clinical outcomes of critically ill individuals suffering from intracerebral hemorrhage (ICH). Patients diagnosed with ICH were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Various statistical methods, including restricted cubic spline (RCS) regression, multivariable logistic regression, subgroup analysis, and sensitivity analysis, were employed to examine the relationship between the TyG index and the primary outcomes of ICH. A total of 791 patients from MIMIC-IV and 1,113 ones from eICU-CRD were analyzed. In MIMIC-IV, the in-hospital and ICU mortality rates were 14% and 10%, respectively, while in eICU-CRD, they were 16% and 8%. Results of the RCS regression revealed a consistent linear relationship between the TyG index and the risk of in-hospital and ICU mortality across the entire study population of both databases. Logistic regression analysis revealed a significant positive association between the TyG index and the likelihood of in-hospital and ICU death among ICH patients in both databases. Subgroup and sensitivity analysis further revealed an interaction between patients' age and the TyG index in relation to in-hospital and ICU mortality among ICH patients. Notably, for patients over 60 years old, the association between the TyG index and the risk of in-hospital and ICU mortality was more pronounced compared to the overall study population in both MIMIC-IV and eICU-CRD databases, suggesting a synergistic effect between old age (over 60 years) and the TyG index on the in-hospital and ICU mortality of patients with ICH. This study established a positive correlation between the TyG index and the risk of in-hospital and ICU mortality in patients over 60 years who diagnosed with ICH, suggesting that the TyG index holds promise as an indicator for risk stratification in this patient population.

Background: Sepsis-associated encephalopathy (SAE) is related to an increased in-hospital mortality in patients with sepsis. We aim to establish a user-friendly nomogram for individual prediction of in-hospital death probability in patients with SAE.Methods: Data were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) open source clinical database. SAE was defined by a Glasgow Coma Score (GCS) <15 at the presence of sepsis. Prediction model with a nomogram was constructed in the training set by Logistic regression analysis and then internally validated. A decision curve analysis (DCA) was performed to evaluate the net benefit of intervention with the nomogram.Results: A total of 669 and 287 patients with SAE were randomly assigned to training set and internal validation set according to an allocation ratio of 7:3, respectively. Parameters eligible for the nomogram included age, Sequential Organ Failure Assessment (SOFA) score, red blood cell distribution width (RDW) and the mean values of heart rate, temperature and respiratory rate at first day of ICU admission. The nomogram exhibited good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.773 (95%CI: 0.729–0.818) in the training set and 0.741 (95%CI 0.673–0.809) in the validation set, respectively. Calibration of the derived model was also excellent, with Brier score of 0.136 (95%CI: 0.12–0.153) and 0.168 (95%CI: 0.144–0.192) in both sets. The DCA of the nomogram indicated greater net benefit than SOFA. X-tile analysis showed that the nomogram can clearly stratify patients into three subgroups with different risks of hospital death. Conclusions: The nomogram presents excellent performance in predicting in-hospital mortality of SAE patients, which can guide the prevention of SAE progression and may be more beneficial once specific treatments towards SAE are developed.

BackgroundThe over-consumption of time for data collection in the Sequential Organ Failure Assessment (SOFA) score and the poor performance of the quickSOFA (qSOFA) score in predicting poor prognosis among patients with suspected infection in intensive care unit (ICU) may delay the treatment towards sepsis. The aim of this study is to develop a prediction model to early identify patients with suspected infection who are under a high risk of in-hospital mortality in ICU.MethodsPatients with suspected infection were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Objective variables whose results can be obtained within a short period of time were integrated into the uni- and multi-variate logistic regression to screen the independent predictors for the in-hospital mortality in ICU patients with suspected infection. Then, the prediction nomogram was constructed by these independent predictors in the training set, and undergone internal validation and sensitivity analysis.ResultsA total of 7000 patients with suspected infection were included into the final analysis. In the training set, multivariate logistic regression revealed that patient’s age, elixhauser index, the first result of lactate, red blood cell distribution width (RDW), white blood cell (WBC), lymphocyte, PH value and bicarbonate as well as the first hour’s mean value of respiratory rate, temperature and SpO2 since ICU admission were the independent predictors for the in-hospital mortality. Then, a nomogram for predicting the in-hospital mortality in ICU patients with suspected infection was developed in the training set and exhibited strong discrimination and calibration as well as great clinical usefulness in both of the training and validation sets as well as the sensitivity analysis.ConclusionsThe nomogram shows good predictive performance in ICU to identify adult patients with suspected infection who are likely to die in hospital, which may facilitate clinicians to initiate necessary treatment towards sepsis timely and finally improve the prognosis of septic patients.