IntroductionStroke-associated pneumonia (SAP) is a common and serious complication in patients with acute severe stroke, and existing risk assessment tools have limited predictive accuracy in critically ill populations. This study innovatively incorporated frailty and nutritional risk, which reflect stress tolerance and overall physiological reserve, into an early SAP prediction model.MethodsA retrospective cohort study was conducted on 293 critically ill stroke patients admitted to the Neurocritical Care Unit of the First Affiliated Hospital of Chongqing Medical University between 2013 and 2024. Collect clinical characteristics and laboratory indicators of patients, assess their frailty status and nutritional risk, and analyze the additive interaction effect between the two on the occurrence of SAP. Independent predictors were identified through multivariate logistic regression and incorporated into a visual nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis, and 10-fold cross-validation.ResultsA total of 293 patients with severe stroke were included in this study, among whom 126 (43%) developed SAP. The results of the additive interaction analysis showed a positive additive interaction between frailty and nutritional risk in the development of SAP, with an attributable proportion (AP) of 0.711 (95% CI = 0.358 ~ 1.065) and a synergy index (SI) of 3.694 (95% CI = 1.200 ~ 11.364). A SAP risk prediction model incorporating age, nasogastric tube use, neutrophil-to-lymphocyte ratio (NLR), frailty status, and nutritional risk demonstrated good discriminative performance, with an area under the curve (AUC) of 0.848, which was significantly higher than that of the conventional SAP prediction score (ISAN score: AUC = 0.589). Internal validation showed that the model achieved an accuracy of 73.93%, sensitivity of 77.30%, and specificity of 71.95%, indicating good stability. The calibration curve demonstrated good agreement between predicted and observed outcomes. Decision curve analysis (DCA) indicated that the model provided substantially greater clinical net benefit than the ISAN score.DiscussionThis study is the first to integrate frailty and nutritional risk into an SAP prediction model, significantly improving early risk identification and providing an innovative, practical tool for precision prevention and targeted intervention in critically ill stroke patients.

ObjectivesTo develop a Simplified EEG Risk Score (SERS) for accurate and time-robust prognostic assessment in comatose patients after cardiac arrest.MethodsThis retrospective cohort study enrolled comatose patients after cardiac arrest admitted to the ICU of the First Affiliated Hospital of Chongqing Medical University between January 2020 and December 2024. Univariate logistic regression was used to derive β-coefficients for EEG features, including background amplitude, dominant frequency, continuity, reactivity, and sleep elements, which were incorporated into the SERS. Multivariate logistic regression identified independent predictors of poor neurological outcome. Prognostic performance was evaluated at Day 1, Days 2–5, and >5 days after cardiac arrest using receiver operating characteristic curves, with assessment of the area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsA total of 251 comatose patients after cardiac arrest were included; 45 (18%) had good outcomes and 206 (82%) had poor outcomes. Abnormal background amplitude, slow dominant frequency (δ/θ), discontinuous background, absent reactivity, and absent sleep waveforms were all significantly associated with poor prognosis (P < 0.001). The AUCs of SERS were 0.912, 0.893, and 0.881 at Day 1, Days 2–5, and >5 days, respectively, outperforming individual EEG features, commonly used EEG scores, and traditional clinical predictors. Risk stratification demonstrated clear separation of prognosis across low-, medium-, and high-risk groups at all time windows.ConclusionsSERS shows high predictive accuracy and temporal stability, enabling reliable risk stratification for poor neurological outcomes in comatose patients after cardiac arrest, supporting its potential clinical utility.

Background:New-onset atrial fibrillation (NOAF) is a common cardiovascular complication in critically ill patients and is consistently associated with adverse outcomes. However, substantial heterogeneity exists in its clinical presentation and prognosis. This study aimed to identify distinct clinical subtypes of NOAF and evaluate their prognostic and management-related implications. Methods:Adult NOAF patients were extracted from the MIMIC-IV database. Demographic and laboratory data within 24 h of ICU admission were analyzed. Consensus k-means clustering was used for identifying subtypes. Survival differences were compared using Kaplan-Meier and log-rank tests, and multivariable Cox models assessed mortality risk and pharmacologic treatment associations. Key variables identified by SHAP analysis were incorporated into a simplified six-variable model, validated externally in MIMIC-III. Results:Among 8472 NOAF patients, four distinct subtypes were identified from the MIMIC-IV cohort (n = 5554), showing progressively increased severity and mortality. Subtype A (30.28%) included mainly post-cardiac surgery patients with preserved homeostasis and the lowest 28-day mortality (4.9%). Subtype B (34.52%) was characterized by marked hypomagnesemia and a moderate burden of comorbid malignancy (28-day mortality 15.5%). Subtype C (19.70%) featured anemia, hypoxemia, and inflammation (28-day mortality 30.2%). Subtype D (15.50%) presented with organ failure and the highest 28-day mortality (42.7%). 28-day mortality risk increased stepwise across subtypes (HR 4.24-5.98; all P < 0.001). Pharmacologic responses, including heart rate control, sedation, and electrolyte therapy, varied across different subtypes. The simplified six-variable model demonstrated high predictive performance (AUC 0.89-0.96) in external validation. Conclusion:Unsupervised clustering revealed four distinct NOAF subtypes in ICU patients, characterized by heterogeneous clinical trajectories. The simplified six-variable model enabled practical bedside classification, supporting precision risk assessment and potentially informing phenotype-oriented management of NOAF in the ICU.

OBJECTIVE:This study aimed to develop and validate machine learning (ML) models for predicting the prognosis of status epilepticus (SE) patients with multisystem complications. METHODS:We developed predictive models using six ML algorithms: least absolute shrinkage and selection operator (LASSO) logistic regression, k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). We systematically evaluated the prognostic performance of these models against established clinical scores. Specifically, we compared them with the Status Epilepticus Severity Score (STESS), the Encephalitis-Nonconvulsive Status Epilepticus-Diazepam Resistance-Imaging Abnormalities-Tracheal Intubation (ENDIT) score, and the Epidemiology-based Mortality Score in Status Epilepticus (EMSE). RESULTS:A total of 169 patients with SE were included in this study. In the test dataset, the areas under the curve (AUC) of the models were 0.660 for DT, 0.644 for RF, 0.663 for SVM, 0.689 for KNN, 0.825 for XGBoost, and 0.610 for LASSO logistic regression.The SHAP analysis revealed the top ten predictors contributing to the XGBoost model: hypoalbuminemia, nutritional risk score, age, ventilation duration, NCSE, GCS score, duration of impaired consciousness, creatinine level, APACHE II score, and CCI. CONCLUSION:Compared with the other models and scoring systems, XGBoost demonstrated superior predictive performance, suggesting its potential utility for the early identification of high-risk patients and timely clinical intervention. Hypoalbuminemia was identified as the most important prognostic factor, highlighting the critical role of systemic injury in determining adverse outcomes in SE patients treated within the neurocritical care setting.

BACKGROUND AND PURPOSE:Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease of the central nervous system. It remains unclear whether pathological changes in ALS can lead to abnormalities in neural dynamics and how these abnormalities relate to key clinical characteristics of ALS. METHODS:Nonlinear neural dynamics analyses of electroencephalography (EEG) sensorimotor channels were conducted using recurrence quantification analysis (RQA), permutation Lempel-Ziv complexity (PLZC), shannon entropy (ShannonE) and permutation entropy (PermEn). Whole-brain spatiotemporal topological dynamics were assessed using microstate analysis. RESULTS:The study shows that the nonlinear neural dynamics across all frequency bands in the sensorimotor channels of ALS patients are impaired (reduction in Shannon Entropy of Diagonal Line Length Distribution [ENTR]). Frequency-specific nonlinear neural dynamics indicate increased nonlinear neural dynamics in high-frequency bands (with increases in PLZC in beta2 (20-25 Hz)). Nonlinear neural dynamics in low-frequency bands decreases (with decreases in ShannonE in theta (4-7 Hz)) and is negatively correlated with disease duration. ENTR across all frequency bands and ShannonE in the theta band of the sensorimotor channels are potential protective factors for ALS. Furthermore, sensorimotor channel analysis shows a close relationship with whole-brain spatiotemporal topological neural dynamics. Duration A is positively correlated with RR, DET and ENTR, while Occurrence B is negatively correlated with it. CONCLUSIONS:The study demonstrates widespread abnormalities in the neural dynamics of ALS, which are closely related to clinical characteristics of ALS. There is also a close relationship between the neural dynamics of sensorimotor channels and whole-brain spatiotemporal topological dynamics in ALS patients.

Advancements in diagnostic imaging have improved the detection of brain metastases, which are now recognized as the most common intracranial malignancies. While surgical intervention may be indicated for various reasons – including histopathological confirmation, relief of mass effect, neurological improvement, and survival benefits – evidence suggests that gross total resection is particularly important for enhancing both progression-free and overall survival. However, the management of brain metastases remains largely subjective and controversial. This review aims to critically evaluate existing evidence regarding the role of surgical intervention in the treatment of brain metastases.

OBJECTIVE:To investigate the clinical manifestations of secondary epilepsy (EP) in children with viral encephalitis and to identify any associated risk factors. METHODS:A retrospective analysis was conducted on 130 children with viral encephalitis treated at Lu'an People's Hospital Affiliated with Anhui Medical University between December 2021 and October 2024. Of these, 36 children who developed secondary EP were classified as the EP group, and 94 children without secondary EP were categorized as the non-EP group. The overall incidence of secondary EP, clinical symptoms, cerebrospinal fluid (CSF) indices, and electroencephalogram (EEG) findings were compared between the groups. Multivariate logistic regression analysis was employed to identify independent risk factors for the development of secondary EP. RESULTS:Of the 130 children with viral encephalitis, 36 (27.69%) developed secondary EP. Among them, 10 children (27.78%) had self-limited generalized EP, and 26 children (72.22%) had self-limited focal EP. Status epilepticus occurred in 7/36 cases (19.44%), but not in the other 29/36 cases (80.56%). No notable differences were observed in fever, headache, drowsiness, and coma between the EP group and non-EP group (P>0.05). However, vomiting and coma were significantly more frequent in the EP group (P<0.05). Abnormal EEG findings were also more prevalent in the EP group compared to the non-EP group (P<0.05). Logistic regression analysis identified non-use of antiepileptic drugs (P=0.039; CI: 0.181-0.958), elevated white blood cell count in CSF (P=0.006; CI: 1.028-1.185), and moderate to severe abnormal EEG results (P=0.041; CI: 1.035-5.41) as independent risk factors for the occurrence of secondary EP in children with viral encephalitis. CONCLUSION:The incidence of secondary EP in children with viral encephalitis is relatively high. Non-use of antiepileptic drugs, elevated white blood cell count in the CSF, and moderate to severe abnormal EEG results were independent risk factors for the occurrence of secondary EP in children with viral encephalitis.

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OBJECTIVE:We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work. METHODS:This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model. RESULTS:After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979). CONCLUSION:A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge. CLINICAL IMPLICATION:This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

OBJECTIVE The aim of this study is to explore the application effectiveness and value of combining problem-based learning (PBL) and case-based learning (CBL) in clinical electroencephalography (EEG) education. METHODS A total of 104 standardized training for residents and refresher physicians from the Neurology Department of the First Affiliated Hospital of Chongqing Medical University, Neurology Department of Chongqing Yubei Hospital, and Neurology Department of Banan Hospital of Chongqing Medical University were enrolled. According to randomization principles, 52 participants were assigned into the PBL-CBL combination group and 52 subjects were assigned into the control group. We used statistical methods to compare the differences between the 2 groups in basic theory, case analysis, practical assessment scores, and teaching satisfaction. RESULTS In terms of basic theory, case analysis, practical assessment scores, and teaching satisfaction, there were significant differences between the 2 groups, and the PBL-CBL combination group was superior to the control group ( P < .05). CONCLUSION In clinical EEG education, the teaching model of combining PBL and CBL has certain application effects and value.

This study investigates the obesity paradox, where obesity is linked to lower mortality in certain patient groups, focusing on its impact on long-term mortality in chronic critically ill (CCI) patients. We retrospectively analyzed CCI patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database’s Intensive Care Unit, categorizing them into six groups based on Body Mass Index (BMI). Using stepwise multivariable Cox regression and restricted cubic spline models, we examined the association between BMI and 90 day mortality, accounting for confounding variables through subgroup analyses. The study included 1996 CCI patients, revealing a 90 day mortality of 34.12

OBJECTIVES:To explore the factors associated with poor prognosis in critically ill patients with Electroencephalogram (EEG) patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and to construct a prognostic prediction model. METHODS:This study included a total of 53 critically ill patients with EEG patterns exhibiting SIRPIDs who were admitted to the First Affiliated Hospital of Chongqing Medical University from May 2023 to March 2024. Patients were divided into two groups based on their Modified Rankin Scale (mRS) scores at discharge: good prognosis group (0-3 points) and poor prognosis group (4-6 points). Retrospective analyses were performed on the clinical and EEG parameters of patients in both groups. Logistic regression analysis was applied to identify the risk factors related to poor prognosis in critically ill patients with EEG patterns exhibiting SIRPIDs; a risk prediction model for poor prognosis was constructed, along with an individualized predictive nomogram model, and the predictive performance and consistency of the model were evaluated. RESULTS:Multivariate logistic regression analysis revealed that APACHE II score (OR=1.217, 95 %CI=1.030∼1.438), slow frequency bands or no obvious brain electrical activity (OR=8.720, 95 %CI=1.220∼62.313), and no sleep waveforms (OR=9.813, 95 %CI=1.371∼70.223) were independent risk factors for poor prognosis in patients. A regression model established based on multivariate logistic regression analysis had an area under the curve of 0.902. The model's accuracy was 90.60 %, with a sensitivity of 92.86 % and a specificity of 89.70 %. The nomogram model, after internal validation, showed a concordance index of 0.904. CONCLUSIONS:A high APACHE II score, EEG patterns with slow frequency bands or no obvious brain electrical activity, and no sleep waveforms were independent risk factors for poor prognosis in patients with SIRPIDs. The nomogram model constructed based on these factors had a favorably high level of accuracy in predicting the risk of poor prognosis and held certain reference and application value for clinical neurofunctional assessment and prognostic determination.

Background and objective: Neurocritical patients often experience uncontrolled high catabolic metabolism state during the acuta phase of the disease. The complex interactions of neuroendocrine, inflammation, and immune system lead to massive protein breakdown and changes in body composition. Bioelectrical impedance analysis (BIA) evaluates the content and proportions of body components based on the principles of bioelectricity. Its parameters reflect the overall health status of the body and the integrity of cellular structure and function, playing an important role in assessing the disease status and predicting prognosis of such patients. This study explored the association of BIA parameters trajectories with clinical outcomes in neurocritical patients. Methods: This study prospectively collected BIA parameters of 127 neurocritical patients in the Department of Neurology admitted to the NICU for the first 1–7 days. All these patients were adults (≥18 years old) experiencing their first onset of illness and were in the acute phase of the disease. The group-based trajectory modeling (GBTM), which aims to identify individuals following similar developmental trajectories, was used to identify potential subgroups of individuals based on BIA parameters. The short-term prognosis of patients in each trajectory group with variations in phase angle (PA) and extracellular water/total body water (ECW/TBW) over time was differentially analyzed, and the logistic regression model was used to analyze the relationship between potential trajectory groups of PA and ECW/TBW and the short-term prognosis of neurocritical patients. The outcome was Glasgow Outcome Scale (GOS) score at discharge. Results: Four PA trajectories and four ECW/TBW trajectories were detected respectively in neurocritical patients. Among them, compared with the other latent subgroups, the “Low PA rapidly decreasing subgroup” and the “High ECW/TBW slowly rising subgroup” had higher incidences of adverse outcomes at discharge (GOS:1–3), in-hospital mortality, and length of neurology intensive care unit stay (all P < 0.05). After correcting for potential confounders, compared with the “Low PA rapidly decreasing subgroup”, the risk of adverse outcome (GOS:1–3) was lower in the other three PA trajectories, with OR values of 0.0003, 0.0004, and 0.003 respectively (all P < 0.05). Compared with the “High ECW/TBW slowly rising subgroup”, the risk of adverse outcome (GOS:1–3) was lower in the other three ECW/TBW trajectories, with OR values of 0.013, 0.035 and 0.038 respectively (all P < 0.05). Conclusion: Latent PA trajectories and latent ECW/TBW trajectories during 1–7 days after admission were associated with the clinical outcomes of neurocritical patients. The risk of adverse outcomes was highest in the “Low PA rapidly decreasing subgroup” and the “High ECW/TBW slowly rising subgroup”. These results reflected the overall health status and nutritional condition of neurocritical patients at the onset of the disease, and demonstrated the dynamic change process in body composition caused by the inflammatory response during the acute phase of the disease. This provided a reference basis for the observation and prognostic evaluation of such patients.

Large Hemispheric Infarction (LHI) is a devastating disease with high mortality. This study aimed to use electroencephalography (EEG) to evaluate the death risk of LHI patients and identify suitable evaluation time. This study retrospectively collected clinical and EEG data from 73 LHI patients, dividing them into death and survival group at discharge. EEG data was classified as 1–5 days and 6–14 days after onset according to the time intervals of cerebral edema. Regression and receiver operator characteristic curve (ROC) analysis were applied to explore the impact of temporal changes in various EEG and clinical features on death. The areas under ROC curve (AUC) of death prediction for non-α frequency on non-infarct side at 6–14 days after onset was significantly higher than that at 1–5 days (p = 0.004). And there was no significant difference between the AUC of seizure activity for death prediction at 1–5 days and 6–14 days (p = 0.418). Multivariate regression analysis revealed that non-α frequency on non-infarct side and seizure activity at 6–14 days after onset were the independent risk factors for the death of LHI patients. Additionally, above two EEG features significantly improved the death predictive efficacy of clinical features in LHI patients with the integrated discrimination improvement index (IDI) of 0.174 (p = 0.015) and the net reclassification improvement (NRI) of 1.314 (p<0.001). Non-α frequency on non-infarct side and seizure activity were reliable indicators for death prediction. 6–14 days after onset was the better time window for death evaluation of LHI patients through EEG.

Triple-negative breast cancer (TNBC) is a special subtype of breast cancer, which is highly aggressive and incurable. Here, we proposed an ultrasound activatable bromodomain-containing protein 4 (BRD4) proteolysis targeting chimera (PROTAC) release strategy for the first time for precisely controlled protein degradation in preclinical TNBC model. Through combination of PROTAC and ultrasound-targeted microbubble destruction (UTMD) technology, the present strategy also aims to concurrently solve the major limitations of poor loading capacity of microbubbles and undesirable targeting and membrane permeability of PROTAC. PROTAC (ARV-825)-encapsulated microbubbles, ARV-MBs, were developed for the efficacious treatment of TNBC in vitro and in vivo. The microbubbles we synthesized showed ultrasound-responsive drug release ability, which could effectively promote the penetration of PROTAC into tumor site and tumor cell. Under ultrasound, ARV-MBs could play an effective antitumor effect by potentiating the ubiquitination and degradation of BRD4 in tumor. The current study may provide a new idea for promoting clinical translation of drug-loaded microbubbles and PROTAC, and offer a new efficacious therapeutic modality for TNBC.

The short-term prognosis of stroke patients is mainly influenced by the severity of the primary disease at admission and the trend of disease development during the acute phase (1–7 days after admission). The aim of this study is to explore the relationship between the bioelectrical impedance analysis (BIA) parameter trajectories during the acute phase of stroke patients and their short-term prognosis, and to investigate the predictive value of the prediction model constructed using BIA parameter trajectories and clinical indicators at admission for short-term prognosis in stroke patients. A total of 162 stroke patients were prospectively enrolled, and their clinical indicators at admission and BIA parameters during the first 1–7 days of admission were collected. A Group-Based Trajectory Model (GBTM) was employed to identify different subgroups of longitudinal trajectories of BIA parameters during the first 1–7 days of admission in stroke patients. The random forest algorithm was applied to screen BIA parameter trajectories and clinical indicators with predictive value, construct prediction models, and perform model comparisons. The outcome measure was the Modified Rankin Scale (mRS) score at discharge. PA in BIA parameters can be divided into four separate trajectory groups. The incidence of poor prognosis (mRS: 4–6) at discharge was significantly higher in the “Low PA Rapid Decline Group” (85.0

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.