Evaluation of extravascular lung water index in critically ill patients based on lung ultrasound radiomics analysis combined with machine learning / 中华危重病急救医学

OBJECTIVE@#To explore lung ultrasound radiomics features which related to extravascular lung water index (EVLWI), and to predict EVLWI in critically ill patients based on lung ultrasound radiomics combined with machine learning and validate its effectiveness.@*METHODS@#A retrospective case-control study was conducted. The lung ultrasound videos and pulse indicated continuous cardiac output (PiCCO) monitoring results of critically ill patients admitted to the department of critical care medicine of the First Affiliated Hospital of Guangxi Medical University from November 2021 to October 2022 were collected, and randomly divided into training set and validation set at 8:2. The corresponding images from lung ultrasound videos were obtained to extract radiomics features. The EVLWI measured by PiCCO was regarded as the "gold standard", and the radiomics features of training set was filtered through statistical analysis and LASSO algorithm. Eight machine learning models were trained using filtered radiomics features including random forest (RF), extreme gradient boost (XGBoost), decision tree (DT), Naive Bayes (NB), multi-layer perceptron (MLP), K-nearest neighbor (KNN), support vector machine (SVM), and Logistic regression (LR). Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of models on EVLWI in the validation set.@*RESULTS@#A total of 151 samples from 30 patients were enrolled (including 906 lung ultrasound videos and 151 PiCCO monitoring results), 120 in the training set, and 31 in the validation set. There were no statistically significant differences in main baseline data including gender, age, body mass index (BMI), mean arterial pressure (MAP), central venous pressure (CVP), heart rate (HR), cardiac index (CI), cardiac function index (CFI), stroke volume index (SVI), global end diastolic volume index (GEDVI), systemic vascular resistance index (SVRI), pulmonary vascular permeability index (PVPI) and EVLWI. The overall EVLWI range in 151 PiCCO monitoring results was 3.7-25.6 mL/kg. Layered analysis showed that both datasets had EVLWI in the 7-15 mL/kg interval, and there was no statistically significant difference in EVLWI distribution. Two radiomics features were selected by using LASSO algorithm, namely grayscale non-uniformity (weight was -0.006 464) and complexity (weight was -0.167 583), and they were used for modeling. ROC curve analysis showed that the MLP model had better predictive performance. The area under the ROC curve (AUC) of the prediction validation set EVLWI was higher than that of RF, XGBoost, DT, KNN, LR, SVM, NB models (0.682 vs. 0.658, 0.657, 0.614, 0.608, 0.596, 0.557, 0.472).@*CONCLUSIONS@#The gray level non-uniformity and complexity of lung ultrasound were the most correlated radiomics features with EVLWI monitored by PiCCO. The MLP model based on gray level non-uniformity and complexity of lung ultrasound can be used for semi-quantitative prediction of EVLWI in critically ill patients.

Research progress on transcranial electrical stimulation for deep brain stimulation / 生物医学工程学杂志

Transcranial electric stimulation (TES) is a non-invasive, economical, and well-tolerated neuromodulation technique. However, traditional TES is a whole-brain stimulation with a small current, which cannot satisfy the need for effectively focused stimulation of deep brain areas in clinical treatment. With the deepening of the clinical application of TES, researchers have constantly investigated new methods for deeper, more intense, and more focused stimulation, especially multi-electrode stimulation represented by high-precision TES and temporal interference stimulation. This paper reviews the stimulation optimization schemes of TES in recent years and further analyzes the characteristics and limitations of existing stimulation methods, aiming to provide a reference for related clinical applications and guide the following research on TES. In addition, this paper proposes the viewpoint of the development direction of TES, especially the direction of optimizing TES for deep brain stimulation, aiming to provide new ideas for subsequent research and application.

A qualitative study on the current situation of hospital-institution-community comprehensive prevention of stroke / 中国实用护理杂志

Objective:To deeply explore the real work of primary, secondary and tertiary prevention of stroke in general hospitals, rehabilitation and pension institutions and community health service centers (stations), and to provide reference for improving the level of comprehensive prevention of stroke in the medical system.Methods:From July to November 2022, face-to-face semi-structured interviews with 18 medical staff in general hospitals, 10 medical staff in rehabilitation and pension institutions, and 8 medical staff in community health service centers (stations) were conducted by phenomenological research methods. Colaizzi 7-step analysis method was used to analyze the interview data, and Nvivo12 software was used to code the interview data.Results:Four themes were extracted: hospitals, institutions and communities all played an important role in the comprehensive prevention of stroke and actively cooperated with each other; the public was still lack of comprehensive prevention awareness and correct cognition of stroke; lack of professional medical resources hindered the development of comprehensive stroke prevention; incomplete collaborative mechanism of comprehensive prevention of stroke in hospitals, institutions and communities.Conclusions:In the current comprehensive prevention of stroke, there are still problems such as weak public awareness of prevention, insufficient professional medical resources, and imperfect coordination mechanism. In view of the advantages of medical institutions at all levels, it is necessary to clarify the responsibilities of the system at all levels and explore efficient and sustainable coordination mechanism, so as to improve the comprehensive prevention level of stroke in the medical system.