Gait Parameters can Reflect Cognitive Performance in Older Adults with Cerebral Small Vessel Disease: A Cross-sectional Research.

BACKGROUND: Cerebral small vessel disease (CSVD) is a common chronic progressive disease. It remains unclear whether high gait variability is a marker of cognitive cortical dysfunction. METHODS: This study included 285 subjects (aged from 60 to 85 years, 60.3% female) including 37 controls, 179 presented as Fazekas II, and 69 presented as Fazekas III. The severity of white matter hyperintensities was assessed by the Fazekas Rating Scale. Gait parameters were assessed using a vision-based artificial intelligent gait analyzer. Cognitive function was tested by MMSE, MoCA, DST, and VFT. RESULTS: Three gait parameters including gait speed, gait length, and swing time were associated with cognitive performance in patients with CSVD. Gait speed was associated with cognitive performance, including MMSE (ß 0.200; 95%CI 1.706-6.018; p <.001), MoCA (ß 0.183; 95%CI 2.047-7.046; p <.001), DST (order) (ß 0.204; 95%CI 0.563-2.093; p =.001) and VFT (ß 0.162; 95%CI 0.753-4.865; p =.008). Gait length was associated with cognitive performance, including MMSE (ß 0.193; 95%CI 3.475-12.845; p =.001), MoCA (ß 0.213; 95%CI 6.098-16.942; p <.001), DST (order) (ß 0.224; 95%CI 1.056-4.839; P <.001) and VFT (ß 0.149; 95%CI 1.088- 10.114; p =.015). Swing time was associated with cognitive performance, including MMSE (ß - 0.242; 95%CI -2.639 to -0.974; p<.001), MoCA (ß -0.211; 95%CI -2.989 to -1.034; p <.001) and DST (reverse order) (ß -0.140; 95%CI -0.568 to -0.049; p =.020). CONCLUSION: This study revealed that the relationship between gait parameters and cognitive performance in patients with CSVD and the deteriorated gait parameters can reflect cognitive impairment and even dementia in older people with CSVD.

Sensitivity and specificity of high frequency ultrasound score (DCEC) in diabetic peripheral neuropathy.

Objectives: To summarize the ultrasonic characteristics of peripheral nerve damage in type 2 diabetes and to verify the diagnostic value of DCEC score for DPN. Methods: A total of 289 patients with type 2 diabetes evaluated peripheral neuropathy with neuroultrasound and nerve conduction at the Affiliated Hospital of Guizhou Medical University from June 2016 to June 2020. According to the diagnostic criteria from 2017 guidelines of China, 289 patients with type 2 diabetes were divided into three groups: DPN group: 203 cases; subclinical group: 48 cases; and non-DPN group: 38 cases. Kruskal Wallis test was used to identify the differences and characteristics of ultrasound scores between the all groups. The best cut-off value, sensitivity and specificity of DCEC score were obtained by receiver operator characteristic curve. Taking the diagnostic standard of diabetes peripheral neuropathy as the "gold standard", the best diagnostic threshold, sensitivity and specificity were obtained by drawing the ROC curve of DCEC score, and then the diagnostic value of DCEC score for DPN was verified. Results: Compared with non-DPN group, DCEC score in DPN group was significantly higher (P < 0.05). Otherwise,according to the ROC curve, the best cut-off value of DCEC score for DPN diagnosis was 12.5 (sensitivity 69.7%, specificity 71.1%). Conclusions: The DCEC score system can effectively diagnose DPN with length-dependence,mainly including the increase of definition score.

A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients.

BACKGROUND: Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. OBJECTIVES: This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. METHODS: This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital's Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., "patients weaned within 24 h" (n = 1042) and "patients not weaned within 24 h" (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. RESULTS: The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. CONCLUSIONS: The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.

Case Report: Successful Outcome for Refractory Diabetic Peripheral Neuropathy in Patients With Ultrasound-Guided Injection Treatment.

Diabetic peripheral neuropathy is the most prevalent chronic complication of diabetes and is based on sensory and autonomic nerve symptoms. Generally, intensive glucose control and nerve nourishment are the main treatments. However, it is difficult to improve the symptoms for some patients; such cases are defined as refractory diabetic peripheral neuropathy (RDPN). In this paper, we present five patients treated with saline and mecobalamin by ultrasound-guided injection. The Visual Analog Scale and Toronto Clinical Scoring System were used to evaluate the symptoms, and the neuro-ultrasound scoring system and electrophysiological severity scale were evaluated by ultrasound and electrophysiological examination. In brief, ultrasound-guided hydrodissection may be a safe way to treat RDPN.

Application of High-Resolution Ultrasound on Diagnosing Diabetic Peripheral Neuropathy.

Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). The typical manifestation is a length-dependent "glove and sock" sensation. At present, diagnosis is mainly dependent on clinical manifestations. Since the pathogenesis is not clear, there are no effective treatment measures. Management consists mainly of glucose control, peripheral nerve nutrition, and other measures to delay the progress of the disease; early diagnosis is therefore crucial to improving prognosis and quality of life for patients with DPN. Due to the lack of obvious symptoms in 50% of patients and the low sensitivity of neuro-electrophysiology to small fibers, the missed diagnosis rate is high. High-resolution ultrasound (HRU), as a convenient noninvasive tool, has been proven by many studies to have excellent clinical value in diagnosing DPN. With the development of related new technology, HRU shows promise for the screening, diagnosing, and follow-up of DPN, which could serve as a biomarker and provide new diagnostic insights. In this paper, we review the ability of HRU to detect nerve cross-sectional area and blood flow, and echo and other image changes, and in showing the characteristics of peripheral nerve morphological changes in patients with DPN. We also explore the application of two other recent technological developments-shear wave elastography (SWE) and ultrasound scoring systems-in improving the diagnostic efficiency of HRU in peripheral neuropathy.