Retrospective Analysis of Steady-State Sodium Valproate Plasma Concentrations in Chinese Patients With Bipolar Disorder: Impact of Demographic and Clinical Characteristics.

BACKGROUND: This study comprehensively examined the demographic and clinical characteristics of patients undergoing valproic acid therapy and explored their potential impact on plasma valproic acid concentrations. All enrolled patients were administered the extended-release formulation. An in-depth investigation of factors, including dose, age, sex, body mass index, co-administered medications, and laboratory test findings, was conducted to evaluate their potential influence on study outcomes. METHODS: In total, 164 patients met the inclusion criteria and were included in the analysis. The patient age ranged from 13 to 60 years, with a median age of 25.71 years. Most patients (89%) received a daily dose of 1 g valproic acid. Co-administered psychiatric medications included aripiprazole, quetiapine, and lorazepam. Laboratory test results, such as hemoglobin and transaminase levels, were also collected as part of the study. RESULTS: The average plasma valproic acid plasma concentration was 79.8 mg/L. The dose significantly affected valproic acid concentrations, as a higher percentage of measurements exceeded the therapeutic range at a daily dose of 1 g. Furthermore, females exhibited significantly higher valproic acid concentrations compared with males at the same dose (P < 0.05). However, different age groups showed no statistically significant differences in valproic acid concentrations (P > 0.05). The co-administered antipsychotic and antidepressant medications significantly affected valproate concentrations, as reflected in the multiple regression model (P < 0.01). CONCLUSIONS: This study offers valuable insights into the demographic and clinical characteristics of patients undergoing valproic acid therapy. It highlights the influence of dose, sex, and concomitant medications on plasma valproic acid concentrations. Overall, these findings can help guide dose adjustments and implement personalized treatment strategies in valproic acid therapy.

A wearable action recognition system based on acceleration and attitude angles using real-time detection algorithm.

Falls are a main cause of trauma and death. The purpose of this study is to adopt unique resultant acceleration and attitude angles to distinguish falls from activities of daily life before impact. In this study, we developed a wearable action recognition system to acquire action data. The moving average filter was employed to deal with raw data, and then complementary filter was adopted to compromise sensor data for attitude angles. The real-time detection algorithm embedded in this device was applied to recognize six actions based on processed data. Eight subjects (five males, three females) participated in the experiment. The optimal features and related thresholds were extracted. In addition, the real-time action detection results indicated that the real-time action recognition model reached an accuracy of 96.25%, with 98% for male and 93.3% for female. Thus, our device potentially achieves a high sensitivity of fall-related actions recognition.

Fall risk factors analysis based on sample entropy of plantar kinematic signal during stance phase.

Falls are a multi-causal phenomenon with a complex interaction. The aim of our research is to study the effect of multiple variables for potential risk of falls and construct an elderly fall risk assessment model based on demographics data and gait characteristics. A total of 101 subjects, whom belong to Malianwa Street, aged above 50 years old and participated in questionnaire survey. Participants were classified into three groups (high, medium and low risk group) according to the score of elderly fall risk assessment scale. In addition, the data of ground reaction force (GRF) and ground reaction moment (GRM) was record when they performed walking at comfortable state. The demographic variables, sample entropy of GRF and GRM, and impulse difference of bilateral foot were considered as potential explanatory variables of risk assessment model. Firstly, we investigated whether different groups could present difference in every variable. Statistical differences were found for the following variables: age (p=2.28e-05); impulse difference (p=0.02036); sample entropy of GRF in vertical direction (p=0.0144); sample entropy of GRM in anterior-posterior direction (p=0.0387). Finally, the multiple regression analysis results indicated that age, impulse difference and sample entropy of resultant GRM could identify individuals who had different levels of fall risk. Therefore, those results could potentially be useful in the fall risk assessment and monitor the state of physical function in elderly population.