【摘要】 目的 探讨肌电生物反馈治疗对脑卒中偏瘫患肢上肢腕背伸功能的影响。方法 将36例脑卒中偏瘫患者随机分为治疗组和对照组,每组18例。两组药物治疗相同,对照组进行常规康复治疗,治疗组在常规康复治疗基础上加肌电生物反馈技术进行治疗。观察两组治疗前后腕背伸时主动关节活动范围(AROM),腕背伸时肌肉最大收缩时肌电(EMG)阈值。 结果 3个疗程后治疗组患者腕关节的AROM、EMG阈值均优于对照组(P＜0.001)。 结论 肌电生物反馈治疗有助于明显改善偏瘫患者腕背伸功能。【Abstract】 Objective To explore the effect of the electromyographic biofeedback therapy on the extension of wrist joint of the hemiplegic patients after stroke. Methods Thirtysix hemiplegic patients were included and were divided into two groups randomly, including a treatment group and a control group. They were treated with the same drugs and the routine rehabilitation therapy while the patients in the treatment group still received the electromyographic biofeedback therapy additionally. Results After three courses of treatment, the patients in the treatment group had better active range of movement (AROM) of extension of wrist joint and also higher electromyographic (EMG) threshold of maximum contraction of muscle than the patients in the control group (Plt;0.001). Conclusion The electromyographic biofeedback therapy has good effect on improving the function of the wrist of hemiplegic patients after stroke.
Objective To develop a machine learning (ML) model to predict the risk of death in intensive care unit (ICU) patients with chronic obstructive pulmonary disease (COPD), explain the factors related to the risk of death in COPD patients, and solve the "black box" problem of ML model. Methods A total of 8088 patients with severe COPD were selected from the eICU Collaborative Research Database (eICU-CRD). Data within the initial 24 hours of each ICU stay were extracted and randomly divided, with 70% for model training and 30% for model validation. The LASSO regression was deployed for predictor variable selection to avoid overfitting. Five ML models were employed to predict in-hospital mortality. The prediction performance of the ML models was compared with alternative models using the area under curve (AUC), while SHAP (SHapley Additive exPlanations) method was used to explain this random forest (RF) model. Results The RF model performed best among the APACHE IVa scoring system and five ML models with the AUC of 0.830 (95%CI 0.806 - 0.855). The SHAP method detects the top 10 predictors according to the importance ranking and the minimum of non-invasive systolic blood pressure was recognized as the most significant predictor variable. Conclusion Leveraging ML model to capture risk factors and using the SHAP method to interpret the prediction outcome can predict the risk of death of patients early, which helps clinicians make accurate treatment plans and allocate medical resources rationally.