【摘要】 目的 探討肌電生物反饋治療對腦卒中偏癱患肢上肢腕背伸功能的影響。方法 將36例腦卒中偏癱患者隨機分為治療組和對照組,每組18例。兩組藥物治療相同,對照組進行常規康復治療,治療組在常規康復治療基礎上加肌電生物反饋技術進行治療。觀察兩組治療前后腕背伸時主動關節活動范圍(AROM),腕背伸時肌肉最大收縮時肌電(EMG)閾值。 結果 3個療程后治療組患者腕關節的AROM、EMG閾值均優于對照組(P＜0.001)。 結論 肌電生物反饋治療有助于明顯改善偏癱患者腕背伸功能。【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.