基于Logistic回归模型评估肌电图震颤指标对帕金森病的诊断价值

Diagnostic value of electromyographic tremor indicators for Parkinson's disease based on Logistic regression model

  • 摘要:
    目的 基于Logistic回归模型探讨肌电图震颤指标对帕金森病(PD)的诊断价值。
    方法 选取65例PD患者(PD组)和39例特发性震颤(ET)患者(ET组)作为研究对象,均接受肌电图震颤分析,比较2组患者的一般资料、疾病相关资料和肌电图震颤特点。采用多因素Logistic回归分析法筛选PD的独立影响因素,并绘制受试者工作特征(ROC)曲线,通过曲线下面积(AUC)评估肌电图震颤指标对PD的诊断价值。
    结果 与ET组比较, PD组单侧肢体起病者和震颤波谱的波频数≥2个者占比更高,有震颤家族史者占比更低,差异有统计学意义(P < 0.05); PD组静息状态、姿势状态及负重状态(负重1 000 g)的震颤峰频率均低于ET组,差异有统计学意义(P < 0.05); 2组静息状态、负重状态的震颤节律形式差异有统计学意义(P < 0.05), PD组以交替收缩模式为主, ET组以同步收缩模式为主。多因素Logistic回归分析结果显示,负重状态震颤峰频率、静息状态震颤节律形式、震颤波谱的波频数均为PD的独立影响因素(P < 0.05)。ROC曲线显示,负重状态震颤峰频率、静息状态震颤节律形式、震颤波谱的波频数诊断PD的AUC分别为0.886、0.750、0.779, 且三者联合诊断PD的AUC最大(0.936),敏感度、特异度分别为81.54%、94.87%。
    结论 肌电图震颤分析提供的负重状态震颤峰频率、静息状态震颤节律形式及震颤波谱的波频数可作为早期诊断PD的临床指标,且三者联用的诊断价值更高,可用于鉴别诊断PD和ET。

     

    Abstract:
    Objective To investigate the diagnostic value of electromyographic (EMG) tremor indicators for Parkinson's disease (PD) using the Logistic regression model.
    Methods A total of 65 patients with PD (PD group) and 39 patients with essential tremor (ET) (ET group) were enrolled and underwent EMG tremor analysis. General information, disease-related data, and EMG tremor characteristics were compared between the two groups. Multivariate Logistic regression analysis was performed to screen for independent influencing factors of PD, and receiver operating characteristic (ROC) curves were plotted. The area under the curve (AUC) was used to evaluate the diagnostic value of EMG tremor indicators for PD.
    Results Compared with the ET group, the PD group had a higher proportion of patients with unilateral onset and those with tremor spectrum frequency ≥2 times, and a lower proportion of patients with a family history of tremor (P < 0.05). The tremor peak frequencies in the resting, postural, and weight-bearing (1 000 g) states were lower in the PD group than in the ET group (P < 0.05). There were statistically significant differences in the tremor rhythm patterns between the two groups in the resting and weight-bearing states (P < 0.05), with the PD group dominated by alternating contraction patterns and the ET group by synchronous contraction patterns. Multivariate Logistic regression analysis revealed that the tremor peak frequency in the weight-bearing state, the tremor rhythm pattern in the resting state, and the frequency of tremor spectrum were independent influencing factors of PD (P < 0.05). The ROC curves showed that the AUCs of the tremor peak frequency in the weight-bearing state, the tremor rhythm pattern in the resting state, and the frequency of tremor spectrum for diagnosing PD were 0.886, 0.750, and 0.779, respectively. The combination of these three indicators yielded the highest AUC (0.936) for diagnosing PD, with a sensitivity of 81.54% and a specificity of 94.87%.
    Conclusion The tremor peak frequency in the weight-bearing state, the tremor rhythm pattern in the resting state, and the frequency of tremor spectrum provided by EMG tremor analysis can serve as clinical indicators for early diagnosis of PD, and their combined use offers higher diagnostic value, which can be used to differentiate PD from ET.

     

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