Wieser M, Buetler L, Koenig A, Riener R
Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Tannenstrasse 1, 8092, Switzerland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5533-6. doi: 10.1109/IEMBS.2010.5626763.
Clinical scales represent the golden standard in characterizing awareness for patients in vegetative or in a minimally conscious state. Clinical scales suffer from problems of sensitivity, specificity, subjectivity, and inter-rater reliability. This leads to a misdiagnosis rate of up to 40% and consequences associated with inappropriate treatment decisions. In this study, objective measures including physiological and neurological signals are used to quantify the patient status. Using linear backward regression analysis, 13 variables (based on frequency analysis of the electrocardiogram, heart rate variability, amplitude and latency of the P300, skin conductance responses, changes in the blood pressure and respiration signal) were found to be sufficient to describe 74.7% of the variability of the scores. In this regression model, the P300, electrocardiogram and the blood pressure signal account for most of the variability. More patient data and additional measures will enable refinement of the methods. This new objective-measurement based model of the state of awareness will complement the clinical scales in order to increase the quality of diagnosis.
临床量表是判定植物人或微意识状态患者意识水平的金标准。临床量表存在敏感性、特异性、主观性及评分者间信度等问题。这导致误诊率高达40%，并产生与不恰当治疗决策相关的后果。在本研究中，采用包括生理和神经信号在内的客观测量方法来量化患者状态。通过线性向后回归分析，发现13个变量（基于心电图频率分析、心率变异性、P300的幅度和潜伏期、皮肤电反应、血压和呼吸信号变化）足以描述分数变异性的74.7%。在该回归模型中，P300、心电图和血压信号占大部分变异性。更多的患者数据和额外的测量方法将使这些方法得到完善。这种基于客观测量的意识状态新模型将补充临床量表，以提高诊断质量。
