OBJECTIVE: A-trains are a pathological pattern in intraoperative EMG-monitoring. Traintime, a parameter calculated by automated EMG-analysis, quantifies A-train activity. Its extent is associated with the degree of postoperative facial nerve palsy. However, false positive results have been observed. A systematic flaw in automated analysis was hypothesized. METHODS: Facial nerve EMG-data from 79 patients undergoing vestibular schwannoma surgery were analyzed visually. Automated traintime was compared with these results. The progressive risk for postoperative paresis was calculated with respect to traintime (visual and automated). RESULTS: Automated analysis identified a small (1.46%), but highly representative fraction of overall A-train activity: Pearson's correlation coefficient between both values was 0.944 (p<0.001). Both were correlated with clinical outcome in a highly significant way (p<0.001) with Spearman's Rho 0.592 (automated) and 0.563 (visual). Progressive risk development was visualized as an inverse sigmoid curve with traintime on a logarithmic scale. CONCLUSIONS: Automated traintime is a representative and reliable expression for overall A-train activity. As risk-development is complex, rigid thresholds are problematic. SIGNIFICANCE: Individual risk for postoperative palsy can be estimated on the basis of the calculated curve presented. This approach is of higher practical value than a rigid (and low) threshold.
OBJECTIVE: A-trains are a pathological pattern in intraoperative EMG-monitoring. Traintime, a parameter calculated by automated EMG-analysis, quantifies A-train activity. Its extent is associated with the degree of postoperative facial nerve palsy. However, false positive results have been observed. A systematic flaw in automated analysis was hypothesized. METHODS: Facial nerve EMG-data from 79 patients undergoing vestibular schwannoma surgery were analyzed visually. Automated traintime was compared with these results. The progressive risk for postoperative paresis was calculated with respect to traintime (visual and automated). RESULTS: Automated analysis identified a small (1.46%), but highly representative fraction of overall A-train activity: Pearson's correlation coefficient between both values was 0.944 (p<0.001). Both were correlated with clinical outcome in a highly significant way (p<0.001) with Spearman's Rho 0.592 (automated) and 0.563 (visual). Progressive risk development was visualized as an inverse sigmoid curve with traintime on a logarithmic scale. CONCLUSIONS: Automated traintime is a representative and reliable expression for overall A-train activity. As risk-development is complex, rigid thresholds are problematic. SIGNIFICANCE: Individual risk for postoperative palsy can be estimated on the basis of the calculated curve presented. This approach is of higher practical value than a rigid (and low) threshold.
Authors: Kathleen Seidel; Matthias S Biner; Irena Zubak; Jonathan Rychen; Jürgen Beck; Andreas Raabe Journal: Neurosurg Rev Date: 2018-10-26 Impact factor: 3.042
Authors: Tom Hendriks; Henricus P M Kunst; Maarten Huppelschoten; Jonne Doorduin; Mark Ter Laan Journal: Acta Neurochir (Wien) Date: 2020-03-07 Impact factor: 2.216