Charité - Universitätsmedizin Berlin, Klinik für Anästhesiologie mit Schwerpunkt Operative Intensivmedizin, Campus Charité Mitte und Campus Virchow-Klinikum, Berlin, Germany. Electronic address: https://www.charite.de.
Charité - Universitätsmedizin Berlin, Klinik für Anästhesiologie mit Schwerpunkt Operative Intensivmedizin, Campus Charité Mitte und Campus Virchow-Klinikum, Berlin, Germany.
Clin Neurophysiol. 2018 Mar;129(3):572-583. doi: 10.1016/j.clinph.2017.11.030. Epub 2017 Dec 28.
A variety of algorithms is used for nociceptive flexion reflex threshold (NFRT) estimation, but their estimation accuracy is unknown. We developed a computer based simulation model of the NFRT to quantify and compare the accuracy of available estimation algorithms.
This simulation model is based on basic characteristics of the NFRT and specified by data collected from 60 healthy volunteers. We validated the model by comparing simulated data with data obtained independently in another volunteer population. The model was used to quantify the accuracy of previously published NFRT estimation algorithm for three NFRT variabilities representing sensory deprivation, distraction and general anaesthesia.
The dynamic staircase algorithm obtained most accurate NFRT estimates during all NFRT variabilities. The number of stimuli applied can be chosen higher to increase estimate precision or lower to reduce measurement time.
Our simulation model is a valid tool to measure the accuracy of NFRT estimation algorithms. It can be applied to analyse and develop algorithms. The dynamic staircase algorithm shows the highest precision in NFRT estimation and is recommended for NFRT studies.
Using optimized NFRT estimation algorithms increases precision in clinical and experimental NFRT studies and might therefore reduce the measurement effort necessary.
有多种算法用于评估伤害性屈肌反射阈值(NFRT),但它们的估计准确性尚不清楚。我们开发了一种基于计算机的 NFRT 模拟模型,以量化和比较现有的估计算法的准确性。
该模拟模型基于 NFRT 的基本特征,并根据从 60 名健康志愿者中收集的数据进行了具体规定。我们通过将模拟数据与另一个志愿者群体中独立获得的数据进行比较,验证了该模型。该模型用于量化先前发表的 NFRT 估计算法在代表感觉剥夺、分心和全身麻醉的三种 NFRT 可变性方面的准确性。
在所有 NFRT 可变性下,动态阶梯算法获得了最准确的 NFRT 估计值。可以选择应用更高的刺激数量来提高估计精度,或者选择更低的数量来减少测量时间。
我们的模拟模型是一种测量 NFRT 估计算法准确性的有效工具。它可以用于分析和开发算法。动态阶梯算法在 NFRT 估计中显示出最高的精度,因此推荐用于 NFRT 研究。
使用优化的 NFRT 估计算法可提高临床和实验 NFRT 研究的精度,从而减少所需的测量工作量。