Von Frey testing revisited: Provision of an online algorithm for improved accuracy of 50 thresholds

摘要

Abstract Background In the pain field, it is essential to quantify nociceptive responses. The response to the application of von Frey filaments to the skin measures tactile sensitivity and is a surrogate marker of allodynia in states of peripheral and/or central sensitization. The method is widely used across species within the pain field. However, uncertainties appear to exist regarding the appropriate method for analysing obtained data. Therefore, there is a need for refinement of the calculations for transformation of raw data to quantifiable data. Methods Here, we briefly review the fundamentals behind von Frey testing using the standard up‐down method and the associated statistics and show how different parameters of the statistical equation influence the calculated 50% threshold results. We discuss how to obtain the most accurate estimations in a given experimental setting. Results To enhance accuracy and reproducibility across laboratories, we present an easy to use algorithm that calculates 50% thresholds based on the exact filaments and their interval using math beyond the traditional methods. This tool is available to the everyday user of von Frey filaments and allows the insertion of all imaginable ranges of filaments and is thus applicable to data derived in any species. Conclusion We advocate for the use of this algorithm to minimize inaccuracies and to improve internal and external reproducibility. Significance The von Frey testing procedure is standard for assessing peripheral and central sensitization but is associated with inaccuracies and lack of transparency in the associated math. Here, we describe these problems and present a novel statistical algorithm that calculates the exact thresholds using math beyond the traditional methods. The online platform is transparent, free of charge and easy to use also for the everyday user of von Frey filaments. Application of this resource will ultimately reduce errors due to methodological misinterpretations and increase reproducibility across laboratories.