Deriving reference values for nerve conduction studies from existing data using mixture model clustering

Objective: to obtain locally valid reference values (RVs) from existing nerve conduction study (NCS) data. Methods: we used age, sex, height and limb temperature-based mixture model clustering (MMC) to identify normal and abnormal measurements on NCS data from two university hospitals. We compared MMC-derived RVs to published data; examined the effect of using different variables; validated MMC-derived RVs using independent data from 26 healthy control subjects and investigated their clinical applicability for the diagnosis of polyneuropathy. Results: MMC-derived RVs were similar to published RVs. Clustering can be achieved using only sex and age as variables. MMC is likely to yield reliable results with fewer abnormal than normal measurements and when the total number of measurements is at least 300. Measurements from healthy controls fell within the 95% MMC-derived prediction interval in 97.4% of cases. Conclusions: MMC can be used to obtain RVs from existing data, providing a locally valid, accurate reflection of the (ab)normality of an NCS result. Significance: MMC can be used to generate locally valid RVs for any test for which sufficient data are available.(1) (C) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.

Automated summary

This study demonstrated the reliability and accuracy of obtaining locally valid reference values from existing nerve conduction study data using a mixture model clustering method. Clustering based on sex and age variables can achieve reliable results, and the method has clinical applicability for the diagnosis of polyneuropathy.