Harnessing Historical Data to Derive Reference Limits - A Comparison of E-norms to Traditionally Derived Reference Limits

Overview

Journal Clin Neurophysiol Pract

Publisher Elsevier

Specialty Neurology

Date 2024 May 6

PMID 38707483

Authors

O Dunker

T S Szczepanski

H O P Do

P Omland

M U Lie

T Sand

J F Jabre

K B Nilsen

Affiliations

Soon will be listed here.

Abstract

Objective: Nerve conduction studies (NCS) require valid reference limits for meaningful interpretation. We aimed to further develop the extrapolated norms (e-norms) method for obtaining NCS reference limits from historical laboratory datasets for children and adults, and to validate it against traditionally derived reference limits.

Methods: We compared reference limits obtained by applying a further developed e-norms with reference limits from healthy controls for the age strata's 9-18, 20-44 and 45-60 years old. The control data consisted of 65 healthy children and 578 healthy adults, matched with 1294 and 5628 patients respectively. Five commonly investigated nerves were chosen: The tibial and peroneal motor nerves (amplitudes, conduction velocities, F-waves), and the sural, superficial peroneal and medial plantar sensory nerves (amplitudes, conduction velocities). The datasets were matched by hospital to ensure identical equipment and protocols. The e-norms method was adapted, and reference limit calculation using both ±2 SD (original method) and ±2.5 SD (to compensate for predicted underestimation of population SD by the e-norms method) was compared to control data using ±2 SD. Percentage agreement between e-norms and the traditional method was calculated.

Results: On average, the e-norms method (mean ±2 SD) produced slightly stricter reference limits compared to the traditional method. Increasing the e-norms range to mean ±2.5 SD improved the results in children while slightly overcorrecting in the adult group. The average agreement between the two methods was 95 % (±2 SD) and 96 % (±2.5 SD).

Conclusions: The e-norms method yielded slightly stricter reference limits overall than ones obtained through traditional methods; However, much of the difference can be attributed to a few outlying plots where the raters found it difficult to apply e-norms correctly. The two methods disagreed on classification of 4-5% of cases. Our e-norms software is suited to analyze large amounts of raw NCS data; it should further reduce bias and facilitate more accurate ratings.

Significance: With small adaptations, the e-norms method adequately replicates traditionally derived reference limits, and is a viable method to produce reference limits from historical datasets.

Citing Articles

E-norms and AI in clinical neurophysiology.

Jabre J Clin Neurophysiol Pract. 2025; 9:299-304.

PMID: 39758505 PMC: 11696621. DOI: 10.1016/j.cnp.2024.12.001.

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