Thermal threshold testing: call for a balance between the number of measurements and abnormalities in the diagnosis of sarcoidosis-associated small fiber neuropathy.

Introduction: Several recent studies of diagnosing small fiber neuropathy (SFN) have shown a lack of uniformity in thermal threshold testing (TTT) or quantitative sensory testing (QST) which makes it a challenge to compare the data. It is known that the chance of finding an abnormality increases with increasing number of measurements. Objectives: With this study, we first wanted to investigate whether TTT could benefit from a new approach focusing on the balance between the number of measurements, depending on the selection of parameters and measuring sites, and on number of abnormalities (NOAs). Second, we wanted to address the role of the method of levels (MLe) in possible desensitization during TTT measurements. Methods: One hundred seventeen participants were included (48 patients with sarcoidosis with probable SFN, 49 without SFN, and 20 healthy controls). Thermal threshold testing measurements and Small Fiber Neuropathy Screening List (SFNSL) questionnaire were used to assess SFN. Results: A combination of measuring all thermal threshold parameters at both feet except for MLe showed the best diagnostic performance. Increasing TTT NOAs correlates with the severity of SFN. Adding the SFNSL questionnaire further improves diagnostic performance. Discussion: Looking at TTT NOAs in all TTT parameters except for MLe at both feet should be considered as a new approach to improve the consistency and balance between the selection of TTT parameters, measuring sites, and definition of "abnormal QST." Moreover, the SFNSL questionnaire is a valuable tool to quantify SFN symptoms and could improve SFN diagnosis.

Fully Automatic, Semiautomatic, and Manual Corneal Nerve Fiber Analysis in Patients With Sarcoidosis.

Purpose: No guidelines are available on the preferred method for analyzing corneal confocal microscopy (CCM) data. Manual, semiautomatic, and automatic analyzes are all currently in use. The purpose of the present study was threefold. First, we aimed to investigate the different methods for CCM analysis in patients with and without small fiber neuropathy (SFN). Second, to determine the correlation of different methods for measuring corneal nerve fiber length (CNFL) and nerve fiber area (NFA). Finally, we investigated the added value of automatic NFA analysis. Methods: We included 20 healthy controls and 80 patients with sarcoidosis, 31 with established SFN and 49 without SFN. The CNFL was measured using CCMetrics, ACCMetrics, and NeuronJ. NFA was measured with NFA FIJI and ACCMetrics NFA. Results: CNFL and NFA could not distinguish sarcoidosis with and without SFN or healthy controls. CCMetrics, NeuronJ, and ACCMetrics CNFL highly correlated. Also, NFA FIJI and ACCMetrics NFA highly correlated. Reproducing a nonlinear formula between CNFL and NFA confirmed the quadratic relation between NFA FIJI and ACCMetrics CNFL. CCMetrics and NeuronJ instead showed a square root relationship and seem to be less comparable owing to differences between automatic and manual techniques. Conclusions: ACCMetrics can be used for fully automatic analysis of CCM images to optimize efficiency. However, CNFL and NFA do not seem to have a discriminatory value for SFN in sarcoidosis. Further research is needed to determine the added value and normative values of NFA in CCM analysis. Translational Relevance: Our study improves the knowledge about CCM software and pathophysiology of SFN.

Current View of Diagnosing Small Fiber Neuropathy.

Small fiber neuropathy (SFN) is a disorder of the small myelinated Aδ-fibers and unmyelinated C-fibers [5, 6]. SFN might affect small sensory fibers, autonomic fibers or both, resulting in sensory changes, autonomic dysfunction or combined symptoms [7]. As a consequence, the symptoms are potentially numerous and have a large impact on quality of life [8]. Since diagnostic methods for SFN are numerous and its pathophysiology complex, this extensive review focusses on categorizing all aspects of SFN as disease and its diagnosis. In this review, sensitivity in combination with specificity of different diagnostic methods are described using the areas under the curve. In the end, a diagnostic work-flow is suggested based on different phenotypes of SFN.