Pain drawings as a diagnostic tool for the differentiation between two pain-associated rare diseases (Ehlers-Danlos-Syndrome, Guillain-Barré-Syndrome)

Abstract

Background The diagnosis of rare diseases poses a particular challenge to clinicians. This study analyzes whether patients’ pain drawings (PDs) help in the differentiation of two pain-associated rare diseases, Ehlers-Danlos Syndrome (EDS) and Guillain-Barré Syndrome (GBS). Method The study was designed as a prospective, observational, single-center study. The sample comprised 60 patients with EDS (3 male, 52 female, 5 without gender information; 39.2 ± 11.4 years) and 32 patients with GBS (10 male, 20 female, 2 without gender information; 50.5 ± 13.7 years). Patients marked areas afflicted by pain on a sketch of a human body with anterior, posterior, and lateral views. PDs were electronically scanned and processed. Each PD was classified based on the Ružička similarity to the EDS and the GBS averaged image (pain profile) in a leave-one-out cross validation approach. A receiver operating characteristic (ROC) curve was plotted. Results 60–80% of EDS patients marked the vertebral column with the neck and the tailbone and the knee joints as pain areas, 40–50% the shoulder-region, the elbows and the thumb saddle joint. 60–70% of GBS patients marked the dorsal and plantar side of the feet as pain areas, 40–50% the palmar side of the fingertips, the dorsal side of the left palm and the tailbone. 86% of the EDS patients and 96% of the GBS patients were correctly identified by computing the Ružička similarity. The ROC curve yielded an excellent area under the curve value of 0.95. Conclusion PDs are a useful and economic tool to differentiate between GBS and EDS. Further studies should investigate its usefulness in the diagnosis of other pain-associated rare diseases. This study was registered in the German Clinical Trials Register, No. DRKS00014777 (Deutsches Register klinischer Studien, DRKS), on 01.06.2018.

­A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D

“…We therefore did not distinguish between the two subtypes in our study population. In addition, we used 88 PDs from two different rare diseases that were recruited as part of a previous study: 59 EDS from Ehlers-Danlos syndrome (EDS) and 29 GBS from Guillain Barre syndrome (GBS) [16].…”

“…This study was registered at the German register for clinical studies DRKS (DRKS-ID: DRKS00014776 (participants recruited for this study) and DRKS00014777 (previously recruited participants, [16]).…”

“…Pain2D is a software package previously developed by our group [16] for the automated processing and analysis of pain drawings based on a template, which can be printed and filled out on paper. As part of the package, an application for tablets (Pain2D-Tablet, paperless) is available, which was however not used for this study.…”

“…Pain2D offers a binary and a k-disease classifier. The function of the binary classifier has been previously described [16]. In short, it classifies PDs by calculating the Ružička similarity of a given PD to two pain profiles, in a leave-one-out cross-validation (LOOCV) approach.…”

“…We have previously shown that a binary classifier from Pain2D-Tool of the PD software Pain2D is able to distinguish between two rare diseases, Ehlers-Danlos syndrome (EDS) and Guillain-Barré syndrome (GBS) [16]. Here, we test the performance of the binary classifier, as well as a new k-disease classifier implemented into Pain2D, on four different diseases (EDS, GBS, facioscapulohumeral muscular dystrophy (FSHD) and proximal myotonic myopathy (PROMM)) and a control group of patients with non-specific chronic pain.…”