[Ehlers-Danlos syndromes]

Child abuse misdiagnosed as Ehlers-Danlos syndrome: A case report

Child abuse is a sensitive subject, and its diagnosis is sometimes difficult and requires awareness among physicians of the conditions that can mimic its symptoms.We report the case of a child aged two years and eight months who, according to his mother, had suffered multiple traumas of accidental and spontaneous occurrence for which he was admitted several times to the Children's University Hospital Ibn Sina in Rabat.The diagnosis of Ehlers-Danlos syndrome was made following the first skin biopsy. During the last admission, the child presented with a bleeding wound that raised the nursing team's doubts; hence, they requested a medico-legal opinion. Our examination confirmed a diagnosis of physical abuse.

Uncommon association between vascular Ehlers–Danlos syndrome and ocular complications

Ehlers–Danlos syndromes (EDS) represent a group of rare inherited disorders that affect connective tissues. There are 13 types of disease, most of them affecting joints or skin; symptoms usually include loose joints, joint pain, stretchy velvety skin, abnormal scar formation. However, the most serious type of disease is vascular EDS (vEDS), or EDS type 4 because patients may suffer vessels dissections or internal organs lesions, followed by bleeding, which endangers patient’s life, but also thromboembolic events. We present two clinical cases of vEDS managed in our clinic in 1 year distance. In both cases, patients were active young persons (in their thirties, and respectively, twenties), both with multiple non-traumatic vascular dissections, and severe ocular complications: arterio-venous fistula with massive exophthalmia, and central retinal artery occlusion, respectively. Both cases were challenging since the life of the patients were threatened by their condition. However, in both cases, prompt treatment and finding the right trigger of the ocular pathology and vascular injuries helped doctors to provide proper and prompt medical care, in order to prevent future similar events to happen and to preserve a good quality of life for these patients.

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

BACKGROUND AND OBJECTIVE

The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients.

METHODS

Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach.

RESULTS

Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%.

CONCLUSIONS

Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.

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

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.