Diagnosing complex regional pain syndrome using an electronic nose, a pilot study

Objective: Complex regional pain syndrome (CRPS)is a complication after surgery or trauma and is characterized by a continuing regional pain in a distal extremity. The pain is disproportionate in severity and duration in relation to the preceding trauma. Currently, the diagnosis is based on the patients’signs and symptoms. There is no objective clinically applicable test available to confirm the diagnosis of CRPS, however this could contribute to a more reliable and valid diagnosis. Since the treatment of CRPS differs from that of other types of pain this could thereby lead to earlier and (more) appropriate treatment and possibly to lower medical costs. The Aeonose™ is a diagnostic test device which detects volatile organic profiles in exhaled air. Exhaled breath analysis using an electronic nose has been successfully applied to differentiate between sick and healthy persons for various indications. This study was a feasibility study in which we investigated whether the Aeonose™ is able to measure a difference in the volatome of CRPS patients compared to the volatome of healthy controls. Design: Prospective observational study. Setting: University Center for Pain Medicine. Subjects: Adult patients diagnosed with CRPS according to the latest IASP criteria (n = 36) and matched healthy controls (n = 36). Methods: Breath profiles were sampled by breathing in and out through the Aeonose™. Data were compressed using a Tucker3-like solution and subsequently used for training an artificial neural network together with the classification ‘CRPS: Yes’ or‘CRPS: No’. Cross-validation was applied using the leave-10%-out method. Results: Data of the 72 participants were analyzed, resulting in a sensitivity of 83% (95% CI 67%–93%), specificity of 78% (95% CI 60%–89%), and an overall accuracy of 81%. Conclusions: This study suggests that the Aeonose™ can possibly distinguish patients with CRPS from healthy controls based on analysis of their volatome (MEC-2014-149).

• View at Publisher

Free Full Text ( Final Version , 631kb )