Assessing thermal sensitivity using transient heat and cold stimuli combined with a Bayesian adaptive method in a clinical setting: A proof of concept study.

ABSTRACT

BACKGROUND: Quantitative sensory testing of thermal detection abilities is used as a clinical tool to assess the function of pain pathways. The most common procedure to assess thermal sensitivity, the 'method of limits', provides a quick but rough estimate of detection thresholds. Here, we investigate the potential of evaluating not only the threshold but also the slope of the psychometric functions for cold and warm detection. METHOD: A convenience sample of 15 patients with diabetes mellitus (DM) and 15 age-matched healthy controls (HC) was tested. Thirty brief (100 ms) stimuli of each modality were applied to the volar wrist and foot dorsum. Cold and warm stimuli were delivered with a Peltier thermode and a temperature-controlled CO2 laser, respectively. Stimulus intensities were dynamically selected using an adaptive Bayesian algorithm (psi method) maximizing information gain for threshold and slope estimation. ROC analyses were used to assess the ability of slopes, thresholds and the combination of both to discriminate between groups. RESULTS: Assessment of the slope and threshold of the psychometric function for thermal detection took about 10 min. The ability to detect warmth was not reduced in DM patients as compared to HC. Cold detection performance assessed using slope or threshold parameters separated DM from HC with good discriminative power. Discrimination was further increased when both parameters were used together (93% sensitivity and 87% specificity), indicating that they provide complementary information on patient status. CONCLUSION: The psi method may be an interesting alternative to the classical method of limits for thermal QST. SIGNIFICANCE: Current QST protocols provide an incomplete and potentially biased estimate of sensory detection performance. We propose a method that estimates the slope and the threshold of the psychometric function, defining heat and cold sensory detection performance, in only a few minutes. Furthermore, we provide preliminary evidence that combining slope and threshold parameters of cold detection performance leads to a better discriminative ability than relying solely on the threshold.