There are many problems in the anesthetic management of patients with scar contracture. In this case, a 41-year-old male with severe scar contracture on his face, neck, anterior chest, and both shoulders underwent surgery for resurfacing with flaps. We tried to awake fiberoptic orotracheal intubation with GlideScope® Video laryngoscope guide after surgical release of contracture under local anesthesia. We report a successful management of a patient with severe burn contracture achieved by combined effort of surgeons and anesthesiologists.

Background Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients. Objective This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients. Methods PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram–CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices. Results PPGs from 100 patients were used to develop the spectrogram–CNN index. When there was pain, the mean (95% CI) spectrogram–CNN index value increased significantly—baseline: 28.5 (24.2-30.7) versus recovery area: 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram–CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%. Conclusions Although there were limitations to the study design, we confirmed that the spectrogram–CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram–CNN index’s feasibility and prevent overfitting to various populations, including patients under general anesthesia. Trial Registration Clinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638

This study evaluated the structure of complex regional pain syndrome (CRPS) population and suggested a weighted scoring system to balance on objective signs. One hundred sixty-eight consecutive patients were evaluated using the Budapest Research Criteria (BRC). By using multidimensional scaling and logistic regression analysis, we analyzed the degree of importance and relationships between objective findings. In addition, a receiver operating characteristic curve was constructed using a weighted score derived from the risk ratio as a diagnostic test. There were correlations between skin color change and edema, and between decreased range of motion and motor dysfunction when multidimensional scaling was applied. The trophic change was excluded by a logistic regression (95% CI; 0.80-11.850). The cutoff point based on weighted score derived from the risk ratios for determining CRPS was 7.88. At this point, the sensitivity, specificity, positive predictive value and negative predictive value were 75.0%, 95.3%, 96.3%, and 70.1%, respectively. We propose a weighted scoring system for the BRC using risk ratios of objective signs. Although a thorough systematic review would be required in the future, this study can contribute to reduction of the possible distortion of the feature of CRPS populations by the BRC.

BACKGROUND Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anaesthesia, the performance of these indices is not high in awake patients. Therefore, there is a need for the development of a new analgesic index with improved performance to quantify postoperative pain in awake patients. OBJECTIVE The aim of this study was to develop a new analgesic index using spectrogram of photoplethysmogram and convolutional neural network to objectively assess pain in awake patients. METHODS Photoplethysmograms (PPGs) were obtained for 6 min both in the absence (preoperatively) and presence (postoperatively) of pain in a group of surgical patients. Of these, 5 min worth of PPG data, barring the first minute, were used for analysis. Based on the spectrogram from the photoplethysmography and convolutional neural network, we developed a spectrogram-CNN index (SCI) for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic (ROC) curve was measured to evaluate the performance of the two indices. RESULTS PPGs from 100 patients were used to develop the SCI. When there was pain, the mean [95% confidence interval, CI] SCI value increased significantly (baseline: 28.5 [24.2 - 30.7] vs. recovery area: 65.7 [60.5 - 68.3]; P<0.01). The AUC of ROC curve and balanced accuracy were 0.76 and 71.4%, respectively. The cut-off value for detecting pain was 48 on the SCI, with a sensitivity of 68.3% and specificity of 73.8%. CONCLUSIONS Although there were limitations to the study design, we confirmed that the SCI can efficiently detect postoperative pain in conscious patients. Further studies are needed to assess feasibility and prevent overfitting in various populations, including patients under general anaesthesia. CLINICALTRIAL KCT0002080

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