Bellal Mathieu, Lelandais Julien, Chabin Thomas, Heudron Aurélie, Gourmelon Thomas, Bauduin Pierrick, Cuchet Pierre, Daubin Cédric, De Carvalho Ribeiro Célia, Delcampe Augustin, Goursaud Suzanne, Joret Aurélie, Mombrun Martin, Valette Xavier, Cerasuolo Damiano, Morello Rémy, Mordel Patrick, Chaillot Fabien, Dutheil Jean Jacques, Vivien Denis, Du Cheyron Damien
Department of Medical Intensive Care, Caen University Hospital, Caen, France.
Normandie Univ., UNICAEN, INSERM UMRS U1237 PhIND, Caen, France.
Front Med (Lausanne). 2024 Jun 27;11:1309720. doi: 10.3389/fmed.2024.1309720. eCollection 2024.
Pain management is an essential and complex issue for non-communicative patients undergoing sedation in the intensive care unit (ICU). The Behavioral Pain Scale (BPS), although not perfect for assessing behavioral pain, is the gold standard based partly on clinical facial expression. , an automatic pain assessment tool based on facial expressions in critically ill patients, is a much-needed innovative medical device.
In this prospective pilot study, we recorded the facial expressions of critically ill patients in the medical ICU of Caen University Hospital using the iPhone and Smart Motion Tracking System (SMTS) software with the Facial Action Coding System (FACS) to measure human facial expressions metrically during sedation weaning. Analyses were recorded continuously, and BPS scores were collected hourly over two 8 h periods per day for 3 consecutive days. For this first stage, calibration of the innovative medical device algorithm was obtained by comparison with the reference pain scale (BPS).
Thirty participants were enrolled between March and July 2022. To assess the acute severity of illness, the Sequential Organ Failure Assessment (SOFA) and the Simplified Acute Physiology Score (SAPS II) were recorded on ICU admission and were 9 and 47, respectively. All participants had deep sedation, assessed by a Richmond Agitation and Sedation scale (RASS) score of less than or equal to -4 at the time of inclusion. One thousand and six BPS recordings were obtained, and 130 recordings were retained for final calibration: 108 BPS recordings corresponding to the absence of pain and 22 BPS recordings corresponding to the presence of pain. Due to the small size of the dataset, a leave-one-subject-out cross-validation (LOSO-CV) strategy was performed, and the training results obtained the receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.792. This model has a sensitivity of 81.8% and a specificity of 72.2%.
This pilot study calibrated the medical device and showed the feasibility of continuous facial expression analysis for pain monitoring in ICU patients. The next step will be to correlate this device with the BPS scale.
对于在重症监护病房（ICU）接受镇静的无沟通能力患者而言，疼痛管理是一个至关重要且复杂的问题。行为疼痛量表（BPS）虽并非评估行为疼痛的完美工具，但部分基于临床面部表情，它是金标准。一种基于危重症患者面部表情的自动疼痛评估工具，是急需的创新性医疗设备。
在这项前瞻性试点研究中，我们使用iPhone和智能运动跟踪系统（SMTS）软件以及面部动作编码系统（FACS），在卡昂大学医院内科ICU记录危重症患者在撤机镇静期间的面部表情，以便定量测量人类面部表情。分析持续记录，并且在连续3天里，每天分两个8小时时段每小时收集BPS评分。在第一阶段，通过与参考疼痛量表（BPS）比较，获得了创新性医疗设备算法的校准。
2022年3月至7月招募了30名参与者。为评估疾病的急性严重程度，在入住ICU时记录了序贯器官衰竭评估（SOFA）和简化急性生理学评分（SAPS II），分别为9分和47分。所有参与者均处于深度镇静状态，纳入时根据里士满躁动镇静量表（RASS）评分小于或等于 -4进行评估。共获得1006次BPS记录，130次记录被保留用于最终校准：108次BPS记录对应无痛，22次BPS记录对应有痛。由于数据集规模较小，实施了留一法交叉验证（LOSO - CV）策略，训练结果获得了曲线下面积（AUC）为0.792的受试者工作特征（ROC）曲线。该模型的灵敏度为81.8%，特异度为72.2%。
这项试点研究校准了该医疗设备，并展示了对ICU患者进行疼痛监测的连续面部表情分析的可行性。下一步将是使该设备与BPS量表相关联。
