Clinicaltrials.gov identifier: NCT01467102.

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BackgroundModern theories define chronic pain as a multidimensional experience – the result of complex interplay between physiological and psychological factors with significant impact on patients' physical, emotional and social functioning. The development of reliable assessment tools capable of capturing the multidimensional impact of chronic pain has challenged the medical community for decades. A number of validated tools are currently used in clinical practice however they all rely on self-reporting and are therefore inherently subjective. In this study we show that a comprehensive analysis of physical activity (PA) under real life conditions may capture behavioral aspects that may reflect physical and emotional functioning.MethodologyPA was monitored during five consecutive days in 60 chronic pain patients and 15 pain-free healthy subjects. To analyze the various aspects of pain-related activity behaviors we defined the concept of PA ‘barcoding’. The main idea was to combine different features of PA (type, intensity, duration) to define various PA states. The temporal sequence of different states was visualized as a ‘barcode’ which indicated that significant information about daily activity can be contained in the amount and variety of PA states, and in the temporal structure of sequence. This information was quantified using complementary measures such as structural complexity metrics (information and sample entropy, Lempel-Ziv complexity), time spent in PA states, and two composite scores, which integrate all measures. The reliability of these measures to characterize chronic pain conditions was assessed by comparing groups of subjects with clinically different pain intensity.ConclusionThe defined measures of PA showed good discriminative features. The results suggest that significant information about pain-related functional limitations is captured by the structural complexity of PA barcodes, which decreases when the intensity of pain increases. We conclude that a comprehensive analysis of daily-life PA can provide an objective appraisal of the intensity of pain.

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Objective For many medical professionals dealing with patients with persistent pain following spine surgery, the term failed back surgery syndrome (FBSS) as a diagnostic label is inadequate, misleading and potentially troublesome. It misrepresents causation. Alternative terms have been suggested but none has replaced FBSS. The International Association for the Study of Pain (IASP) published a revised classification of chronic pain, as part of the new International Classification of Diseases (ICD-11), which has been accepted by the World Health Organization (WHO). This includes the term Chronic pain after spinal surgery (CPSS), which is suggested as a replacement for FBSS. Methods This article provides arguments and rationale for a replacement definition. In order to propose a broadly applicable yet more precise and clinically informative term, an international group of experts was established. Results 14 candidate replacement terms were considered and ranked. The application of agreed criteria reduced this to a shortlist of four. A preferred option – Persistent spinal pain syndrome – was selected by a structured workshop and Delphi process. We provide rationale for using Persistent spinal pain syndrome and a schema for its incorporation into ICD-11. We propose the adoption of this term would strengthen the new ICD-11 classification. Conclusions This project is important to those in the fields of pain management, spine surgery and neuromodulation, as well as patients labelled with FBSS. Through a shift in perspective it could facilitate the application of the new ICD-11 classification and allow clearer discussion amongst medical professionals, industry, funding organisations, academia, and the legal profession.

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Abstract-Goal: Difficult tracheal intubation is a major cause of anesthesia related injuries with potential life threatening complications. Detection and anticipation of difficult airway in the preoperative period is thus crucial for the patients' safety. We propose an automatic face analysis approach to detect morphological traits related to difficult intubation and improve its prediction. Methods: For this purpose, we have collected a database of 970 patients including photos, videos and ground truth data. Specific statistical face models have been learned using the faces in our database providing an automated parametrization of the facial morphology. The most discriminative morphological features are selected through the importance ranking provided by the random forest algorithm. The random forest approach has also been used to train a classifier on these selected features. We compare a threshold tuning method based on class prior with two methods which learn an optimal threshold on a training set for tackling the inherent imbalanced nature of the database. Results: Our fully-automated method achieves an AUC of 81.0% in a simplified experimental setup where only easy and difficult patients are considered. A further validation on the entire database has proven that our method is applicable for real-world difficult intubation prediction, with AUC = 77.9%. Conclusion:The system performance is in line with the state-ofthe-art medical diagnosis, based on ratings provided by trained anesthesiologists, whose assessment is guided by an extensive set of criteria. Significance: We present the first completely automatic and non-invasive difficult intubation detection system that is suitable for use in clinical settings.