Subjective risk factors of severe pain at discharge from the emergency department.

OBJECTIVE: To examine the risk factors for severe pain upon discharge from the emergency department, assuming appropriate pharmacological treatment of pain, in order to improve pain relief in emergency departments and reduce the risk of potential chronic pain. METHODS: An analytic study was conducted utilizing data from a multicenter randomized controlled trial to evaluate patients' experiences upon admission and discharge from the emergency department (ED). Severe pain was defined by a score of six on a numerical rating scale of zero to ten. Stress and negative emotions (including anger, fear, sadness, and regret) were evaluated using numerical rating scales, respectively ranging from 0 to 10 and 1 to 5. The risk factors of severe pain at discharge (SPD) from ED were calculated using logistic regression considering patient characteristics evaluated at their admission to the ED. RESULTS: From the 1240 patients analyzed, 22.2% had SPD from the ED. Each increase of one point in the intensity of acute pain and anger was significantly associated with a higher risk of SPD from ED. In addition, woman, negative self-perceived health, and age under 65 years, are other significant factors associated with SPD from the ED. DISCUSSION: In addition to acute pain on admission, this study highlights new factors to consider when managing pain in emergency care, such as anger, and self-perceived health. Addressing these aspects can help reduce the likelihood of developing SPD from the ED, which in turn could potentially lead to the onset of chronic pain in future. CLINICAL TRIAL REGISTRY: SOFTER IV Project clinical identification number: NCT04916678.

Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study.

BACKGROUND: Public health surveillance relies on the collection of data, often in near-real time. Recent advances in natural language processing make it possible to envisage an automated system for extracting information from electronic health records. OBJECTIVE: To study the feasibility of setting up a national trauma observatory in France, we compared the performance of several automatic language processing methods in a multiclass classification task of unstructured clinical notes. METHODS: A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among these clinical notes, 32.5% (22,481/69,110) were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the term frequency-inverse document frequency associated with the support vector machine method. RESULTS: The transformer models consistently performed better than the term frequency-inverse document frequency and a support vector machine. Among the transformers, the GPTanam model pretrained with a French corpus with an additional autosupervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969. CONCLUSIONS: The transformers proved efficient at the multiclass classification of narrative and medical data. Further steps for improvement should focus on the expansion of abbreviations and multioutput multiclass classification.