La peur de mourir et l'apparition des symptômes de stress post- traumatique après un syndrome coronarien aigu : une étude prospective observationnelle

Le but de cette étude est d'évaluer si l'expérience de la peur de mourir après le syndrome coronarien aigu prédit ultérieurement les symptômes de stress post-traumatique. Nous avons inclus 90 patients hospitalisés avec le syndrome coronarien aigu et évalué les caractéristiques de base. Un mois après la sortie, nous avons collecté l'échelle de stress post-traumatique. Un total de 24 patients (26.7%) ont développé des symptômes de stress post-traumatique un mois après l'événement du syndrome coronarien aigu. Les patients atteints de symptômes de stress post-traumatique présentaient une plus grande peur de mourir, un sentiment d'impuissance, le coping d'évitement et l'anxiété sévère. Dans notre étude prospective, la peur de mourir était associée à l'apparition de symptômes de stress post-traumatique chez les patients hospitalisés avec le syndrome coronarien aigu. Une simple question sur la peur de mourir pourrait être utile pour identifier les patients à risque plus élevé des symptômes de stress post-traumatique

The fear of dying and occurrence of posttraumatic stress symptoms after an acute coronary syndrome: A prospective observational study

The purpose of the study was to investigate whether experiencing fear of dying after acute coronary syndrome predicts later posttraumatic stress symptoms. We enrolled 90 patients hospitalized with main diagnosis of acute coronary syndrome and assessed baseline characteristics. One month after discharge, we collected the Posttraumatic Stress Scale. A total of 24 patients : 26.7%) developed posttraumatic stress symptoms 1 month after the acute coronary syndrome event. Patients with posttraumatic stress symptoms reported significantly greater fear of dying, helplessness, avoidance-focused coping, and severe anxiety. In our prospective study, fear of dying was associated with occurrence of posttraumatic stress symptoms in patients hospitalized with acute coronary syndrome

Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists

Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine learning allow for several types of pathologies encountered on radiological images to be automatically and reliably distinguished. The aim of the study was to assess the contribution of radiomics and machine learning in the differentiation between soft-tissue lipoma and liposarcoma on preoperative MRI and to assess the diagnostic accuracy of a machine-learning model compared to musculoskeletal radiologists. 86 radiomics features were retrospectively extracted from volume-of-interest on T1-weighted spin-echo 1.5 and 3.0 Tesla MRI of 38 soft-tissue tumors (24 lipomas and 14 liposarcomas, based on histopathological diagnosis). These radiomics features were then used to train a machine-learning classifier to distinguish lipoma and liposarcoma. The generalization performance of the machine-learning model was assessed using Monte-Carlo cross-validation and receiver operating characteristic curve analysis (ROC-AUC). Finally, the performance of the machine-learning model was compared to the accuracy of three specialized musculoskeletal radiologists using the McNemar test. Machine-learning classifier accurately distinguished lipoma and liposarcoma, with a ROC-AUC of 0.926. Notably, it performed better than the three specialized musculoskeletal radiologists reviewing the same patients, who achieved ROC-AUC of 0.685, 0.805, and 0.785. Despite being developed on few cases, the trained machine-learning classifier accurately distinguishes lipoma and liposarcoma on preoperative MRI, with better performance than specialized musculoskeletal radiologists