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  1. AU="Devie, Antoine"
  2. AU="Zhou, Zhifeng"
  3. AU="Rector, Annabel"
  4. AU="Silverman, Bernard W."
  5. AU="Kuang, Jialiang"
  6. AU="Noordermeer, Jasprina N"
  7. AU="Sumner, Madeleine W"
  8. AU=Huang Kai
  9. AU="Flavia Bustreo"
  10. AU="Collins, Jamie"
  11. AU="Quinn, Patrick J"
  12. AU="Debnath Pal"
  13. AU="Kamali Kakhki, Reza"
  14. AU=Mortele Koenraad J
  15. AU="Skaarup, Søren H"
  16. AU="Lin, Li-Er"
  17. AU=Goulard Marie
  18. AU=Rosner Mitchell H
  19. AU="Murphy, Bríd"
  20. AU="Tsuneyoshi, Isao"
  21. AU="Tram, Le Thi Hong"
  22. AU="Veli-Pekka Jaakola"
  23. AU="Erduğan, Hüseyin"

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  1. Artikel ; Online: Stomach size in anorexia nervosa: A new challenge?

    Joyeux, Marie-Alix / Pierre, Antoine / Barrois, Marion / Hoeffel, Christine / Devie, Antoine / Brugel, Mathias / Bertin, Eric

    European eating disorders review : the journal of the Eating Disorders Association

    2024  

    Abstract: Background & aims: Changes in stomach size may impact eating behaviour. A recent study showed gastric dilatation in restrictive eating disorders using computed tomography scans. This study aimed to describe stomach size in the standing position in women ...

    Abstract Background & aims: Changes in stomach size may impact eating behaviour. A recent study showed gastric dilatation in restrictive eating disorders using computed tomography scans. This study aimed to describe stomach size in the standing position in women with anorexia nervosa (AN).
    Methods: Women treated for AN at our institution were retrospectively included if they had undergone upper gastrointestinal radiography (UGR) after the diagnosis of AN. Two control groups (CG1 and CG2) were included, both comprising female patients: CG1 patients were not obese and underwent UGR for digestive symptoms of other aetiologies, and CG2 comprised obese individuals who had UGR before bariatric surgery. A UGR-based Stomach Size Index (SSI), calculated as the ratio of the length of the stomach to the distance between the upper end of the stomach and the top of the iliac crests, was measured in all three groups. Gastromegaly was defined as SSI >1.00.
    Results: 45 patients suffering from AN (28 with restrictive and 17 with binge/purge subtype), 10 CG1 and 20 CG2 subjects were included in this study. Stomach Size Index was significantly higher in AN (1.27 ± 0.24) than in CG1 (0.80 ± 0.11) and CG2 (0.68 ± 0.09); p < 0.001, but was not significantly different between patients with the restrictive and binge/purge subtypes. Gastromegaly was present in 82.2% of patients with AN and not present in the control groups. In patients with AN, gastromegaly was present in 12/15 patients without digestive symptoms (80.0%) and in 25/30 patients with digestive complaints (83.3%) at time of UGR (p = 0.99). In the AN group, no significant relationship was found between SSI and body mass index.
    Conclusion: Gastromegaly is frequent in AN and could influence AN recovery. This anatomical modification could partially explain the alterations of gastric motility previously reported in AN.
    Sprache Englisch
    Erscheinungsdatum 2024-03-23
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 1159507-3
    ISSN 1099-0968 ; 1067-1633 ; 1072-4133
    ISSN (online) 1099-0968
    ISSN 1067-1633 ; 1072-4133
    DOI 10.1002/erv.3089
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: COVID-19: A qualitative chest CT model to identify severe form of the disease.

    Devie, Antoine / Kanagaratnam, Lukshe / Perotin, Jeanne-Marie / Jolly, Damien / Ravey, Jean-Noël / Djelouah, Manel / Hoeffel, Christine

    Diagnostic and interventional imaging

    2020  Band 102, Heft 2, Seite(n) 77–84

    Abstract: Purpose: The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a ... ...

    Abstract Purpose: The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form.
    Materials and methods: A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68±14 (SD) years (range: 24-96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method.
    Results: Central involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score≥15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score≥15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI: 0.638-0.806) vs. 0.739 (95% CI: 0.656-0.823), respectively (P=0.209).
    Conclusion: We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.
    Mesh-Begriff(e) Adult ; Aged ; Aged, 80 and over ; Bronchiectasis/diagnostic imaging ; COVID-19/diagnostic imaging ; Computer Simulation ; Female ; Humans ; Lung/diagnostic imaging ; Male ; Middle Aged ; Multivariate Analysis ; ROC Curve ; Risk Factors ; Severity of Illness Index ; Tomography, X-Ray Computed
    Sprache Englisch
    Erscheinungsdatum 2020-12-17
    Erscheinungsland France
    Dokumenttyp Journal Article
    ZDB-ID 2648283-6
    ISSN 2211-5684 ; 2211-5684
    ISSN (online) 2211-5684
    ISSN 2211-5684
    DOI 10.1016/j.diii.2020.12.002
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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