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  1. Article ; Online: Response to 'Integrating clinical judgment, advanced radiology, and molecular diagnosis: the modern ways of pneumonia management'.

    Claessens, Yann-Erick

    European journal of emergency medicine : official journal of the European Society for Emergency Medicine

    2023  Volume 30, Issue 4, Page(s) 301

    MeSH term(s) Humans ; Judgment ; Radiography ; Radiology ; Pneumonia/diagnostic imaging
    Language English
    Publishing date 2023-06-27
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 1233544-7
    ISSN 1473-5695 ; 0969-9546
    ISSN (online) 1473-5695
    ISSN 0969-9546
    DOI 10.1097/MEJ.0000000000001040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book: Maladies rares en médecine d'urgence

    Claessens, Yann-Érick

    (Références en médecine d'urgence)

    2013  

    Author's details sous la direction de Yann-Érick Claessens et Luc Mouthon
    Series title Références en médecine d'urgence
    MeSH term(s) Rare Diseases/diagnosis ; Emergencies
    Language French
    Size xi, 425 pages :, illustrations.
    Document type Book
    ISBN 9782817803494 ; 2817803493
    Database Catalogue of the US National Library of Medicine (NLM)

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  3. Book: Les biomarqueurs en médecine d'urgence

    Claessens, Yann-Érick

    des données biologiques au lit du malade

    (Référence en médecine d'urgence,)

    2012  

    Author's details sous la direction de Yann-Érick Claessens et Patrick Ray
    Series title Référence en médecine d'urgence,
    MeSH term(s) Biomarkers ; Emergency Medicine
    Language French
    Size x, 384 pages :, illustrations.
    Document type Book
    ISBN 9782817802961 ; 2817802969
    Database Catalogue of the US National Library of Medicine (NLM)

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  4. Article ; Online: Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies.

    Hofmeister, Jeremy / Garin, Nicolas / Montet, Xavier / Scheffler, Max / Platon, Alexandra / Poletti, Pierre-Alexandre / Stirnemann, Jérôme / Debray, Marie-Pierre / Claessens, Yann-Erick / Duval, Xavier / Prendki, Virginie

    European radiology experimental

    2024  Volume 8, Issue 1, Page(s) 20

    Abstract: Background: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we ... ...

    Abstract Background: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we evaluated the accuracy of DL in diagnosing pneumonia on CXR using a more robust reference diagnosis.
    Methods: We trained a DL convolutional neural network model to diagnose pneumonia and evaluated its accuracy in two prospective pneumonia cohorts including 430 patients, for whom the reference diagnosis was determined a posteriori by a multidisciplinary expert panel using multimodal data. The performance of the DL model was compared with that of senior radiologists and emergency physicians reviewing CXRs and that of radiologists reviewing computed tomography (CT) performed concomitantly.
    Results: Radiologists and DL showed a similar accuracy on CXR for both cohorts (p ≥ 0.269): cohort 1, radiologist 1 75.5% (95% confidence interval 69.1-80.9), radiologist 2 71.0% (64.4-76.8), DL 71.0% (64.4-76.8); cohort 2, radiologist 70.9% (64.7-76.4), DL 72.6% (66.5-78.0). The accuracy of radiologists and DL was significantly higher (p ≤ 0.022) than that of emergency physicians (cohort 1 64.0% [57.1-70.3], cohort 2 63.0% [55.6-69.0]). Accuracy was significantly higher for CT (cohort 1 79.0% [72.8-84.1], cohort 2 89.6% [84.9-92.9]) than for CXR readers including radiologists, clinicians, and DL (all p-values < 0.001).
    Conclusions: When compared with a robust reference diagnosis, the performance of AI models to identify pneumonia on CXRs was inferior than previously reported but similar to that of radiologists and better than that of emergency physicians.
    Relevance statement: The clinical relevance of AI models for pneumonia diagnosis may have been overestimated. AI models should be benchmarked against robust reference multimodal diagnosis to avoid overestimating its performance.
    Trial registration: NCT02467192 , and NCT01574066 .
    Key point: • We evaluated an openly-access convolutional neural network (CNN) model to diagnose pneumonia on CXRs. • CNN was validated against a strong multimodal reference diagnosis. • In our study, the CNN performance (area under the receiver operating characteristics curve 0.74) was lower than that previously reported when validated against radiologists' diagnosis (0.99 in a recent meta-analysis). • The CNN performance was significantly higher than emergency physicians' (p ≤ 0.022) and comparable to that of board-certified radiologists (p ≥ 0.269).
    MeSH term(s) Humans ; Prospective Studies ; Artificial Intelligence ; Deep Learning ; X-Rays ; Pneumonia/diagnostic imaging
    Language English
    Publishing date 2024-02-02
    Publishing country England
    Document type Journal Article
    ISSN 2509-9280
    ISSN (online) 2509-9280
    DOI 10.1186/s41747-023-00416-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Early chest CT-scan in emergency patients affected by community-acquired pneumonia is associated with improved diagnosis consistency.

    Claessens, Yann-Erick / Berthier, Frédéric / Baqué-Juston, Marie / Perrin, Christophe / Faraggi, Marc / Keita-Perse, Olivia / Duval, Xavier

    European journal of emergency medicine : official journal of the European Society for Emergency Medicine

    2022  Volume 29, Issue 6, Page(s) 417–420

    Abstract: Chest CT-scan (CT) exceeds chest X-ray (CXR) to diagnose community-acquired pneumonia (CAP) but actual use and results remain unclear. We examine whether CT performed at ED visit improved ED diagnosis of CAP as compared to a final diagnosis of CAP at ... ...

    Abstract Chest CT-scan (CT) exceeds chest X-ray (CXR) to diagnose community-acquired pneumonia (CAP) but actual use and results remain unclear. We examine whether CT performed at ED visit improved ED diagnosis of CAP as compared to a final diagnosis of CAP at hospital discharge (gold standard diagnosis for the study), and how it impacts relevant clinical outcomes. This retrospective monocenter observational study was based on the analysis of the hospital database. Patients with a diagnosis of CAP in the ED (ICD-10 codes: J110, J111, from J12- to J18-, J440, J690, U0710, and U0711) were included. We compared ED patients who were diagnosed with CAP using CXR and CT. We measured diagnostic consistency, duration of ED visit, percentage of CXR and CT during hospital stay, hospital length-of-stay, ICU admission, and in-hospital mortality. Multivariate analysis was adjusted for CRB65 score by multiple logistic regression analysis for binary outcomes and by multivariate analysis of variance for continuous outcomes. We included 994 ED patients with an initial diagnosis of CAP (751 receiving CXR, 243 receiving CT). CT prescription in the ED increased over time ( P < 0.001). In patients admitted after ED, CT improved diagnosis consistency for CAP [88.2% vs. 80.9%; difference 7.3% (95% confidence interval 1.2-13.3%)] with a trend for lower hospital length-of-stay [10.2 vs. 12.2 days; difference -2.0 (95% confidence interval -3.9 to -0.1)], but not ICU admission ( P = 0.09) and in-hospital mortality ( P = 0.056). Diagnosis of patients admitted with CAP improved when CT was obtained at ED visit. These results should be reproduced at a larger scale to test whether early CT conserves healthcare resources.
    MeSH term(s) Humans ; Retrospective Studies ; Emergency Service, Hospital ; Community-Acquired Infections/diagnostic imaging ; Pneumonia/diagnostic imaging ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2022-06-28
    Publishing country England
    Document type Observational Study ; Journal Article
    ZDB-ID 1233544-7
    ISSN 1473-5695 ; 0969-9546
    ISSN (online) 1473-5695
    ISSN 0969-9546
    DOI 10.1097/MEJ.0000000000000955
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Effect of diagnosis level of certainty on adherence to antibiotics' guidelines in ED patients with pneumonia: a post-hoc analysis of an interventional trial.

    Tubiana, Sarah / Epelboin, Loïc / Casalino, Enrique / Naccache, Jean-Marc / Feydy, Antoine / Khalil, Antoine / Hausfater, Pierre / Duval, Xavier / Claessens, Yann-Erick

    European journal of emergency medicine : official journal of the European Society for Emergency Medicine

    2022  Volume 30, Issue 2, Page(s) 102–109

    Abstract: Background and importance: Clinical diagnosis of community-acquired pneumonia (CAP) is difficult to establish with certainty. Adherence to antibiotic guidelines independently affects the prognosis of CAP patients.: Objective: We aimed to determine ... ...

    Abstract Background and importance: Clinical diagnosis of community-acquired pneumonia (CAP) is difficult to establish with certainty. Adherence to antibiotic guidelines independently affects the prognosis of CAP patients.
    Objective: We aimed to determine whether guidelines' adherence was related to CAP diagnosis level of certainty and could be reinforced accordingly to diagnosis improvement.
    Design: Secondary analysis of a prospective, multicenter study, which evaluated the impact of early thoracic CT scan on diagnosis and therapeutic plan in patients with clinically suspected CAP visiting emergency departments.
    Setting and participants: In total 319 patients with clinically suspected CAP were enrolled in four emergency departments, Paris, France, between Nov 2011 and Jan 2013.
    Outcome measures and analysis: We evaluated guidelines' adherence before and after CT scan and its relationship with CAP diagnosis level of certainty. Antibiotics were categorized as adherent according to 2010 French guidelines. CAP diagnosis level of certainty was prospectively classified by the emergency physicians based on a Likert scale as excluded, possible, probable or definite before and immediately after the CT scan. These classifications and therapeutic plans were also completed by an independent adjudication committee. Determinants of adherence were assessed using Poisson regression with robust variance.
    Main results: Adherence to guidelines increased from 34.2% before CT scan to 51.3% after CT scan [difference 17.1% (95% CI, 9.5-24.7)], meanwhile CAP diagnosis with high level of certainty (definite and excluded CAP) increased from 46.1 to 79.6% [difference 33.5% (95% CI, 26.5-40.5)]. Diagnosis level of certainty before CT scan was the strongest determinant of adherence in multivariate analysis (RR, 2.63; 95% CI, 1.89-3.67).
    Conclusion: Antibiotic guidelines' adherence was poor and positively related to CAP diagnosis level of certainty. The results suggest that improvements in CAP diagnosis may increase adherence to antibiotic guidelines. Clinical trial registered with www.clinicaltrials.gov (NCT01574066).
    MeSH term(s) Humans ; Anti-Bacterial Agents/therapeutic use ; Prospective Studies ; Pneumonia/diagnosis ; Community-Acquired Infections/diagnosis ; Tomography, X-Ray Computed/methods
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2022-06-27
    Publishing country England
    Document type Multicenter Study ; Journal Article
    ZDB-ID 1233544-7
    ISSN 1473-5695 ; 0969-9546
    ISSN (online) 1473-5695
    ISSN 0969-9546
    DOI 10.1097/MEJ.0000000000000954
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Les biomarqueurs en médecine d’urgence

    Claessens, Yann-Érick / Ray, Patrick

    Des données biologiques au lit du malade

    (Références en médecine d’urgence. Collection de la SFMU)

    2012  

    Abstract: Les biomarqueurs appliqués à la médecine d'urgence sont en plein essor. Ils correspondent à des dosages biologiques spécifiques d’organes, témoignant de dysfonctions ou de souffrance de systèmes. Le développement de stratégies associant ces biomarqueurs ... ...

    Author's details edited by Yann-Érick Claessens, Patrick Ray
    Series title Références en médecine d’urgence. Collection de la SFMU
    Abstract Les biomarqueurs appliqués à la médecine d'urgence sont en plein essor. Ils correspondent à des dosages biologiques spécifiques d’organes, témoignant de dysfonctions ou de souffrance de systèmes. Le développement de stratégies associant ces biomarqueurs aux données traditionnelles a permis d’améliorer considérablement la qualité diagnostique et thérapeutique de la prise en charge des patients en situation d’urgence. La SFMU a décidé d’initier la collection « Références en médecine d’urgence » par un premier ouvrage sur ce thème. Coordonné par deux médecins urgentistes reconnus pour leur qualité d’experts, cette monographie associe également des cliniciens, biologistes et spécialistes d’organes impliqués dans le traitement de l’urgence. Ce livre fait le point sur les biomarqueurs actuellement disponibles ou dont le développement est suffisamment avancé pour avoir un intérêt au lit du malade. Conçu tant pour les cliniciens que pour les biologistes, il est un guide pour la pratique quotidienne et la réflexion autour de leur évaluation et leur développement .
    Keywords Biochemistry ; Critical care medicine ; Emergency medicine ; Medicine ; Politik Gesundheitspolitik
    Language French
    Size Online-Ressource, digital
    Publisher Springer
    Publishing place Paris
    Document type Book ; Online
    Note Description based upon print version of record
    ISBN 9782817802961 ; 9782817802978 ; 2817802969 ; 2817802977
    DOI 10.1007/978-2-8178-0297-8
    Database Former special subject collection: coastal and deep sea fishing

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  8. Article ; Online: Prognostic value of soluble urokinase plasminogen activator receptor in patients presenting to the emergency department with chest pain suggestive of acute coronary syndrome.

    Chenevier-Gobeaux, Camille / Lemarechal, Hervé / Doumenc, Benoit / Peschanski, Nicolas / Claessens, Yann-Erick / Borderie, Didier / Ray, Patrick

    Clinical biochemistry

    2021  Volume 92, Page(s) 19–24

    Abstract: Introduction: Soluble urokinase plasminogen activator receptor (suPAR) is a prognostic biomarker of cardiovascular disease.: Objectives: We aimed to evaluate the early prognostic value of suPAR in patients presenting to the emergency department (ED) ... ...

    Abstract Introduction: Soluble urokinase plasminogen activator receptor (suPAR) is a prognostic biomarker of cardiovascular disease.
    Objectives: We aimed to evaluate the early prognostic value of suPAR in patients presenting to the emergency department (ED) with chest pain suggestive of acute coronary syndrome (ACS).
    Patients and methods: In a post-hoc analysis from a multicenter study including patients with a chest pain < 6 h, suPAR concentrations at ED admission were studied according to the outcome at 30-days.
    Results: 198 patients (median age 56 years) in whom 16% had an ACS, were included. Fifteen (7.3%) patients presented a 30-day event. At ED admission, median (IQR) suPAR concentrations were higher in patients with a 30-day event in comparison to patients without event (4.54 (3.09-8.61) vs. 2.72 (2.10-3.43) ng/mL, p < 0.001). The ROC curve AUC of suPAR for the prediction of a 30-days event was 0.775 [95%CI: 0.710-0.831]. The optimal threshold was 3.3 ng/mL, with a sensitivity of 73 [45-92] % and a specificity of 72 [65-79] %. The association of a suPAR < 3.3 ng/mL AND a NT-proBNP < 160 ng/L AND a HEART score < 4 had a negative predictive value of 99 [91-100] %. A suPAR value at admission above 3.3 ng/mL was independently and significantly associated with a 30-day event in chest pain emergency patients (OR 4.87 [1.35-17.51], p = 0.015).
    Conclusion: suPAR is a promising biomarker for early prediction of events in chest pain emergency patients.
    MeSH term(s) Acute Coronary Syndrome/diagnosis ; Adult ; Aged ; Aged, 80 and over ; Biomarkers/blood ; Chest Pain ; Emergency Service, Hospital ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Prognosis ; Receptors, Urokinase Plasminogen Activator/blood
    Chemical Substances Biomarkers ; PLAUR protein, human ; Receptors, Urokinase Plasminogen Activator
    Language English
    Publishing date 2021-03-02
    Publishing country United States
    Document type Evaluation Study ; Journal Article ; Multicenter Study
    ZDB-ID 390372-2
    ISSN 1873-2933 ; 0009-9120
    ISSN (online) 1873-2933
    ISSN 0009-9120
    DOI 10.1016/j.clinbiochem.2021.02.009
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 624: Show issues Location:
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  9. Article ; Online: Reduction in cardiovascular emergency admissions in Monaco during the COVID-19 pandemic.

    Enache, Bogdan / Claessens, Yann-Erick / Boulay, Fabrice / Dor, Vincent / Eker, Armand / Civaia, Filippo / Pathak, Atul

    Clinical research in cardiology : official journal of the German Cardiac Society

    2020  Volume 109, Issue 12, Page(s) 1577–1578

    MeSH term(s) COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/transmission ; Cardiology Service, Hospital/trends ; Cardiovascular Diseases/diagnosis ; Cardiovascular Diseases/therapy ; Emergency Service, Hospital/trends ; Health Services Accessibility/trends ; Humans ; Monaco ; Patient Acceptance of Health Care ; Patient Admission/trends ; Retrospective Studies ; Social Isolation ; Time Factors
    Keywords covid19
    Language English
    Publishing date 2020-06-12
    Publishing country Germany
    Document type Letter
    ZDB-ID 2213295-8
    ISSN 1861-0692 ; 1861-0684
    ISSN (online) 1861-0692
    ISSN 1861-0684
    DOI 10.1007/s00392-020-01687-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book ; Online: Maladies rares en médecine d’urgence

    Claessens, Yann-Erick / Mouthon, Luc

    (Références en médecine d’urgence. Collection de la SFMU)

    2013  

    Abstract: La médecine d’urgence a pour spécificité d’assurer l’accueil et la prise en charge d’un très grand nombre de malades. Les pathologies communautaires et circonstancielles qui constituent les grands problèmes de santé publique forment le plus important ... ...

    Author's details by Yann-Erick Claessens, Luc Mouthon
    Series title Références en médecine d’urgence. Collection de la SFMU
    Abstract La médecine d’urgence a pour spécificité d’assurer l’accueil et la prise en charge d’un très grand nombre de malades. Les pathologies communautaires et circonstancielles qui constituent les grands problèmes de santé publique forment le plus important contingent de patients. Cependant, le service d’urgence est par essence un lieu privilégié pour rencontrer des patients porteurs de maladies rares. En raison de leur faible nombre, le médecin non expert du domaine pense rarement  à ce type de pathologie. Or, comme pour tout problème médical aigu, le retard diagnostique et thérapeutique a une incidence sur le devenir du patient présentant une maladie rare. Cet ouvrage a pour objet de rappeler au médecin urgentiste quelles sont les principales maladies oubliées et les principaux syndromes complexes qu’il pourra rencontrer lors de son exercice, et les éléments diagnostiques et de traitement permettant d’optimiser les soins en situation d’urgence. Il s'inscrit dans la collection « Références en médecine d'urgence » comme un guide quotidien pour la prise en charge des patients présentant ces maladies, certes rares, mais fréquemment rencontrées dans les services d'urgence
    Keywords Emergency medicine ; Internal medicine ; Medicine ; Metabolic diseases
    Language French
    Size Online-Ressource (XI, 428 p), online resource
    Publisher Springer Paris
    Publishing place Paris ;s.l
    Document type Book ; Online
    Note Description based upon print version of record
    ISBN 9782817803494 ; 9782817803500 ; 2817803493 ; 2817803507
    DOI 10.1007/978-2-8178-0350-0
    Database Former special subject collection: coastal and deep sea fishing

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