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  1. Article ; Online: Performing complex interdisciplinary procedures: Never on the patient the first time!

    Ghazali, Daniel Aiham / Ilha-Schuelter, Patricia / Oriot, Denis

    Medical education

    2023  Volume 58, Issue 2, Page(s) 263–264

    Language English
    Publishing date 2023-11-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 195274-2
    ISSN 1365-2923 ; 0308-0110
    ISSN (online) 1365-2923
    ISSN 0308-0110
    DOI 10.1111/medu.15282
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    In stock of ZB MED Cologne/Königswinter

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  2. Article ; Online: Contribution of certified registered nurse anaesthetists to the management of the COVID-19 pandemic health crisis.

    Ouersighni, Amina / Ghazali, Daniel Aiham

    Intensive & critical care nursing

    2020  Volume 60, Page(s) 102888

    MeSH term(s) Betacoronavirus ; COVID-19 ; Clinical Competence ; Coronavirus Infections/nursing ; Coronavirus Infections/therapy ; Humans ; Nurse Anesthetists/organization & administration ; Nurse's Role ; Nursing Staff, Hospital/organization & administration ; Pandemics ; Patient-Centered Care/methods ; Patient-Centered Care/organization & administration ; Pneumonia, Viral/nursing ; Pneumonia, Viral/therapy ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-05-26
    Publishing country Netherlands
    Document type Letter
    ZDB-ID 1105892-4
    ISSN 1532-4036 ; 0964-3397
    ISSN (online) 1532-4036
    ISSN 0964-3397
    DOI 10.1016/j.iccn.2020.102888
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  4. Article ; Online: Contribution of certified registered nurse anaesthetists to the management of the COVID-19 pandemic health crisis

    Ouersighni, Amina / Ghazali, Daniel Aiham

    Intensive and Critical Care Nursing

    2020  Volume 60, Page(s) 102888

    Keywords Critical Care ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 1105892-4
    ISSN 0964-3397
    ISSN 0964-3397
    DOI 10.1016/j.iccn.2020.102888
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    In stock of ZB MED Cologne/Königswinter

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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Early diagnosis of sepsis using an E-health application for a clinical early warning system outside of the intensive care unit

    Daniel Aiham Ghazali / Philippe Kenway / Christophe Choquet / Enrique Casalino

    Journal of Medical Case Reports, Vol 16, Iss 1, Pp 1-

    a case report

    2022  Volume 8

    Abstract: Abstract Background Elderly and frail patients who are unable to call for help in case of vital distress can develop complications during their hospitalization. As a supplement to clinical monitoring by the nursing staff, these patients can also be ... ...

    Abstract Abstract Background Elderly and frail patients who are unable to call for help in case of vital distress can develop complications during their hospitalization. As a supplement to clinical monitoring by the nursing staff, these patients can also be monitored in real time, with the Sensium E-health technology. An application notifies clinical staff of any change in their vital signs (heart rate, respiratory rate, temperature) outside of normal ranges, suggestive of physiological decline. Nurses and physicians are notified of these abnormal changes by email and also via mobile application (iPhone or iPad), allowing early intervention to prevent further deterioration. Case presentation An 86-year-old Caucasian female, with chronic kidney disease, was hospitalized in our medical unit for pyelonephritis associated with a moderate deterioration of serum creatinine. Remote continuous monitoring allowed us to diagnose clinical deterioration early and adjust her treatment. The treatment improved her clinical condition and amended the secondary sepsis with circulation failure in 2 days. Conclusions The prognosis for patients with acute complicated pyelonephritis is much worse than for those with uncomplicated pyelonephritis. Remote continuous monitoring might be helpful to early diagnose urosepsis. This technology leads to improved prognosis of patients without initial vital distress, allowing early treatment and admission to intensive care unit.
    Keywords E-health ; Older patient ; Sepsis ; Emergency medicine ; Intensive care unit ; Medicine ; R
    Subject code 610
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Predictive models in emergency medicine and their missing data strategies: a systematic review.

    Arnaud, Emilien / Elbattah, Mahmoud / Ammirati, Christine / Dequen, Gilles / Ghazali, Daniel Aiham

    NPJ digital medicine

    2023  Volume 6, Issue 1, Page(s) 28

    Abstract: In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute "data not purposely collected" (DNPC). ... ...

    Abstract In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute "data not purposely collected" (DNPC). This accepted information bias can be managed in various ways: dropping patients with missing data, imputing with the mean, or using automatic techniques (e.g., machine learning) to handle or impute the data. Here, we systematically reviewed the methods used to handle missing data in EM research. A systematic review was performed after searching PubMed with the query "(emergency medicine OR emergency service) AND (artificial intelligence OR machine learning)". Seventy-two studies were included in the review. The trained models variously predicted diagnosis in 25 (35%) publications, mortality in 21 (29%) publications, and probability of admission in 21 (29%) publications. Eight publications (11%) predicted two outcomes. Only 15 (21%) publications described their missing data. DNPC constitute the "missing data" in EM machine learning studies. Although DNPC have been described more rigorously since 2020, the descriptions in the literature are not exhaustive, systematic or homogeneous. Imputation appears to be the best strategy but requires more time and computational resources. To increase the quality and the comparability of studies, we recommend inclusion of the TRIPOD checklist in each new publication, summarizing the machine learning process in an explicit methodological diagram, and always publishing the area under the receiver operating characteristics curve-even when it is not the primary outcome.
    Language English
    Publishing date 2023-02-23
    Publishing country England
    Document type Journal Article ; Review
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-023-00770-6
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  7. Article ; Online: Early diagnosis of sepsis using an E-health application for a clinical early warning system outside of the intensive care unit: a case report.

    Ghazali, Daniel Aiham / Kenway, Philippe / Choquet, Christophe / Casalino, Enrique

    Journal of medical case reports

    2022  Volume 16, Issue 1, Page(s) 185

    Abstract: Background: Elderly and frail patients who are unable to call for help in case of vital distress can develop complications during their hospitalization. As a supplement to clinical monitoring by the nursing staff, these patients can also be monitored in ...

    Abstract Background: Elderly and frail patients who are unable to call for help in case of vital distress can develop complications during their hospitalization. As a supplement to clinical monitoring by the nursing staff, these patients can also be monitored in real time, with the Sensium E-health technology. An application notifies clinical staff of any change in their vital signs (heart rate, respiratory rate, temperature) outside of normal ranges, suggestive of physiological decline. Nurses and physicians are notified of these abnormal changes by email and also via mobile application (iPhone or iPad), allowing early intervention to prevent further deterioration.
    Case presentation: An 86-year-old Caucasian female, with chronic kidney disease, was hospitalized in our medical unit for pyelonephritis associated with a moderate deterioration of serum creatinine. Remote continuous monitoring allowed us to diagnose clinical deterioration early and adjust her treatment. The treatment improved her clinical condition and amended the secondary sepsis with circulation failure in 2 days.
    Conclusions: The prognosis for patients with acute complicated pyelonephritis is much worse than for those with uncomplicated pyelonephritis. Remote continuous monitoring might be helpful to early diagnose urosepsis. This technology leads to improved prognosis of patients without initial vital distress, allowing early treatment and admission to intensive care unit.
    MeSH term(s) Aged ; Aged, 80 and over ; Early Diagnosis ; Female ; Humans ; Intensive Care Units ; Male ; Pyelonephritis/diagnosis ; Pyelonephritis/therapy ; Sepsis/diagnosis ; Sepsis/therapy ; Telemedicine
    Language English
    Publishing date 2022-05-09
    Publishing country England
    Document type Case Reports ; Journal Article
    ZDB-ID 2269805-X
    ISSN 1752-1947 ; 1752-1947
    ISSN (online) 1752-1947
    ISSN 1752-1947
    DOI 10.1186/s13256-022-03385-9
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  8. Article ; Online: Effect of real-time feedback device compared to use or non-use of a checklist performance aid on post-training performance and retention of infant cardiopulmonary resuscitation: A randomized simulation-based trial.

    Ghazali, Daniel Aiham / Rousseau, Raphaëlle / Breque, Cyril / Oriot, Denis

    Australasian emergency care

    2022  Volume 26, Issue 1, Page(s) 36–44

    Abstract: Introduction: This study aims to determine the best method for achieving optimal performance of pediatric cardiopulmonary resuscitation (CPR) during simulation-based training, whether with or without a performance aid.: Methods: In this randomized ... ...

    Abstract Introduction: This study aims to determine the best method for achieving optimal performance of pediatric cardiopulmonary resuscitation (CPR) during simulation-based training, whether with or without a performance aid.
    Methods: In this randomized controlled study, 46 participants performed simulated CPR in pairs on a Resusci Baby QCPR™ mannequin, repeated after four weeks. All participants performed the first simulation without performance aids. For the second simulation, they were randomly assigned to one of three groups with stratification based on status: throughout CPR, Group A (n = 16) was the control group and did not use a performance aid; Group B (n = 16) used the CPR checklist; Group C (n = 14) used real-time visualization of their CPR activity on a feedback device. Overall performance was assessed using the QCPR™.
    Results: All groups demonstrated improved performance on the second simulation (p < 0.01). Use of the feedback device resulted in better CPR performance than use of the CPR checklist (p = 0.02) or no performance aid (p = 0.04). Additionally, participants thought that the QCPR™ could effectively improve their technical competences.
    Conclusions: Performance aid based on continuous feedback is helpful in the learning process. The use of the QCPR™, a real-time feedback device, improved the quality of resuscitation during infant CPR simulation-based training.
    MeSH term(s) Humans ; Infant ; Child ; Cardiopulmonary Resuscitation/methods ; Heart Arrest ; Feedback ; Checklist ; Learning
    Language English
    Publishing date 2022-07-29
    Publishing country Australia
    Document type Randomized Controlled Trial ; Journal Article
    ISSN 2588-994X
    ISSN (online) 2588-994X
    DOI 10.1016/j.auec.2022.07.005
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  9. Article ; Online: Development and Testing of a Hybrid Simulator for Emergent Umbilical Vein Catheter Insertion Simulation Training.

    Ghazali, Daniel Aiham / Cholet, Quitterie / Breque, Cyril / Oriot, Denis

    Simulation in healthcare : journal of the Society for Simulation in Healthcare

    2022  Volume 18, Issue 5, Page(s) 333–340

    Abstract: Introduction: Emergent umbilical venous catheter (eUVC) insertion is the recommended vascular access in neonatal resuscitation. Although the theoretical knowledge can be taught, existing models are either unrealistic (plastic) or train only the steps of ...

    Abstract Introduction: Emergent umbilical venous catheter (eUVC) insertion is the recommended vascular access in neonatal resuscitation. Although the theoretical knowledge can be taught, existing models are either unrealistic (plastic) or train only the steps of the task. This study aimed to develop and test a hybrid simulator for eUVC insertion training that would be realistic, reproducible, easy to build, and inexpensive, thereby facilitating detailed learning of the procedure.
    Methods: Development took place in the Poitiers simulation laboratory using a neonatal mannequin into which a real umbilical cord was integrated. In the first phase, pediatric and emergency physicians and residents tested the model. In the second phase, another group of participants tested the hybrid simulator and the same neonatal mannequin associated with an artificial umbilical cord. Participants completed a satisfaction survey.
    Results: A real umbilical cord connected to an intra-abdominal reservoir containing artificial blood was added to the mannequin, allowing insertion of the eUVC, drawback of blood, and infusion of fluids using the real anatomical structures. The model was easily reproduced and assembled in less than 30 minutes; the cost of construction and use was evaluated at €115. One hundred two participants tested the model, 60 in the first phase and 42 in the second. The success rate was higher in fully trained compared with untrained participants. All were satisfied, 97% found the model realistic, and 78.6% strongly recommended the use of this model. The participants believed that the hybrid simulator allowed better learning and a gain in performance and self-confidence in comparison with the same mannequin with an artificial umbilical cord.
    Conclusions: A hybrid simulator was developed for eUVC insertion. Participants were satisfied with this model, which was realistic, reproducible, easy to use, inexpensive, and facilitated an understanding of the anatomy and performance of all steps for successful eUVC insertion.
    MeSH term(s) Humans ; Infant, Newborn ; Child ; Umbilical Veins ; Resuscitation/education ; Catheterization/methods ; Simulation Training/methods ; Catheters
    Language English
    Publishing date 2022-11-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2223429-9
    ISSN 1559-713X ; 1559-2332
    ISSN (online) 1559-713X
    ISSN 1559-2332
    DOI 10.1097/SIH.0000000000000700
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  10. Article ; Online: Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic

    Emilien Arnaud / Mahmoud Elbattah / Christine Ammirati / Gilles Dequen / Daniel Aiham Ghazali

    International Journal of Environmental Research and Public Health, Vol 19, Iss 9667, p

    A Prospective, Single-Center Study

    2022  Volume 9667

    Abstract: Background: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy ...

    Abstract Background: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called “Prediction of the Patient Pathway in the Emergency Department” (3P-U) to predict patient outcomes. Materials: Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH’s ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as “likely to be discharged”, 18,815 (17.8%) were flagged as “likely to be admitted”, and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units. Discussion and conclusions: The 3P-U model’s AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.
    Keywords COVID-19 ; artificial intelligence ; triage ; management of organizations ; emergency department ; Medicine ; R
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
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