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Internal Validation of a Subjective Bayesian Model for the Prediction of Anesthetic Accidents in Hospitals in Kinshasa

Received: 8 April 2022     Accepted: 2 May 2022     Published: 24 May 2022
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Abstract

Background and Aims: This study evaluates the Subjective Bayes Model (SBM) by comparing it to the consensus of the 75 hypothetical cases having experienced an anesthetic accident, generated by the Experts. Methods: The experts generate the cases with anaesthetic accidents and determine the degrees of agreement within and between experts, the discrimination criterion called Cut Off Point (C.O.P.), and look for the values of the following parameters: sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), overall effectiveness value (VEG). Results: The laboration of the 75 hypothetical cases of anaesthetic accidents by the experts. The intra and inter expert agreement was 100% perfect reflecting the consistency of the experts. The MSB predicts the occurrence of AA in 37 cases and the non-occurrence of AA in 27 cases confirmed by the consensus of the experts: the discrimination criterion (Cut of point = COP) is equal to 0.5, the MSB presents a good intrinsic validity with test performances of Se = 94. 8%, Sp = 75%, VP = 80%, NPV = 93% and VEG = 85%, the MSB gave an 80% probability that an AA identified as having occurred would actually occur (PPV) and a 93% probability that an AA identified as not having occurred would not occur (NPV). Conclusion: Expert consensus on the occurrence of SAs in the 75 hypothetical cases of anaesthetic accidents generated by the Experts was used to determine the internal validation of the Subjective Bayes Model of anaesthetic accident prediction.

Published in International Journal of Anesthesia and Clinical Medicine (Volume 10, Issue 1)
DOI 10.11648/j.ijacm.20221001.16
Page(s) 32-37
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Anaesthetic Accident (AA), Internal Validation, Subjective Bayes Model

References
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  • APA Style

    Berthe Nsimire Barhayiga, Gibency Mpenda Mfulani, Konde, Rostin Matendo Mabela, Adolphe Manzanza Kilembe, et al. (2022). Internal Validation of a Subjective Bayesian Model for the Prediction of Anesthetic Accidents in Hospitals in Kinshasa. International Journal of Anesthesia and Clinical Medicine, 10(1), 32-37. https://doi.org/10.11648/j.ijacm.20221001.16

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    ACS Style

    Berthe Nsimire Barhayiga; Gibency Mpenda Mfulani; Konde; Rostin Matendo Mabela; Adolphe Manzanza Kilembe, et al. Internal Validation of a Subjective Bayesian Model for the Prediction of Anesthetic Accidents in Hospitals in Kinshasa. Int. J. Anesth. Clin. Med. 2022, 10(1), 32-37. doi: 10.11648/j.ijacm.20221001.16

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    AMA Style

    Berthe Nsimire Barhayiga, Gibency Mpenda Mfulani, Konde, Rostin Matendo Mabela, Adolphe Manzanza Kilembe, et al. Internal Validation of a Subjective Bayesian Model for the Prediction of Anesthetic Accidents in Hospitals in Kinshasa. Int J Anesth Clin Med. 2022;10(1):32-37. doi: 10.11648/j.ijacm.20221001.16

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  • @article{10.11648/j.ijacm.20221001.16,
      author = {Berthe Nsimire Barhayiga and Gibency Mpenda Mfulani and Konde and Rostin Matendo Mabela and Adolphe Manzanza Kilembe and Sylvain Mukongo Munyanga and Christian Kisoka Lusunsi and Benjamin Longo-Mbenza},
      title = {Internal Validation of a Subjective Bayesian Model for the Prediction of Anesthetic Accidents in Hospitals in Kinshasa},
      journal = {International Journal of Anesthesia and Clinical Medicine},
      volume = {10},
      number = {1},
      pages = {32-37},
      doi = {10.11648/j.ijacm.20221001.16},
      url = {https://doi.org/10.11648/j.ijacm.20221001.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijacm.20221001.16},
      abstract = {Background and Aims: This study evaluates the Subjective Bayes Model (SBM) by comparing it to the consensus of the 75 hypothetical cases having experienced an anesthetic accident, generated by the Experts. Methods: The experts generate the cases with anaesthetic accidents and determine the degrees of agreement within and between experts, the discrimination criterion called Cut Off Point (C.O.P.), and look for the values of the following parameters: sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), overall effectiveness value (VEG). Results: The laboration of the 75 hypothetical cases of anaesthetic accidents by the experts. The intra and inter expert agreement was 100% perfect reflecting the consistency of the experts. The MSB predicts the occurrence of AA in 37 cases and the non-occurrence of AA in 27 cases confirmed by the consensus of the experts: the discrimination criterion (Cut of point = COP) is equal to 0.5, the MSB presents a good intrinsic validity with test performances of Se = 94. 8%, Sp = 75%, VP = 80%, NPV = 93% and VEG = 85%, the MSB gave an 80% probability that an AA identified as having occurred would actually occur (PPV) and a 93% probability that an AA identified as not having occurred would not occur (NPV). Conclusion: Expert consensus on the occurrence of SAs in the 75 hypothetical cases of anaesthetic accidents generated by the Experts was used to determine the internal validation of the Subjective Bayes Model of anaesthetic accident prediction.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Internal Validation of a Subjective Bayesian Model for the Prediction of Anesthetic Accidents in Hospitals in Kinshasa
    AU  - Berthe Nsimire Barhayiga
    AU  - Gibency Mpenda Mfulani
    AU  - Konde
    AU  - Rostin Matendo Mabela
    AU  - Adolphe Manzanza Kilembe
    AU  - Sylvain Mukongo Munyanga
    AU  - Christian Kisoka Lusunsi
    AU  - Benjamin Longo-Mbenza
    Y1  - 2022/05/24
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijacm.20221001.16
    DO  - 10.11648/j.ijacm.20221001.16
    T2  - International Journal of Anesthesia and Clinical Medicine
    JF  - International Journal of Anesthesia and Clinical Medicine
    JO  - International Journal of Anesthesia and Clinical Medicine
    SP  - 32
    EP  - 37
    PB  - Science Publishing Group
    SN  - 2997-2698
    UR  - https://doi.org/10.11648/j.ijacm.20221001.16
    AB  - Background and Aims: This study evaluates the Subjective Bayes Model (SBM) by comparing it to the consensus of the 75 hypothetical cases having experienced an anesthetic accident, generated by the Experts. Methods: The experts generate the cases with anaesthetic accidents and determine the degrees of agreement within and between experts, the discrimination criterion called Cut Off Point (C.O.P.), and look for the values of the following parameters: sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), overall effectiveness value (VEG). Results: The laboration of the 75 hypothetical cases of anaesthetic accidents by the experts. The intra and inter expert agreement was 100% perfect reflecting the consistency of the experts. The MSB predicts the occurrence of AA in 37 cases and the non-occurrence of AA in 27 cases confirmed by the consensus of the experts: the discrimination criterion (Cut of point = COP) is equal to 0.5, the MSB presents a good intrinsic validity with test performances of Se = 94. 8%, Sp = 75%, VP = 80%, NPV = 93% and VEG = 85%, the MSB gave an 80% probability that an AA identified as having occurred would actually occur (PPV) and a 93% probability that an AA identified as not having occurred would not occur (NPV). Conclusion: Expert consensus on the occurrence of SAs in the 75 hypothetical cases of anaesthetic accidents generated by the Experts was used to determine the internal validation of the Subjective Bayes Model of anaesthetic accident prediction.
    VL  - 10
    IS  - 1
    ER  - 

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Author Information
  • Department of Anesthesia-Resuscitation, University Clinics of Kinshasa, Kinshasa, Democratic Republic of Congo

  • Department of Anesthesia-Resuscitation, University Clinics of Kinshasa, Kinshasa, Democratic Republic of Congo

  • School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo

  • Department of Mathematics, Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo

  • Department of Anesthesia-Resuscitation, University Clinics of Kinshasa, Kinshasa, Democratic Republic of Congo

  • School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo

  • Department of Public Health, Lomo University for Research, Kinshasa, Democratic Republic of Congo

  • Cardiology Service, Department of Internal Medicine, University Clinics of Kinshasa, Kinshasa, Democratic Republic of Congo

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