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

Berthe Nsimire Barhayiga; Gibency Mpenda Mfulani; Konde; Rostin Matendo Mabela; Adolphe Manzanza Kilembe; Sylvain Mukongo Munyanga; Christian Kisoka Lusunsi; Benjamin Longo-Mbenza

doi:doi:10.11648/j.ijacm.20221001.16

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

Berthe Nsimire Barhayiga, Gibency Mpenda Mfulani, Konde, Rostin Matendo Mabela, Adolphe Manzanza Kilembe, Sylvain Mukongo Munyanga, Christian Kisoka Lusunsi, Benjamin Longo-Mbenza

Published in International Journal of Anesthesia and Clinical Medicine (Volume 10, Issue 1)

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Kuczkowski KM. Nonobstetric surgery in the parturient: anesthetic considerations. J Clin Anesth 2006; 18: 5-7.
[2]	Ouro-Bang' NA, Maman AF, Tomta K, KangnI N, Mouzou T, Egbohou P, Ouro-Bang' NA D, et al. Evaluation of the anesthesia technical platform in Togo: Multicenter survey focused on 23 health facilities. Black African Medicine 2008; 55 (2): 101-105.
[3]	Cheney FW, Posner K, Caplan RA, Ward RJ. Standard of care and anesthesia liability. JAMA 2000; 261 (11) ׃ 1599-1603.
[4]	Van Houwe P. Anesthesia in Africa: quo vadis? Acte Anaesthesiol Belg. 2007; 58 (3): 161-162. PubMed \| Google Scholar.
[5]	Ross AF, Tinker JH. The anesthetic risk, 791-825 in "Anesthesia (French edition)". Miller RD, Flammarion, Paris 2005.
[6]	Fenton, N., Neil, M., Risk Assessment and Decision Analysis with Bayesian Networks, CRC Press, Boca Raton, 2011.
[7]	Campling EA, Devlin HB, Hoile RW, Lunn JN. The report of the National Confidential Inquiry into perioperative deaths 2002/2003. (National CEPOD), London 2005.
[8]	Ka SB, Diatta B. Review of anesthetic activities at Saint Louis Regional Hospital from July to December 2000; Rev Afr Anesth Med Urg 2001; 6 (2): 63-70.
[9]	Luce V, Auroy Y, Ausset S, Luci P, Velay H, Benhamou D. Use of a database of epidemiological anesthetic incidents of intraoperative arterial hypotension. AFAR 2004; (8): 788-793.
[10]	Ka Sall B, Diatta B. Review of anesthetic activities at Saint Louis Regional Hospital from July to December 2000. Rev Afr Anesth Med Urg 2001; 6 (2): 63-70.
[11]	Assina S. Practice of anesthesia in Chad in 2000: current situation and prospects for improvement. Med thesis, Cotonou, 2001.
[12]	David H., Giustafson We, Lawrence Dp, Warner Vs: Subjective probabilities in medical diagnosis. Transactions on man-machine systems. 1969. Vol. 10, 3 Sep.
[13]	Audroing, JF: Introduction to probabilistic calculus and sampling theory, French documentation, Paris 1984.
[14]	Munyanga Sm et Coll.: Predicting the success of Primary Health Care Districts in Zaire. A subjective approach. Dissertation submitted on the 12th Day of April. 1993.
[15]	Holmes, D., Jain, L., Innovations in Bayesian Networks, Springer, Heidelberg, 2008, 317 p.
[16]	Naïm, P., Wuillemin, P., Leray, P., Pourret, O., Becker, A., Bayesian networks, 3rd ed., Eyrolles, Paris, 2007, 424 p. 10. Koski, M., Noble, JM, Bayesian Networks, John Wiley & Sons, Chichester, 2009, 356 p.
[17]	Otteni JC, Desmonts JM, Harberer JP. French and foreign recommendations on the practice of anesthesia resuscitation. Ann Fr Anesth Reanim 2000, (20): 537-548.
[18]	Recommendations for structures and equipment for pediatric anesthesia. Paris, SFAR, September 2000.
[19]	Okafor UV, Ezegwui HU, and Ekwazi K. Trends of different forms of anesthesia for caesarean section in South-eastern Nigeria. Journal of Obstetrics and Gynecology. 2009; 29 (5): 392-395. PubMed \| Google Scholar.
[20]	Venet C. Collection of anesthesia incidents and accidents at the Grenoble University Hospital. Thesis presented at the Joseph FOURNIER University Faculty of Medicine of Grenoble on May 5, 2000.

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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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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

Berthe Nsimire Barhayiga

Department of Anesthesia-Resuscitation, University Clinics of Kinshasa, Kinshasa, Democratic Republic of Congo
Gibency Mpenda Mfulani

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

School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
Rostin Matendo Mabela

Department of Mathematics, Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
Adolphe Manzanza Kilembe

Department of Anesthesia-Resuscitation, University Clinics of Kinshasa, Kinshasa, Democratic Republic of Congo
Sylvain Mukongo Munyanga

School of Public Health, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
Christian Kisoka Lusunsi

Department of Public Health, Lomo University for Research, Kinshasa, Democratic Republic of Congo
Benjamin Longo-Mbenza

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

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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}
}

Copy | Download

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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