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Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study

BACKGROUND AND AIMS: The opioid epidemic has extended to many countries. Data regarding the accuracy of conventional prediction models including the Simplified Acute Physiologic Score (SAPS) II and acute physiology and chronic health evaluation (APACHE) II are scarce in opioid overdose cases. We eva...

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Autores principales: Sakhaee, Ehsan, Amirahmadi, Ali, Mahdiani, Morteza, Shojaei, Maziar, Hassanian‐Moghaddam, Hossein, Bauer, Roman, Zamani, Nasim, Pakdaman, Hossein, Gharagozli, Kourosh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9358662/
https://www.ncbi.nlm.nih.gov/pubmed/35949676
http://dx.doi.org/10.1002/hsr2.767
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author Sakhaee, Ehsan
Amirahmadi, Ali
Mahdiani, Morteza
Shojaei, Maziar
Hassanian‐Moghaddam, Hossein
Bauer, Roman
Zamani, Nasim
Pakdaman, Hossein
Gharagozli, Kourosh
author_facet Sakhaee, Ehsan
Amirahmadi, Ali
Mahdiani, Morteza
Shojaei, Maziar
Hassanian‐Moghaddam, Hossein
Bauer, Roman
Zamani, Nasim
Pakdaman, Hossein
Gharagozli, Kourosh
author_sort Sakhaee, Ehsan
collection PubMed
description BACKGROUND AND AIMS: The opioid epidemic has extended to many countries. Data regarding the accuracy of conventional prediction models including the Simplified Acute Physiologic Score (SAPS) II and acute physiology and chronic health evaluation (APACHE) II are scarce in opioid overdose cases. We evaluate the efficacy of adding quantitative electroencephalogram (qEEG) data to clinical and paraclinical data in the prediction of opioid overdose mortality using machine learning. METHODS: In a prospective study, we collected clinical/paraclinical, and qEEG data of 32 opioid‐poisoned patients. After preprocessing and Fast Fourier Transform analysis, absolute power was computed. Also, SAPS II was calculated. Eventually, data analysis was performed using SAPS II as a benchmark at three levels to predict the patient's course in comparison with SAPS II. First, the qEEG data set was used alone, secondly, the combination of the clinical/paraclinical, SAPS II, qEEG datasets, and the SAPS II‐based model was included in the pool of classifier models. RESULTS: Seven out of 32 (22%) died. SAPS II (cut‐off of 50.5) had a sensitivity/specificity/positive/negative predictive values of 85.7%, 84.0%, 60.0%, and 95.5% in predicting mortality, respectively. Adding majority voting on random forest with qEEG and clinical data, improved the model sensitivity, specificity, and positive and negative predictive values to 71.4%, 96%, 83.3%, and 92.3% (not significant). The model fusion level has 40% less prediction error. CONCLUSION: Considering the higher specificity and negative predictive value in our proposed model, it could predict survival much better than mortality. The model would constitute an indicator for better care of opioid poisoned patients in low resources settings, where intensive care unit beds are limited.
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spelling pubmed-93586622022-08-09 Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study Sakhaee, Ehsan Amirahmadi, Ali Mahdiani, Morteza Shojaei, Maziar Hassanian‐Moghaddam, Hossein Bauer, Roman Zamani, Nasim Pakdaman, Hossein Gharagozli, Kourosh Health Sci Rep Original Research BACKGROUND AND AIMS: The opioid epidemic has extended to many countries. Data regarding the accuracy of conventional prediction models including the Simplified Acute Physiologic Score (SAPS) II and acute physiology and chronic health evaluation (APACHE) II are scarce in opioid overdose cases. We evaluate the efficacy of adding quantitative electroencephalogram (qEEG) data to clinical and paraclinical data in the prediction of opioid overdose mortality using machine learning. METHODS: In a prospective study, we collected clinical/paraclinical, and qEEG data of 32 opioid‐poisoned patients. After preprocessing and Fast Fourier Transform analysis, absolute power was computed. Also, SAPS II was calculated. Eventually, data analysis was performed using SAPS II as a benchmark at three levels to predict the patient's course in comparison with SAPS II. First, the qEEG data set was used alone, secondly, the combination of the clinical/paraclinical, SAPS II, qEEG datasets, and the SAPS II‐based model was included in the pool of classifier models. RESULTS: Seven out of 32 (22%) died. SAPS II (cut‐off of 50.5) had a sensitivity/specificity/positive/negative predictive values of 85.7%, 84.0%, 60.0%, and 95.5% in predicting mortality, respectively. Adding majority voting on random forest with qEEG and clinical data, improved the model sensitivity, specificity, and positive and negative predictive values to 71.4%, 96%, 83.3%, and 92.3% (not significant). The model fusion level has 40% less prediction error. CONCLUSION: Considering the higher specificity and negative predictive value in our proposed model, it could predict survival much better than mortality. The model would constitute an indicator for better care of opioid poisoned patients in low resources settings, where intensive care unit beds are limited. John Wiley and Sons Inc. 2022-08-08 /pmc/articles/PMC9358662/ /pubmed/35949676 http://dx.doi.org/10.1002/hsr2.767 Text en © 2022 The Authors. Health Science Reports published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Sakhaee, Ehsan
Amirahmadi, Ali
Mahdiani, Morteza
Shojaei, Maziar
Hassanian‐Moghaddam, Hossein
Bauer, Roman
Zamani, Nasim
Pakdaman, Hossein
Gharagozli, Kourosh
Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
title Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
title_full Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
title_fullStr Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
title_full_unstemmed Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
title_short Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
title_sort developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9358662/
https://www.ncbi.nlm.nih.gov/pubmed/35949676
http://dx.doi.org/10.1002/hsr2.767
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