Cargando…

Personalized predictions of adverse side effects of the COVID-19 vaccines

BACKGROUND: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictio...

Descripción completa

Detalles Bibliográficos
Autores principales: Jamshidi, Elham, Asgary, Amirhossein, Kharrazi, Ali Yazdizadeh, Tavakoli, Nader, Zali, Alireza, Mehrazi, Maryam, Jamshidi, Masoud, Farrokhi, Babak, Maher, Ali, von Garnier, Christophe, Rahi, Sahand Jamal, Mansouri, Nahal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800018/
https://www.ncbi.nlm.nih.gov/pubmed/36597482
http://dx.doi.org/10.1016/j.heliyon.2022.e12753
_version_ 1784861204474232832
author Jamshidi, Elham
Asgary, Amirhossein
Kharrazi, Ali Yazdizadeh
Tavakoli, Nader
Zali, Alireza
Mehrazi, Maryam
Jamshidi, Masoud
Farrokhi, Babak
Maher, Ali
von Garnier, Christophe
Rahi, Sahand Jamal
Mansouri, Nahal
author_facet Jamshidi, Elham
Asgary, Amirhossein
Kharrazi, Ali Yazdizadeh
Tavakoli, Nader
Zali, Alireza
Mehrazi, Maryam
Jamshidi, Masoud
Farrokhi, Babak
Maher, Ali
von Garnier, Christophe
Rahi, Sahand Jamal
Mansouri, Nahal
author_sort Jamshidi, Elham
collection PubMed
description BACKGROUND: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. METHODS: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). RESULTS: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620–0.686, 0.685–0.716, 0.632–0.727, 0.527–0.598, 0.548–0.655, 0.545–0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777–0.867, 0.795–0.848, 0.857–0.906, 0.788–0.875, 0.683–0.850, and 0.486–0.680, respectively. CONCLUSIONS: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.
format Online
Article
Text
id pubmed-9800018
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-98000182022-12-30 Personalized predictions of adverse side effects of the COVID-19 vaccines Jamshidi, Elham Asgary, Amirhossein Kharrazi, Ali Yazdizadeh Tavakoli, Nader Zali, Alireza Mehrazi, Maryam Jamshidi, Masoud Farrokhi, Babak Maher, Ali von Garnier, Christophe Rahi, Sahand Jamal Mansouri, Nahal Heliyon Research Article BACKGROUND: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. METHODS: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). RESULTS: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620–0.686, 0.685–0.716, 0.632–0.727, 0.527–0.598, 0.548–0.655, 0.545–0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777–0.867, 0.795–0.848, 0.857–0.906, 0.788–0.875, 0.683–0.850, and 0.486–0.680, respectively. CONCLUSIONS: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects. Elsevier 2022-12-30 /pmc/articles/PMC9800018/ /pubmed/36597482 http://dx.doi.org/10.1016/j.heliyon.2022.e12753 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Jamshidi, Elham
Asgary, Amirhossein
Kharrazi, Ali Yazdizadeh
Tavakoli, Nader
Zali, Alireza
Mehrazi, Maryam
Jamshidi, Masoud
Farrokhi, Babak
Maher, Ali
von Garnier, Christophe
Rahi, Sahand Jamal
Mansouri, Nahal
Personalized predictions of adverse side effects of the COVID-19 vaccines
title Personalized predictions of adverse side effects of the COVID-19 vaccines
title_full Personalized predictions of adverse side effects of the COVID-19 vaccines
title_fullStr Personalized predictions of adverse side effects of the COVID-19 vaccines
title_full_unstemmed Personalized predictions of adverse side effects of the COVID-19 vaccines
title_short Personalized predictions of adverse side effects of the COVID-19 vaccines
title_sort personalized predictions of adverse side effects of the covid-19 vaccines
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800018/
https://www.ncbi.nlm.nih.gov/pubmed/36597482
http://dx.doi.org/10.1016/j.heliyon.2022.e12753
work_keys_str_mv AT jamshidielham personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT asgaryamirhossein personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT kharrazialiyazdizadeh personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT tavakolinader personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT zalialireza personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT mehrazimaryam personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT jamshidimasoud personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT farrokhibabak personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT maherali personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT vongarnierchristophe personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT rahisahandjamal personalizedpredictionsofadversesideeffectsofthecovid19vaccines
AT mansourinahal personalizedpredictionsofadversesideeffectsofthecovid19vaccines