Cargando…
Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients
Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for thes...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648888/ https://www.ncbi.nlm.nih.gov/pubmed/36357420 http://dx.doi.org/10.1038/s41467-022-34646-2 |
_version_ | 1784827677608247296 |
---|---|
author | Levy, Todd J. Coppa, Kevin Cang, Jinxuan Barnaby, Douglas P. Paradis, Marc D. Cohen, Stuart L. Makhnevich, Alex van Klaveren, David Kent, David M. Davidson, Karina W. Hirsch, Jamie S. Zanos, Theodoros P. |
author_facet | Levy, Todd J. Coppa, Kevin Cang, Jinxuan Barnaby, Douglas P. Paradis, Marc D. Cohen, Stuart L. Makhnevich, Alex van Klaveren, David Kent, David M. Davidson, Karina W. Hirsch, Jamie S. Zanos, Theodoros P. |
author_sort | Levy, Todd J. |
collection | PubMed |
description | Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits. |
format | Online Article Text |
id | pubmed-9648888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96488882022-11-14 Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients Levy, Todd J. Coppa, Kevin Cang, Jinxuan Barnaby, Douglas P. Paradis, Marc D. Cohen, Stuart L. Makhnevich, Alex van Klaveren, David Kent, David M. Davidson, Karina W. Hirsch, Jamie S. Zanos, Theodoros P. Nat Commun Article Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits. Nature Publishing Group UK 2022-11-10 /pmc/articles/PMC9648888/ /pubmed/36357420 http://dx.doi.org/10.1038/s41467-022-34646-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Levy, Todd J. Coppa, Kevin Cang, Jinxuan Barnaby, Douglas P. Paradis, Marc D. Cohen, Stuart L. Makhnevich, Alex van Klaveren, David Kent, David M. Davidson, Karina W. Hirsch, Jamie S. Zanos, Theodoros P. Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients |
title | Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients |
title_full | Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients |
title_fullStr | Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients |
title_full_unstemmed | Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients |
title_short | Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients |
title_sort | development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized covid-19 patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648888/ https://www.ncbi.nlm.nih.gov/pubmed/36357420 http://dx.doi.org/10.1038/s41467-022-34646-2 |
work_keys_str_mv | AT levytoddj developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT coppakevin developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT cangjinxuan developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT barnabydouglasp developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT paradismarcd developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT cohenstuartl developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT makhnevichalex developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT vanklaverendavid developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT kentdavidm developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT davidsonkarinaw developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT hirschjamies developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients AT zanostheodorosp developmentandvalidationofselfmonitoringautoupdatingprognosticmodelsofsurvivalforhospitalizedcovid19patients |