Cargando…
Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland
BACKGROUND: The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 in unvaccinated adults at the time of diagnosis. M...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451645/ https://www.ncbi.nlm.nih.gov/pubmed/36071509 http://dx.doi.org/10.1186/s41512-022-00130-0 |
_version_ | 1784784774472138752 |
---|---|
author | Eythorsson, Elias Bjarnadottir, Valgerdur Runolfsdottir, Hrafnhildur Linnet Helgason, Dadi Ingvarsson, Ragnar Freyr Bjornsson, Helgi K. Olafsdottir, Lovisa Bjork Bjarnadottir, Solveig Agustsson, Arnar Snaer Oskarsdottir, Kristin Thorvaldsson, Hrafn Hliddal Kristjansdottir, Gudrun Bjornsson, Aron Hjalti Emilsdottir, Arna R. Armannsdottir, Brynja Gudlaugsson, Olafur Hansdottir, Sif Gottfredsson, Magnus Bjarnason, Agnar Sigurdsson, Martin I. Indridason, Olafur S. Palsson, Runolfur |
author_facet | Eythorsson, Elias Bjarnadottir, Valgerdur Runolfsdottir, Hrafnhildur Linnet Helgason, Dadi Ingvarsson, Ragnar Freyr Bjornsson, Helgi K. Olafsdottir, Lovisa Bjork Bjarnadottir, Solveig Agustsson, Arnar Snaer Oskarsdottir, Kristin Thorvaldsson, Hrafn Hliddal Kristjansdottir, Gudrun Bjornsson, Aron Hjalti Emilsdottir, Arna R. Armannsdottir, Brynja Gudlaugsson, Olafur Hansdottir, Sif Gottfredsson, Magnus Bjarnason, Agnar Sigurdsson, Martin I. Indridason, Olafur S. Palsson, Runolfur |
author_sort | Eythorsson, Elias |
collection | PubMed |
description | BACKGROUND: The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 in unvaccinated adults at the time of diagnosis. METHODS: All SARS-CoV-2-positive adults in Iceland were prospectively enrolled into a telehealth service at diagnosis. A multivariable proportional-odds logistic regression model was derived from information obtained during the enrollment interview of those diagnosed between February 27 and December 31, 2020 who met the inclusion criteria. Outcomes were defined on an ordinal scale: (1) no need for escalation of care during follow-up; (2) need for urgent care visit; (3) hospitalization; and (4) admission to intensive care unit (ICU) or death. Missing data were multiply imputed using chained equations and the model was internally validated using bootstrapping techniques. Decision curve analysis was performed. RESULTS: The prognostic model was derived from 4756 SARS-CoV-2-positive persons. In total, 375 (7.9%) only required urgent care visits, 188 (4.0%) were hospitalized and 50 (1.1%) were either admitted to ICU or died due to complications of COVID-19. The model included age, sex, body mass index (BMI), current smoking, underlying conditions, and symptoms and clinical severity score at enrollment. On internal validation, the optimism-corrected Nagelkerke’s R(2) was 23.4% (95%CI, 22.7–24.2), the C-statistic was 0.793 (95%CI, 0.789-0.797) and the calibration slope was 0.97 (95%CI, 0.96–0.98). Outcome-specific indices were for urgent care visit or worse (calibration intercept -0.04 [95%CI, -0.06 to -0.02], E(max) 0.014 [95%CI, 0.008–0.020]), hospitalization or worse (calibration intercept -0.06 [95%CI, -0.12 to -0.03], E(max) 0.018 [95%CI, 0.010–0.027]), and ICU admission or death (calibration intercept -0.10 [95%CI, -0.15 to -0.04] and E(max) 0.027 [95%CI, 0.013–0.041]). CONCLUSION: Our prognostic model can accurately predict the later need for urgent outpatient evaluation, hospitalization, and ICU admission and death among unvaccinated SARS-CoV-2-positive adults in the general population at the time of diagnosis, using information obtained by telephone interview. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00130-0. |
format | Online Article Text |
id | pubmed-9451645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94516452022-09-08 Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland Eythorsson, Elias Bjarnadottir, Valgerdur Runolfsdottir, Hrafnhildur Linnet Helgason, Dadi Ingvarsson, Ragnar Freyr Bjornsson, Helgi K. Olafsdottir, Lovisa Bjork Bjarnadottir, Solveig Agustsson, Arnar Snaer Oskarsdottir, Kristin Thorvaldsson, Hrafn Hliddal Kristjansdottir, Gudrun Bjornsson, Aron Hjalti Emilsdottir, Arna R. Armannsdottir, Brynja Gudlaugsson, Olafur Hansdottir, Sif Gottfredsson, Magnus Bjarnason, Agnar Sigurdsson, Martin I. Indridason, Olafur S. Palsson, Runolfur Diagn Progn Res Research BACKGROUND: The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to predict the severity of COVID-19 in unvaccinated adults at the time of diagnosis. METHODS: All SARS-CoV-2-positive adults in Iceland were prospectively enrolled into a telehealth service at diagnosis. A multivariable proportional-odds logistic regression model was derived from information obtained during the enrollment interview of those diagnosed between February 27 and December 31, 2020 who met the inclusion criteria. Outcomes were defined on an ordinal scale: (1) no need for escalation of care during follow-up; (2) need for urgent care visit; (3) hospitalization; and (4) admission to intensive care unit (ICU) or death. Missing data were multiply imputed using chained equations and the model was internally validated using bootstrapping techniques. Decision curve analysis was performed. RESULTS: The prognostic model was derived from 4756 SARS-CoV-2-positive persons. In total, 375 (7.9%) only required urgent care visits, 188 (4.0%) were hospitalized and 50 (1.1%) were either admitted to ICU or died due to complications of COVID-19. The model included age, sex, body mass index (BMI), current smoking, underlying conditions, and symptoms and clinical severity score at enrollment. On internal validation, the optimism-corrected Nagelkerke’s R(2) was 23.4% (95%CI, 22.7–24.2), the C-statistic was 0.793 (95%CI, 0.789-0.797) and the calibration slope was 0.97 (95%CI, 0.96–0.98). Outcome-specific indices were for urgent care visit or worse (calibration intercept -0.04 [95%CI, -0.06 to -0.02], E(max) 0.014 [95%CI, 0.008–0.020]), hospitalization or worse (calibration intercept -0.06 [95%CI, -0.12 to -0.03], E(max) 0.018 [95%CI, 0.010–0.027]), and ICU admission or death (calibration intercept -0.10 [95%CI, -0.15 to -0.04] and E(max) 0.027 [95%CI, 0.013–0.041]). CONCLUSION: Our prognostic model can accurately predict the later need for urgent outpatient evaluation, hospitalization, and ICU admission and death among unvaccinated SARS-CoV-2-positive adults in the general population at the time of diagnosis, using information obtained by telephone interview. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00130-0. BioMed Central 2022-09-08 /pmc/articles/PMC9451645/ /pubmed/36071509 http://dx.doi.org/10.1186/s41512-022-00130-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Eythorsson, Elias Bjarnadottir, Valgerdur Runolfsdottir, Hrafnhildur Linnet Helgason, Dadi Ingvarsson, Ragnar Freyr Bjornsson, Helgi K. Olafsdottir, Lovisa Bjork Bjarnadottir, Solveig Agustsson, Arnar Snaer Oskarsdottir, Kristin Thorvaldsson, Hrafn Hliddal Kristjansdottir, Gudrun Bjornsson, Aron Hjalti Emilsdottir, Arna R. Armannsdottir, Brynja Gudlaugsson, Olafur Hansdottir, Sif Gottfredsson, Magnus Bjarnason, Agnar Sigurdsson, Martin I. Indridason, Olafur S. Palsson, Runolfur Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland |
title | Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland |
title_full | Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland |
title_fullStr | Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland |
title_full_unstemmed | Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland |
title_short | Development of a prognostic model of COVID-19 severity: a population-based cohort study in Iceland |
title_sort | development of a prognostic model of covid-19 severity: a population-based cohort study in iceland |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451645/ https://www.ncbi.nlm.nih.gov/pubmed/36071509 http://dx.doi.org/10.1186/s41512-022-00130-0 |
work_keys_str_mv | AT eythorssonelias developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT bjarnadottirvalgerdur developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT runolfsdottirhrafnhildurlinnet developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT helgasondadi developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT ingvarssonragnarfreyr developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT bjornssonhelgik developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT olafsdottirlovisabjork developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT bjarnadottirsolveig developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT agustssonarnarsnaer developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT oskarsdottirkristin developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT thorvaldssonhrafnhliddal developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT kristjansdottirgudrun developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT bjornssonaronhjalti developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT emilsdottirarnar developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT armannsdottirbrynja developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT gudlaugssonolafur developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT hansdottirsif developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT gottfredssonmagnus developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT bjarnasonagnar developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT sigurdssonmartini developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT indridasonolafurs developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland AT palssonrunolfur developmentofaprognosticmodelofcovid19severityapopulationbasedcohortstudyiniceland |