Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients

David Monterde,1,2 Gerard Carot-Sans,2,3 Miguel Cainzos-Achirica,4,5 Sònia Abilleira,1,6 Marc Coca,2,3 Emili Vela,2,3 Montse Clèries,2,3 Damià Valero-Bover,2,3 Josep Comin-Colet,7– 9 Luis García-Eroles,2,3 Pol Pérez-Sust,3 Miquel Arrufat,1 Yolanda Lejardi,1 Jordi Piera-Jiménez2,3,10 1Catalan Institu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Monterde D, Carot-Sans G, Cainzos-Achirica M, Abilleira S, Coca M, Vela E, Clèries M, Valero-Bover D, Comin-Colet J, García-Eroles L, Pérez-Sust P, Arrufat M, Lejardi Y, Piera-Jiménez J
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	comorbidity multimorbidity covid-19 hospitalization risk Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/2acbdc9c6dd5466fa603d33857bede8d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2acbdc9c6dd5466fa603d33857bede8d
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	comorbidity multimorbidity covid-19 hospitalization risk Public aspects of medicine RA1-1270
spellingShingle	comorbidity multimorbidity covid-19 hospitalization risk Public aspects of medicine RA1-1270 Monterde D Carot-Sans G Cainzos-Achirica M Abilleira S Coca M Vela E Clèries M Valero-Bover D Comin-Colet J García-Eroles L Pérez-Sust P Arrufat M Lejardi Y Piera-Jiménez J Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients
description	David Monterde,1,2 Gerard Carot-Sans,2,3 Miguel Cainzos-Achirica,4,5 Sònia Abilleira,1,6 Marc Coca,2,3 Emili Vela,2,3 Montse Clèries,2,3 Damià Valero-Bover,2,3 Josep Comin-Colet,7– 9 Luis García-Eroles,2,3 Pol Pérez-Sust,3 Miquel Arrufat,1 Yolanda Lejardi,1 Jordi Piera-Jiménez2,3,10 1Catalan Institute of Health, Barcelona, Spain; 2Digitalization for the Sustainability of the Healthcare System (DS3), Sistema de Salut de Catalunya, Barcelona, Spain; 3Servei Català de la Salut, Barcelona, Spain; 4Center for Outcomes Research, Houston Methodist, Houston, TX, USA; 5Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, MD, USA; 6CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; 7Department of Cardiology, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain; 8Bioheart-Cardiovascular Diseases Research Group (Idibell), L’Hospitalet de Llobregat, Barcelona, Spain; 9Department of Clinical Sciences, School of Medicine, Universität de Barcelona - UB, L’Hospitalet de Llobregat, Barcelona, Spain; 10Open Evidence Research Group, Universitat Oberta de Catalunya, Barcelona, SpainCorrespondence: Jordi Piera-JiménezServei Català de la Salut (CatSalut), Travessera de les Corts, 131-159 (Edifici Olímpia), Barcelona, 08028, SpainTel +34 634283110Email jpiera@catsalut.catBackground: Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients’ complexity.Purpose: To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity.Materials and Methods: We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC).Results: Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624– 0.660) for the Charlson index, 0.665 (0.645– 0.681) for the Elixhauser index, and 0.787 (0.773– 0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices.Conclusion: In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.Keywords: comorbidity, multimorbidity, COVID-19, hospitalization, risk
format	article
author	Monterde D Carot-Sans G Cainzos-Achirica M Abilleira S Coca M Vela E Clèries M Valero-Bover D Comin-Colet J García-Eroles L Pérez-Sust P Arrufat M Lejardi Y Piera-Jiménez J
author_facet	Monterde D Carot-Sans G Cainzos-Achirica M Abilleira S Coca M Vela E Clèries M Valero-Bover D Comin-Colet J García-Eroles L Pérez-Sust P Arrufat M Lejardi Y Piera-Jiménez J
author_sort	Monterde D
title	Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients
title_short	Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients
title_full	Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients
title_fullStr	Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients
title_full_unstemmed	Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients
title_sort	performance of three measures of comorbidity in predicting critical covid-19: a retrospective analysis of 4607 hospitalized patients
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/2acbdc9c6dd5466fa603d33857bede8d
work_keys_str_mv	AT monterded performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT carotsansg performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT cainzosachiricam performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT abilleiras performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT cocam performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT velae performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT cleriesm performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT valeroboverd performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT comincoletj performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT garciaerolesl performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT perezsustp performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT arrufatm performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT lejardiy performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients AT pierajimenezj performanceofthreemeasuresofcomorbidityinpredictingcriticalcovid19aretrospectiveanalysisof4607hospitalizedpatients
_version_	1718416141403029504
spelling	oai:doaj.org-article:2acbdc9c6dd5466fa603d33857bede8d2021-11-23T18:43:00ZPerformance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients1179-1594https://doaj.org/article/2acbdc9c6dd5466fa603d33857bede8d2021-11-01T00:00:00Zhttps://www.dovepress.com/performance-of-three-measures-of-comorbidity-in-predicting-critical-co-peer-reviewed-fulltext-article-RMHPhttps://doaj.org/toc/1179-1594David Monterde,1,2 Gerard Carot-Sans,2,3 Miguel Cainzos-Achirica,4,5 Sònia Abilleira,1,6 Marc Coca,2,3 Emili Vela,2,3 Montse Clèries,2,3 Damià Valero-Bover,2,3 Josep Comin-Colet,7– 9 Luis García-Eroles,2,3 Pol Pérez-Sust,3 Miquel Arrufat,1 Yolanda Lejardi,1 Jordi Piera-Jiménez2,3,10 1Catalan Institute of Health, Barcelona, Spain; 2Digitalization for the Sustainability of the Healthcare System (DS3), Sistema de Salut de Catalunya, Barcelona, Spain; 3Servei Català de la Salut, Barcelona, Spain; 4Center for Outcomes Research, Houston Methodist, Houston, TX, USA; 5Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, MD, USA; 6CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; 7Department of Cardiology, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain; 8Bioheart-Cardiovascular Diseases Research Group (Idibell), L’Hospitalet de Llobregat, Barcelona, Spain; 9Department of Clinical Sciences, School of Medicine, Universität de Barcelona - UB, L’Hospitalet de Llobregat, Barcelona, Spain; 10Open Evidence Research Group, Universitat Oberta de Catalunya, Barcelona, SpainCorrespondence: Jordi Piera-JiménezServei Català de la Salut (CatSalut), Travessera de les Corts, 131-159 (Edifici Olímpia), Barcelona, 08028, SpainTel +34 634283110Email jpiera@catsalut.catBackground: Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients’ complexity.Purpose: To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity.Materials and Methods: We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC).Results: Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624– 0.660) for the Charlson index, 0.665 (0.645– 0.681) for the Elixhauser index, and 0.787 (0.773– 0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices.Conclusion: In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.Keywords: comorbidity, multimorbidity, COVID-19, hospitalization, riskMonterde DCarot-Sans GCainzos-Achirica MAbilleira SCoca MVela EClèries MValero-Bover DComin-Colet JGarcía-Eroles LPérez-Sust PArrufat MLejardi YPiera-Jiménez JDove Medical Pressarticlecomorbiditymultimorbiditycovid-19hospitalizationriskPublic aspects of medicineRA1-1270ENRisk Management and Healthcare Policy, Vol Volume 14, Pp 4729-4737 (2021)

Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients

Ejemplares similares