ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

•The ABC2-SPH mortality risk score employs seven variables upon hospital presentation•The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 – 0.859)•External validation confirmed these results in Brazilian and Spanish cohorts•The ABC2-SPH outperformed other available scores•It is implem...

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Published in	International journal of infectious diseases Vol. 110; pp. 281 - 308
Main Authors	Pires, Magda C., Ramos, Lucas Emanuel F., Silva, Rafael T., Oliveira, Luana M., Mourato, Rodolfo Lucas S., Raventós, Berta, Anschau, Fernando, Chatkin, José Miguel, Nogueira, Matheus C.A., Guimarães-Júnior, Milton H., Vietta, Giovanna G., Duani, Helena, Ponce, Daniela, Ziegelmann, Patricia K., Castro, Luís C., Cimini, Christiane C.R., Francisco, Saionara C., Floriani, Maiara A., Nascimento, Guilherme F., Farace, Bárbara L., Monteiro, Luanna S., Souza-Silva, Maira V.R., Sales, Thais L.S., Martins, Karina Paula M.P., Borges do Nascimento, Israel J., Fereguetti, Tatiani O., Ferrara, Daniel T.M.O., Botoni, Fernando A., Etges, Ana Paula B.S., Schwarzbold, Alexandre V., Scotton, Ana Luiza B.A., Costa, André S.M., Glaeser, Andressa B., Madureira, Angélica Aparecida C., Bhering, Angelinda R., de Castro, Bruno Mateus, da Silva, Carla Thais C.A., Gomes, Caroline D., de Carvalho, Cíntia A., Silveira, Daniel V., Cezar, Edilson, Kroger, Emanuele Marianne S., Lucas, Fernanda B., Aranha, Fernando G., Bartolazzi, Frederico, Crestani, Gabriela P., Bastos, Gisele A.N., Madeira, Glícia Cristina de C., Noal, Helena Carolina, Vianna, Heloisa R., Guimarães, Henrique C., Molina, Israel, Batista, Joanna d'Arc L., de Alvarenga, Joice C., Guimarães, Júlia D.S.S., de Morais, Júlia D.P., Rugolo, Juliana M., Pontes, Karen Cristina J.R., Pinheiro, Lílian S., Pacheco, Liliane S., Sousa, Lucas de D., Couto, Luciana S.F., Kopittke, Luciane, de Moura, Luis Cesar S., Cabral, Máderson A.S., Souza, Maíra D., Carneiro, Marcelo, Ferreira, Maria Angélica P., Bicalho, Maria Aparecida C., Lima, Maria Clara P.B., Godoy, Mariana F., Figueiredo, Meire P., Sampaio, Natália C.S., Rangel, Natália L., Crespo, Natália T., de Oliveira, Neimy R., Assaf, Pedro L., Martelli, Petrônio José de L., Almeida, Rafaela S.C., Martins, Raphael C., Lutkmeier, Raquel, Valacio, Reginaldo Aparecido, Finger, Renan G., Cardoso, Ricardo B., Campos, Roberta X., Menezes, Rochele M., de Abreu, Roger M., Silva, Rufino de F., Guimarães, Silvana M.M., Araújo, Silvia F., Pereira, Susany Anastácia, Oliveira, Talita F., Kurtz, Tatiana, de Oliveira, Thainara C., Araújo, Thaíza Simônia M.A., Diniz, Thulio Henrique O., dos Santos, Veridiana B., Gomes, Virginia Mara R., Ramires, Yuri C., Polanczyk, Carisi A.
Format	Journal Article
Language	English
Published	Elsevier Ltd 01.09.2021 The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases Elsevier
Subjects	COVID-19 Hospitalizations Mortality Prognosis Risk factors SARS-CoV-2 Score COVID-19 SARS-CoV-2 Score Prognosis Hospitalizations Mortality Risk factors
Online Access	Get full text
ISSN	1201-9712 1878-3511 1878-3511
DOI	10.1016/j.ijid.2021.07.049

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Abstract	•The ABC2-SPH mortality risk score employs seven variables upon hospital presentation•The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 – 0.859)•External validation confirmed these results in Brazilian and Spanish cohorts•The ABC2-SPH outperformed other available scores•It is implemented in a freely available online risk calculator (https://abc2sph.com/) The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.
AbstractList	•The ABC2-SPH mortality risk score employs seven variables upon hospital presentation•The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 – 0.859)•External validation confirmed these results in Brazilian and Spanish cohorts•The ABC2-SPH outperformed other available scores•It is implemented in a freely available online risk calculator (https://abc2sph.com/) The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19. Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19. The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.OBJECTIVESThe majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.METHODSConsecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/).RESULTSMedian (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/).An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.CONCLUSIONSAn easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.
Author	Ramos, Carolina M. Bartolazzi, Frederico Bhering, Angelinda R. Pontes, Karen Cristina J.R. Almeida, Rafaela S.C. Vallt, Felipe B. Monteiro, Luanna S. Boersma, Eric Godoy, Mariana F. Lutkmeier, Raquel Costa, André S.M. Francisco, Saionara C. Madeira, Glícia Cristina de C. Mourato, Rodolfo Lucas S. Raventós, Berta Souza, Maíra D. Glaeser, Andressa B. Carvalho, Rafael L.R. de Oliveira, Thainara C. Botoni, Fernando A. de Oliveira, Neimy R. Fereguetti, Tatiani O. Ramires, Yuri C. Santos, Luisa Elem A. Borges do Nascimento, Israel J. de Carvalho, Cíntia A. Kopittke, Luciane Valacio, Reginaldo Aparecido Sousa, Lucas de D. Pires, Magda C. Pinheiro, Lílian S. de Moura, Luis Cesar S. Weber, André P. Ferreira, Maria Angélica P. Bicalho, Maria Aparecida C. Rugolo, Juliana M. Ponce, Daniela Lima, Maria Clara P.B. Silveira, Daniel V. Guimarães, Silvana M.M. Araújo, Thaíza Simônia M.A. Couto, Luciana S.F. Guimarães, Júlia D.S.S. Madureira, Angélica Aparecida C. de Morais, Júlia D.P. Oliveira, Luana M. do Vale, Vitor Augusto L. Rangel, Natália L. F
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Santa Maria, Brazil – sequence: 75 givenname: Luciane surname: Kopittke fullname: Kopittke, Luciane email: kluciane@ghc.com.br organization: Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Grupo Hospitalar Conceição. Porto Alegre, Brazil – sequence: 76 givenname: Luis Cesar S. surname: de Moura fullname: de Moura, Luis Cesar S. email: luis.moura@tacchini.com.br organization: Hospital Tacchini. Bento Gonçalves, Brazil – sequence: 78 givenname: Máderson A.S. surname: Cabral fullname: Cabral, Máderson A.S. email: madersonalvares@hotmail.com organization: Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil – sequence: 79 givenname: Maíra D. surname: Souza fullname: Souza, Maíra D. email: mairadiassouza@gmail.com organization: Hospital Metropolitano Odilon Behrens. Belo Horizonte, Brazil – sequence: 81 givenname: Marcelo surname: Carneiro fullname: Carneiro, Marcelo email: marceloc@unisc.br organization: Hospital Santa Cruz. Santa Cruz do Sul, Brazil – sequence: 82 givenname: Maria Angélica P. surname: Ferreira fullname: Ferreira, Maria Angélica P. email: mpiferreira@hcpa.edu.br organization: Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul. Porto Alegre, Brazil – sequence: 83 givenname: Maria Aparecida C. surname: Bicalho fullname: Bicalho, Maria Aparecida C. email: macbicalho@gmail.com organization: Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil – sequence: 84 givenname: Maria Clara P.B. surname: Lima fullname: Lima, Maria Clara P.B. email: mariaclarapontellobl@gmail.com organization: Universidade Federal de Ouro Preto. Ouro Preto, Brazil – sequence: 85 givenname: Mariana F. surname: Godoy fullname: Godoy, Mariana F. email: mfdegodoy@gmail.com organization: Hospital São Lucas PUCRS, Porto Alegre, Brazil – sequence: 87 givenname: Meire P. surname: Figueiredo fullname: Figueiredo, Meire P. email: mpfenf@yahoo.com.br organization: Hospital Santo Antônio. Curvelo, Brazil – sequence: 88 givenname: Natália C.S. surname: Sampaio fullname: Sampaio, Natália C.S. email: natsamster@gmail.com organization: Hospital Eduardo de Menezes. Belo Horizonte. Brazil – sequence: 89 givenname: Natália L. surname: Rangel fullname: Rangel, Natália L. email: netx_11@hotmail.com organization: Hospital Márcio Cunha. Ipatinga, Brasil – sequence: 90 givenname: Natália T. surname: Crespo fullname: Crespo, Natália T. email: nat_trifiletti@hotmail.com organization: Hospital Márcio Cunha. Ipatinga, Brasil – sequence: 91 givenname: Neimy R. surname: de Oliveira fullname: de Oliveira, Neimy R. email: neimyramos@gmail.com organization: Hospital Eduardo de Menezes. Belo Horizonte. Brazil – sequence: 92 givenname: Pedro L. surname: Assaf fullname: Assaf, Pedro L. email: pedro.ledic@hmdcc.com.br organization: Hospital Metropolitano Doutor Célio de Castro. Belo Horizonte, Brazil – sequence: 93 givenname: Petrônio José de L. surname: Martelli fullname: Martelli, Petrônio José de L. email: petroniocarla@uol.com.br organization: Hospital das Clínicas da Universidade Federal de Pernambuco. Recife, Brazil – sequence: 94 givenname: Rafaela S.C. surname: Almeida fullname: Almeida, Rafaela S.C. email: rafacharao@gmail.com organization: Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Grupo Hospitalar Conceição. Porto Alegre, Brazil – sequence: 95 givenname: Raphael C. surname: Martins fullname: Martins, Raphael C. email: rapahaelcm@gmail.com organization: Universidade Federal do Rio Grande do Sul. Porto Alegre, Brazil – sequence: 96 givenname: Raquel surname: Lutkmeier fullname: Lutkmeier, Raquel email: raquellutk83@gmail.com organization: Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Grupo Hospitalar Conceição. Porto Alegre, Brazil – sequence: 97 givenname: Reginaldo Aparecido surname: Valacio fullname: Valacio, Reginaldo Aparecido email: ravalacio@hotmail.com organization: Hospital Metropolitano Odilon Behrens. Belo Horizonte, Brazil – sequence: 98 givenname: Renan G. surname: Finger fullname: Finger, Renan G. email: renanfinger@yahoo.com.br organization: Hospital Regional do Oeste. Chapecó, Brazil – sequence: 99 givenname: Ricardo B. surname: Cardoso fullname: Cardoso, Ricardo B. email: ricardobcardoso@gmail.com organization: Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 \| Sala 507, Porto Alegre, Brazil – sequence: 101 givenname: Roberta X. surname: Campos fullname: Campos, Roberta X. email: xaviercampos.roberta@gmail.com organization: Pontifícia Universidade Católica de Minas Gerais. Belo Horizonte, Brazil – sequence: 102 givenname: Rochele M. surname: Menezes fullname: Menezes, Rochele M. email: rochelemenezes@unisc.br organization: Hospital Santa Cruz. Santa Cruz do Sul, Brazil – sequence: 103 givenname: Roger M. surname: de Abreu fullname: de Abreu, Roger M. email: roger.abreu@hmdcc.com.br organization: Hospital Metropolitano Doutor Célio de Castro. Belo Horizonte, Brazil – sequence: 104 givenname: Rufino de F. surname: Silva fullname: Silva, Rufino de F. email: rufino@ufsj.edu.br organization: Universidade Federal de São João del-Rei. Divinópolis, Brazil – sequence: 105 givenname: Silvana M.M. surname: Guimarães fullname: Guimarães, Silvana M.M. email: smangeon@gmail.com organization: Hospital Semper. Belo Horizonte, Brazil – sequence: 106 givenname: Silvia F. surname: Araújo fullname: Araújo, Silvia F. email: silviaferreiragastro@gmail.com organization: Hospital Semper. Belo Horizonte, Brazil – sequence: 107 givenname: Susany Anastácia surname: Pereira fullname: Pereira, Susany Anastácia email: susany2808@gmail.com organization: Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil – sequence: 108 givenname: Talita F. surname: Oliveira fullname: Oliveira, Talita F. email: talitafischeroliveira@gmail.com organization: Hospital Metropolitano Odilon Behrens. Belo Horizonte, Brazil – sequence: 109 givenname: Tatiana surname: Kurtz fullname: Kurtz, Tatiana email: kurtz@unisc.br organization: Hospital Santa Cruz. Santa Cruz do Sul, Brazil – sequence: 110 givenname: Thainara C. surname: de Oliveira fullname: de Oliveira, Thainara C. email: thainarastaehler@hotmail.com organization: Hospital Universitário de Canoas. Canoas, Brazil – sequence: 111 givenname: Thaíza Simônia M.A. surname: Araújo fullname: Araújo, Thaíza Simônia M.A. email: thaizaalbino16@gmail.com organization: Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil – sequence: 112 givenname: Thulio Henrique O. surname: Diniz fullname: Diniz, Thulio Henrique O. email: thuliodinizmed@gmail.com organization: Departamento de Medicina Interna, Universidade Federal de Santa Maria. Santa Maria, Brazil – sequence: 113 givenname: Veridiana B. surname: dos Santos fullname: dos Santos, Veridiana B. email: veridbaldon@hotmail.com organization: Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Grupo Hospitalar Conceição. Porto Alegre, Brazil – sequence: 114 givenname: Virginia Mara R. surname: Gomes fullname: Gomes, Virginia Mara R. email: vgvirginiagomes@gmail.com organization: Centro Universitário de Belo Horizonte (UniBH). Belo Horizonte, Brazil – sequence: 116 givenname: Yuri C. surname: Ramires fullname: Ramires, Yuri C. email: yuri.ramires@gmail.com organization: Hospital Bruno Born. Lajeado, Brazil – sequence: 118 givenname: Carisi A. surname: Polanczyk fullname: Polanczyk, Carisi A. email: carisi.anne@gmail.com organization: Institute for Health Technology Assessment (IATS/ CNPq). Rua Ramiro Barcelos, 2359. Prédio 21 \| Sala 507, Porto Alegre, Brazil
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Snippet	•The ABC2-SPH mortality risk score employs seven variables upon hospital presentation•The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 –... The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias.... Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high...
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SubjectTerms	COVID-19 Hospitalizations Mortality Prognosis Risk factors SARS-CoV-2 Score
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Title	ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores
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