A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Arquivos Brasileiros de Cardiologia (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0066-782X2017000400304 |
Resumo: | Abstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD) based on variables assessed at admission to the coronary care unit (CCU) due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%), while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC) of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84) in the derivation sample and 0.86 (95%CI = 0.79 - 0.93) in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively). Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002) and validation (p = 0.039) samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001) and validation (p < 0.0015) samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain. |
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Arquivos Brasileiros de Cardiologia (Online) |
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A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and ValidationCoronaryArtery DiseaseMethodsChest PainModels StatisticalCoronary AngiographyTroponinElectrocardiographyAbstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD) based on variables assessed at admission to the coronary care unit (CCU) due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%), while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC) of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84) in the derivation sample and 0.86 (95%CI = 0.79 - 0.93) in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively). Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002) and validation (p = 0.039) samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001) and validation (p < 0.0015) samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain.Sociedade Brasileira de Cardiologia - SBC2017-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0066-782X2017000400304Arquivos Brasileiros de Cardiologia v.108 n.4 2017reponame:Arquivos Brasileiros de Cardiologia (Online)instname:Sociedade Brasileira de Cardiologia (SBC)instacron:SBC10.5935/abc.20170037info:eu-repo/semantics/openAccessCorreia,Luis Cláudio LemosCerqueira,MaurícioCarvalhal,ManuelaFerreira,FelipeGarcia,GuilhermeSilva,André Barcelos daSá,Nicole deLopes,FernandaBarcelos,Ana ClaraNoya-Rabelo,Márciaeng2017-05-12T00:00:00Zoai:scielo:S0066-782X2017000400304Revistahttp://www.arquivosonline.com.br/https://old.scielo.br/oai/scielo-oai.php||arquivos@cardiol.br1678-41700066-782Xopendoar:2017-05-12T00:00Arquivos Brasileiros de Cardiologia (Online) - Sociedade Brasileira de Cardiologia (SBC)false |
dc.title.none.fl_str_mv |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
title |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
spellingShingle |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation Correia,Luis Cláudio Lemos CoronaryArtery Disease Methods Chest Pain Models Statistical Coronary Angiography Troponin Electrocardiography |
title_short |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
title_full |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
title_fullStr |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
title_full_unstemmed |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
title_sort |
A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation |
author |
Correia,Luis Cláudio Lemos |
author_facet |
Correia,Luis Cláudio Lemos Cerqueira,Maurício Carvalhal,Manuela Ferreira,Felipe Garcia,Guilherme Silva,André Barcelos da Sá,Nicole de Lopes,Fernanda Barcelos,Ana Clara Noya-Rabelo,Márcia |
author_role |
author |
author2 |
Cerqueira,Maurício Carvalhal,Manuela Ferreira,Felipe Garcia,Guilherme Silva,André Barcelos da Sá,Nicole de Lopes,Fernanda Barcelos,Ana Clara Noya-Rabelo,Márcia |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Correia,Luis Cláudio Lemos Cerqueira,Maurício Carvalhal,Manuela Ferreira,Felipe Garcia,Guilherme Silva,André Barcelos da Sá,Nicole de Lopes,Fernanda Barcelos,Ana Clara Noya-Rabelo,Márcia |
dc.subject.por.fl_str_mv |
CoronaryArtery Disease Methods Chest Pain Models Statistical Coronary Angiography Troponin Electrocardiography |
topic |
CoronaryArtery Disease Methods Chest Pain Models Statistical Coronary Angiography Troponin Electrocardiography |
description |
Abstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD) based on variables assessed at admission to the coronary care unit (CCU) due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%), while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC) of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84) in the derivation sample and 0.86 (95%CI = 0.79 - 0.93) in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively). Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002) and validation (p = 0.039) samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001) and validation (p < 0.0015) samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0066-782X2017000400304 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0066-782X2017000400304 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/abc.20170037 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Cardiologia - SBC |
publisher.none.fl_str_mv |
Sociedade Brasileira de Cardiologia - SBC |
dc.source.none.fl_str_mv |
Arquivos Brasileiros de Cardiologia v.108 n.4 2017 reponame:Arquivos Brasileiros de Cardiologia (Online) instname:Sociedade Brasileira de Cardiologia (SBC) instacron:SBC |
instname_str |
Sociedade Brasileira de Cardiologia (SBC) |
instacron_str |
SBC |
institution |
SBC |
reponame_str |
Arquivos Brasileiros de Cardiologia (Online) |
collection |
Arquivos Brasileiros de Cardiologia (Online) |
repository.name.fl_str_mv |
Arquivos Brasileiros de Cardiologia (Online) - Sociedade Brasileira de Cardiologia (SBC) |
repository.mail.fl_str_mv |
||arquivos@cardiol.br |
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1752126567211859968 |