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Electrocardiographic predictors of peripartum cardiomyopathy
OBJECTIVE: To identify potential electrocardiographic predictors of peripartum cardiomyopathy (PPCM). METHODS: This was a case–control study carried out in three hospitals in Kano, Nigeria. Logistic regression models and a risk score were developed to determine electrocardiographic predictors of PPC...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Clinics Cardive Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928165/ https://www.ncbi.nlm.nih.gov/pubmed/27213852 http://dx.doi.org/10.5830/CVJA-2015-092 |
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author | Karaye, Kamilu M Karaye, Kamilu M Lindmark, Krister Henein, Michael Y Lindmark, Krister Henein, Michael Y |
author_facet | Karaye, Kamilu M Karaye, Kamilu M Lindmark, Krister Henein, Michael Y Lindmark, Krister Henein, Michael Y |
author_sort | Karaye, Kamilu M |
collection | PubMed |
description | OBJECTIVE: To identify potential electrocardiographic predictors of peripartum cardiomyopathy (PPCM). METHODS: This was a case–control study carried out in three hospitals in Kano, Nigeria. Logistic regression models and a risk score were developed to determine electrocardiographic predictors of PPCM. RESULTS: A total of 54 PPCM and 77 controls were consecutively recruited after satisfying the inclusion criteria. After controlling for confounding variables, a rise in heart rate of one beat/minute increased the risk of PPCM by 6.4% (p = 0.001), while the presence of ST–T-wave changes increased the odds of PPCM 12.06-fold (p < 0.001). In the patients, QRS duration modestly correlated (r = 0.4; p < 0.003) with left ventricular dimensions and end-systolic volume index, and was responsible for 19.9% of the variability of the latter (R(2) = 0.199; p = 0.003). A risk score of ≥ 2, developed by scoring 1 for each of the three ECG disturbances (tachycardia, ST–T-wave abnormalities and QRS duration), had a sensitivity of 85.2%, specificity of 64.9%, negative predictive value of 86.2% and area under the curve of 83.8% (p < 0.0001) for potentially predicting PPCM. CONCLUSION: In postpartum women, using the risk score could help to streamline the diagnosis of PPCM with significant accuracy, prior to confirmatory investigations |
format | Online Article Text |
id | pubmed-4928165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Clinics Cardive Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49281652016-07-13 Electrocardiographic predictors of peripartum cardiomyopathy Karaye, Kamilu M Karaye, Kamilu M Lindmark, Krister Henein, Michael Y Lindmark, Krister Henein, Michael Y Cardiovasc J Afr Cardiovascular Topics OBJECTIVE: To identify potential electrocardiographic predictors of peripartum cardiomyopathy (PPCM). METHODS: This was a case–control study carried out in three hospitals in Kano, Nigeria. Logistic regression models and a risk score were developed to determine electrocardiographic predictors of PPCM. RESULTS: A total of 54 PPCM and 77 controls were consecutively recruited after satisfying the inclusion criteria. After controlling for confounding variables, a rise in heart rate of one beat/minute increased the risk of PPCM by 6.4% (p = 0.001), while the presence of ST–T-wave changes increased the odds of PPCM 12.06-fold (p < 0.001). In the patients, QRS duration modestly correlated (r = 0.4; p < 0.003) with left ventricular dimensions and end-systolic volume index, and was responsible for 19.9% of the variability of the latter (R(2) = 0.199; p = 0.003). A risk score of ≥ 2, developed by scoring 1 for each of the three ECG disturbances (tachycardia, ST–T-wave abnormalities and QRS duration), had a sensitivity of 85.2%, specificity of 64.9%, negative predictive value of 86.2% and area under the curve of 83.8% (p < 0.0001) for potentially predicting PPCM. CONCLUSION: In postpartum women, using the risk score could help to streamline the diagnosis of PPCM with significant accuracy, prior to confirmatory investigations Clinics Cardive Publishing 2016 /pmc/articles/PMC4928165/ /pubmed/27213852 http://dx.doi.org/10.5830/CVJA-2015-092 Text en Copyright © 2015 Clinics Cardive Publishing http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Cardiovascular Topics Karaye, Kamilu M Karaye, Kamilu M Lindmark, Krister Henein, Michael Y Lindmark, Krister Henein, Michael Y Electrocardiographic predictors of peripartum cardiomyopathy |
title | Electrocardiographic predictors of peripartum cardiomyopathy |
title_full | Electrocardiographic predictors of peripartum cardiomyopathy |
title_fullStr | Electrocardiographic predictors of peripartum cardiomyopathy |
title_full_unstemmed | Electrocardiographic predictors of peripartum cardiomyopathy |
title_short | Electrocardiographic predictors of peripartum cardiomyopathy |
title_sort | electrocardiographic predictors of peripartum cardiomyopathy |
topic | Cardiovascular Topics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928165/ https://www.ncbi.nlm.nih.gov/pubmed/27213852 http://dx.doi.org/10.5830/CVJA-2015-092 |
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