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Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study

INTRODUCTION: Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation in heart failure patients worsens their prognosis and leads to a substantial increase in treatment costs. There is...

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Autores principales: Li, Yide, Cai, Zhixiong, She, Yingfang, Shen, Wenjuan, Wang, Tinghuai, Luo, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724334/
https://www.ncbi.nlm.nih.gov/pubmed/36474152
http://dx.doi.org/10.1186/s12872-022-02973-3
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author Li, Yide
Cai, Zhixiong
She, Yingfang
Shen, Wenjuan
Wang, Tinghuai
Luo, Liang
author_facet Li, Yide
Cai, Zhixiong
She, Yingfang
Shen, Wenjuan
Wang, Tinghuai
Luo, Liang
author_sort Li, Yide
collection PubMed
description INTRODUCTION: Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation in heart failure patients worsens their prognosis and leads to a substantial increase in treatment costs. There is no tool that can effectively predict the onset of atrial fibrillation in patients with acute heart failure in the ICU currently. MATERIALS AND METHODS: We retrospectively analyzed the MIMIC-IV database of patients admitted to the intensive care unit (ICU) for acute heart failure and who were initially sinus rhythm. Data on demographics, comorbidities, laboratory findings, vital signs, and treatment were extracted. The cohort was divided into a training set and a validation set. Variables selected by LASSO regression and multivariate logistic regression in the training set were used to develop a model for predicting the occurrence of atrial fibrillation in acute heart failure in the ICU. A nomogram was drawn and an online calculator was developed. The discrimination and calibration of the model was evaluated. The performance of the model was tested using the validation set. RESULTS: This study included 2342 patients with acute heart failure, 646 of whom developed atrial fibrillation during their ICU stay. Using LASSO and multiple logistic regression, we selected six significant variables: age, prothrombin time, heart rate, use of vasoactive drugs within 24 h, Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology Score (APS) III. The C-index of the model was 0.700 (95% CI 0.672–0.727) and 0.682 (95% CI 0.639–0.725) in the training and validation sets, respectively. The calibration curves also performed well in both sets. CONCLUSION: We developed a simple and effective model for predicting atrial fibrillation in patients with acute heart failure in the ICU. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-022-02973-3.
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spelling pubmed-97243342022-12-07 Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study Li, Yide Cai, Zhixiong She, Yingfang Shen, Wenjuan Wang, Tinghuai Luo, Liang BMC Cardiovasc Disord Research INTRODUCTION: Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation in heart failure patients worsens their prognosis and leads to a substantial increase in treatment costs. There is no tool that can effectively predict the onset of atrial fibrillation in patients with acute heart failure in the ICU currently. MATERIALS AND METHODS: We retrospectively analyzed the MIMIC-IV database of patients admitted to the intensive care unit (ICU) for acute heart failure and who were initially sinus rhythm. Data on demographics, comorbidities, laboratory findings, vital signs, and treatment were extracted. The cohort was divided into a training set and a validation set. Variables selected by LASSO regression and multivariate logistic regression in the training set were used to develop a model for predicting the occurrence of atrial fibrillation in acute heart failure in the ICU. A nomogram was drawn and an online calculator was developed. The discrimination and calibration of the model was evaluated. The performance of the model was tested using the validation set. RESULTS: This study included 2342 patients with acute heart failure, 646 of whom developed atrial fibrillation during their ICU stay. Using LASSO and multiple logistic regression, we selected six significant variables: age, prothrombin time, heart rate, use of vasoactive drugs within 24 h, Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology Score (APS) III. The C-index of the model was 0.700 (95% CI 0.672–0.727) and 0.682 (95% CI 0.639–0.725) in the training and validation sets, respectively. The calibration curves also performed well in both sets. CONCLUSION: We developed a simple and effective model for predicting atrial fibrillation in patients with acute heart failure in the ICU. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-022-02973-3. BioMed Central 2022-12-06 /pmc/articles/PMC9724334/ /pubmed/36474152 http://dx.doi.org/10.1186/s12872-022-02973-3 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Yide
Cai, Zhixiong
She, Yingfang
Shen, Wenjuan
Wang, Tinghuai
Luo, Liang
Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study
title Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study
title_full Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study
title_fullStr Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study
title_full_unstemmed Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study
title_short Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: a retrospective cohort study
title_sort development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the icu: a retrospective cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724334/
https://www.ncbi.nlm.nih.gov/pubmed/36474152
http://dx.doi.org/10.1186/s12872-022-02973-3
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