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Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure
AIMS: Acute decompensated heart failure (ADHF) presents with pulmonary congestion, which is caused by an increased pulmonary arterial wedge pressure (PAWP). PAWP is strongly associated with prognosis, but its quantitative evaluation is often difficult. Our prior work demonstrated that a deep learnin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053257/ https://www.ncbi.nlm.nih.gov/pubmed/36583242 http://dx.doi.org/10.1002/ehf2.14282 |
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author | Saito, Yuki Omae, Yuto Mizobuchi, Saki Fujito, Hidesato Miyagawa, Masatsugu Kitano, Daisuke Toyama, Kazuto Fukamachi, Daisuke Toyotani, Jun Okumura, Yasuo |
author_facet | Saito, Yuki Omae, Yuto Mizobuchi, Saki Fujito, Hidesato Miyagawa, Masatsugu Kitano, Daisuke Toyama, Kazuto Fukamachi, Daisuke Toyotani, Jun Okumura, Yasuo |
author_sort | Saito, Yuki |
collection | PubMed |
description | AIMS: Acute decompensated heart failure (ADHF) presents with pulmonary congestion, which is caused by an increased pulmonary arterial wedge pressure (PAWP). PAWP is strongly associated with prognosis, but its quantitative evaluation is often difficult. Our prior work demonstrated that a deep learning approach based on chest radiographs can calculate estimated PAWP (ePAWP) in patients with cardiovascular disease. Therefore, the present study aimed to assess the prognostic value of ePAWP and compare it with other indices of haemodynamic congestion. METHODS AND RESULTS: We conducted a post hoc analysis of a single‐centre, prospective, observational heart failure registry and analysed data from 534 patients admitted for ADHF between January 2018 and December 2019. The deep learning approach was used to calculate ePAWP from chest radiographs at admission and discharge. Patients were divided into three groups based on the ePAWP tertiles at discharge, as follows: first tertile group (ePAWP ≤ 11.2 mm Hg, n = 178), second tertile group (11.2 < ePAWP < 13.5 mm Hg, n = 170), and third tertile group (ePAWP ≥ 13.5 mm Hg, n = 186). The third tertile group had a higher prevalence of atrial fibrillation and lower systolic blood pressure at admission; a lower platelet count and higher total bilirubin at both admission and discharge; and a higher left atrial diameter, peak early diastolic transmitral flow velocity, right ventricular end‐diastolic diameter, and maximal inferior vena cava diameter at discharge. During the median follow‐up period of 289 days, 223 (41.7%) patients reached the primary endpoint (a composite of all‐cause mortality or rehospitalization for heart failure). Kaplan–Meier analysis revealed a significantly higher composite event rate in the third tertile group (log‐rank test, P = 0.006). Even when adjusted for clinically relevant factors, a higher ePAWP at discharge and a smaller decrease in ePAWP from admission to discharge were significantly associated with higher event rates [ePAWP at discharge: hazard ratio, 1.10; 95% confidence interval (CI), 1.02–1.19; P = 0.010; and size of ePAWP decrease: hazard ratio, 0.94; 95% CI, 0.89–0.99; P = 0.038]. CONCLUSIONS: Our study suggests that ePAWP calculated by a deep learning approach may be useful for identifying and monitoring pulmonary congestion during hospitalization for ADHF. |
format | Online Article Text |
id | pubmed-10053257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100532572023-03-30 Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure Saito, Yuki Omae, Yuto Mizobuchi, Saki Fujito, Hidesato Miyagawa, Masatsugu Kitano, Daisuke Toyama, Kazuto Fukamachi, Daisuke Toyotani, Jun Okumura, Yasuo ESC Heart Fail Original Articles AIMS: Acute decompensated heart failure (ADHF) presents with pulmonary congestion, which is caused by an increased pulmonary arterial wedge pressure (PAWP). PAWP is strongly associated with prognosis, but its quantitative evaluation is often difficult. Our prior work demonstrated that a deep learning approach based on chest radiographs can calculate estimated PAWP (ePAWP) in patients with cardiovascular disease. Therefore, the present study aimed to assess the prognostic value of ePAWP and compare it with other indices of haemodynamic congestion. METHODS AND RESULTS: We conducted a post hoc analysis of a single‐centre, prospective, observational heart failure registry and analysed data from 534 patients admitted for ADHF between January 2018 and December 2019. The deep learning approach was used to calculate ePAWP from chest radiographs at admission and discharge. Patients were divided into three groups based on the ePAWP tertiles at discharge, as follows: first tertile group (ePAWP ≤ 11.2 mm Hg, n = 178), second tertile group (11.2 < ePAWP < 13.5 mm Hg, n = 170), and third tertile group (ePAWP ≥ 13.5 mm Hg, n = 186). The third tertile group had a higher prevalence of atrial fibrillation and lower systolic blood pressure at admission; a lower platelet count and higher total bilirubin at both admission and discharge; and a higher left atrial diameter, peak early diastolic transmitral flow velocity, right ventricular end‐diastolic diameter, and maximal inferior vena cava diameter at discharge. During the median follow‐up period of 289 days, 223 (41.7%) patients reached the primary endpoint (a composite of all‐cause mortality or rehospitalization for heart failure). Kaplan–Meier analysis revealed a significantly higher composite event rate in the third tertile group (log‐rank test, P = 0.006). Even when adjusted for clinically relevant factors, a higher ePAWP at discharge and a smaller decrease in ePAWP from admission to discharge were significantly associated with higher event rates [ePAWP at discharge: hazard ratio, 1.10; 95% confidence interval (CI), 1.02–1.19; P = 0.010; and size of ePAWP decrease: hazard ratio, 0.94; 95% CI, 0.89–0.99; P = 0.038]. CONCLUSIONS: Our study suggests that ePAWP calculated by a deep learning approach may be useful for identifying and monitoring pulmonary congestion during hospitalization for ADHF. John Wiley and Sons Inc. 2022-12-29 /pmc/articles/PMC10053257/ /pubmed/36583242 http://dx.doi.org/10.1002/ehf2.14282 Text en © 2022 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Saito, Yuki Omae, Yuto Mizobuchi, Saki Fujito, Hidesato Miyagawa, Masatsugu Kitano, Daisuke Toyama, Kazuto Fukamachi, Daisuke Toyotani, Jun Okumura, Yasuo Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
title | Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
title_full | Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
title_fullStr | Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
title_full_unstemmed | Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
title_short | Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
title_sort | prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053257/ https://www.ncbi.nlm.nih.gov/pubmed/36583242 http://dx.doi.org/10.1002/ehf2.14282 |
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