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Nomogram and risk calculator for severe hypoxemia after heart valve surgery

BACKGROUND: Hypoxemia is a very common issue in patients undergoing heart valve surgery (HVS), related to poor clinical outcomes. However, studies on severe hypoxemia (SH) after HVS have not been reported. The aims of this study were to identify predictors for SH in patients undergoing HVS and to de...

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Autores principales: Ding, Xiangchao, Cheng, Dan, Sun, Bing, Sun, Manda, Wu, Chuangyan, Chen, Jiuling, Li, Xiaoli, Lei, Yuan, Su, Yunshu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386119/
https://www.ncbi.nlm.nih.gov/pubmed/35990967
http://dx.doi.org/10.3389/fcvm.2022.972449
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author Ding, Xiangchao
Cheng, Dan
Sun, Bing
Sun, Manda
Wu, Chuangyan
Chen, Jiuling
Li, Xiaoli
Lei, Yuan
Su, Yunshu
author_facet Ding, Xiangchao
Cheng, Dan
Sun, Bing
Sun, Manda
Wu, Chuangyan
Chen, Jiuling
Li, Xiaoli
Lei, Yuan
Su, Yunshu
author_sort Ding, Xiangchao
collection PubMed
description BACKGROUND: Hypoxemia is a very common issue in patients undergoing heart valve surgery (HVS), related to poor clinical outcomes. However, studies on severe hypoxemia (SH) after HVS have not been reported. The aims of this study were to identify predictors for SH in patients undergoing HVS and to develop and validate a risk prediction model. METHODS: Patients undergoing HVS between 2016 and 2019 in a cardiovascular center were enrolled and were assigned to training and validation sets by a 7:3 ratio. Based on whether patients developed SH, they were divided into two groups. By univariate and multivariate analysis, predictors for SH were identified. Based on the predictors and logistic rule, a nomogram and a risk calculator were generated. The model was evaluated using calibration, discrimination and clinical utility. RESULTS: The incidence rates of SH, moderate hypoxemia and mild hypoxemia were respectively 2.4, 23.9, and 58.2%. By multivariate analysis, seven independent risk factors for SH after HVS were identified, including body mass index, chronic obstructive pulmonary disease, renal insufficiency, white blood cell count, serum globulin, cardiopulmonary bypass time, and surgical types. The logistic model demonstrated satisfactory discrimination, calibration and clinical utility in both the training and validation sets. A nomogram and a risk calculator based on the logistic model were generated for easy application. Risk stratification was performed and three risk intervals were defined according to the nomogram and clinical practice. In addition, compared to patients without SH, patients with SH had significantly poorer clinical outcomes. CONCLUSIONS: Postoperative hypoxemia was prevalent after HVS, related to poor clinical outcomes. A logistic model including seven independent predictors for SH after HVS were established and validated, which demonstrated satisfactory discrimination, calibration and clinical utility. The results of this study may provide help to individualized risk assessment, early prevention and perioperative management.
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spelling pubmed-93861192022-08-19 Nomogram and risk calculator for severe hypoxemia after heart valve surgery Ding, Xiangchao Cheng, Dan Sun, Bing Sun, Manda Wu, Chuangyan Chen, Jiuling Li, Xiaoli Lei, Yuan Su, Yunshu Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Hypoxemia is a very common issue in patients undergoing heart valve surgery (HVS), related to poor clinical outcomes. However, studies on severe hypoxemia (SH) after HVS have not been reported. The aims of this study were to identify predictors for SH in patients undergoing HVS and to develop and validate a risk prediction model. METHODS: Patients undergoing HVS between 2016 and 2019 in a cardiovascular center were enrolled and were assigned to training and validation sets by a 7:3 ratio. Based on whether patients developed SH, they were divided into two groups. By univariate and multivariate analysis, predictors for SH were identified. Based on the predictors and logistic rule, a nomogram and a risk calculator were generated. The model was evaluated using calibration, discrimination and clinical utility. RESULTS: The incidence rates of SH, moderate hypoxemia and mild hypoxemia were respectively 2.4, 23.9, and 58.2%. By multivariate analysis, seven independent risk factors for SH after HVS were identified, including body mass index, chronic obstructive pulmonary disease, renal insufficiency, white blood cell count, serum globulin, cardiopulmonary bypass time, and surgical types. The logistic model demonstrated satisfactory discrimination, calibration and clinical utility in both the training and validation sets. A nomogram and a risk calculator based on the logistic model were generated for easy application. Risk stratification was performed and three risk intervals were defined according to the nomogram and clinical practice. In addition, compared to patients without SH, patients with SH had significantly poorer clinical outcomes. CONCLUSIONS: Postoperative hypoxemia was prevalent after HVS, related to poor clinical outcomes. A logistic model including seven independent predictors for SH after HVS were established and validated, which demonstrated satisfactory discrimination, calibration and clinical utility. The results of this study may provide help to individualized risk assessment, early prevention and perioperative management. Frontiers Media S.A. 2022-08-04 /pmc/articles/PMC9386119/ /pubmed/35990967 http://dx.doi.org/10.3389/fcvm.2022.972449 Text en Copyright © 2022 Ding, Cheng, Sun, Sun, Wu, Chen, Li, Lei and Su. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Ding, Xiangchao
Cheng, Dan
Sun, Bing
Sun, Manda
Wu, Chuangyan
Chen, Jiuling
Li, Xiaoli
Lei, Yuan
Su, Yunshu
Nomogram and risk calculator for severe hypoxemia after heart valve surgery
title Nomogram and risk calculator for severe hypoxemia after heart valve surgery
title_full Nomogram and risk calculator for severe hypoxemia after heart valve surgery
title_fullStr Nomogram and risk calculator for severe hypoxemia after heart valve surgery
title_full_unstemmed Nomogram and risk calculator for severe hypoxemia after heart valve surgery
title_short Nomogram and risk calculator for severe hypoxemia after heart valve surgery
title_sort nomogram and risk calculator for severe hypoxemia after heart valve surgery
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386119/
https://www.ncbi.nlm.nih.gov/pubmed/35990967
http://dx.doi.org/10.3389/fcvm.2022.972449
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