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Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study

BACKGROUND: Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 h after lung transplantation with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after lung transplant remain unclear. The present study...

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Autores principales: Gao, Peigen, Li, Chongwu, Wu, Junqi, Zhang, Pei, Liu, Xiucheng, Li, Yuping, Ding, Junrong, Su, Yiliang, Zhu, Yuming, He, Wenxin, Ning, Ye, Chen, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832679/
https://www.ncbi.nlm.nih.gov/pubmed/36627599
http://dx.doi.org/10.1186/s12890-023-02307-9
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author Gao, Peigen
Li, Chongwu
Wu, Junqi
Zhang, Pei
Liu, Xiucheng
Li, Yuping
Ding, Junrong
Su, Yiliang
Zhu, Yuming
He, Wenxin
Ning, Ye
Chen, Chang
author_facet Gao, Peigen
Li, Chongwu
Wu, Junqi
Zhang, Pei
Liu, Xiucheng
Li, Yuping
Ding, Junrong
Su, Yiliang
Zhu, Yuming
He, Wenxin
Ning, Ye
Chen, Chang
author_sort Gao, Peigen
collection PubMed
description BACKGROUND: Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 h after lung transplantation with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after lung transplant remain unclear. The present study aimed to develop a novel scoring system to identify PMV after lung transplantation. METHODS: A total of 141 patients who underwent lung transplantation were investigated in this study. The patients were divided into PMV and non-prolonged ventilation (NPMV) groups. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was then established based on the multivariate analysis, and model performance was further examined regarding its calibration, discrimination, and clinical usefulness. RESULTS: Eight factors were finally identified to be significantly associated with PMV by the multivariate analysis and therefore were included as risk factors in the nomogram as follows: the body mass index (BMI, P = 0.036); primary diagnosis as idiopathic pulmonary fibrosis (IPF, P = 0.038); pulmonary hypertension (PAH, P = 0.034); primary graft dysfunction grading (PGD, P = 0.011) at T(0); cold ischemia time (CIT P = 0.012); and three ventilation parameters (peak inspiratory pressure [PIP, P < 0.001], dynamic compliance [Cdyn, P = 0.001], and P/F ratio [P = 0.015]) at T(0). The nomogram exhibited superior discrimination ability with an area under the curve of 0.895. Furthermore, both calibration curve and decision-curve analysis indicated satisfactory performance. CONCLUSION: A novel nomogram to predict individual risk of receiving PMV for patients after lung transplantation was established, which may guide preventative measures for tackling this adverse event. GRAPHIC ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02307-9.
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spelling pubmed-98326792023-01-12 Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study Gao, Peigen Li, Chongwu Wu, Junqi Zhang, Pei Liu, Xiucheng Li, Yuping Ding, Junrong Su, Yiliang Zhu, Yuming He, Wenxin Ning, Ye Chen, Chang BMC Pulm Med Research BACKGROUND: Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 h after lung transplantation with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after lung transplant remain unclear. The present study aimed to develop a novel scoring system to identify PMV after lung transplantation. METHODS: A total of 141 patients who underwent lung transplantation were investigated in this study. The patients were divided into PMV and non-prolonged ventilation (NPMV) groups. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was then established based on the multivariate analysis, and model performance was further examined regarding its calibration, discrimination, and clinical usefulness. RESULTS: Eight factors were finally identified to be significantly associated with PMV by the multivariate analysis and therefore were included as risk factors in the nomogram as follows: the body mass index (BMI, P = 0.036); primary diagnosis as idiopathic pulmonary fibrosis (IPF, P = 0.038); pulmonary hypertension (PAH, P = 0.034); primary graft dysfunction grading (PGD, P = 0.011) at T(0); cold ischemia time (CIT P = 0.012); and three ventilation parameters (peak inspiratory pressure [PIP, P < 0.001], dynamic compliance [Cdyn, P = 0.001], and P/F ratio [P = 0.015]) at T(0). The nomogram exhibited superior discrimination ability with an area under the curve of 0.895. Furthermore, both calibration curve and decision-curve analysis indicated satisfactory performance. CONCLUSION: A novel nomogram to predict individual risk of receiving PMV for patients after lung transplantation was established, which may guide preventative measures for tackling this adverse event. GRAPHIC ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02307-9. BioMed Central 2023-01-10 /pmc/articles/PMC9832679/ /pubmed/36627599 http://dx.doi.org/10.1186/s12890-023-02307-9 Text en © The Author(s) 2023 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
Gao, Peigen
Li, Chongwu
Wu, Junqi
Zhang, Pei
Liu, Xiucheng
Li, Yuping
Ding, Junrong
Su, Yiliang
Zhu, Yuming
He, Wenxin
Ning, Ye
Chen, Chang
Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
title Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
title_full Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
title_fullStr Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
title_full_unstemmed Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
title_short Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
title_sort establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832679/
https://www.ncbi.nlm.nih.gov/pubmed/36627599
http://dx.doi.org/10.1186/s12890-023-02307-9
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