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Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study
OBJECTIVE: To develop the first prediction model based on the common clinical symptoms of high-altitude pulmonary edema (HAPE), enabling early identification and an easy-to-execute self-risk prediction tool. METHODS: A total of 614 patients who consulted People’s Hospital of Tibet Autonomous Region...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626841/ https://www.ncbi.nlm.nih.gov/pubmed/37923352 http://dx.doi.org/10.1136/bmjopen-2023-074161 |
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author | Suona, Yangzong Gesang, Luobu Danzeng, Zhuoga Ci, Bai Zhaxi, Quzong Huang, Ju Zhang, Rui |
author_facet | Suona, Yangzong Gesang, Luobu Danzeng, Zhuoga Ci, Bai Zhaxi, Quzong Huang, Ju Zhang, Rui |
author_sort | Suona, Yangzong |
collection | PubMed |
description | OBJECTIVE: To develop the first prediction model based on the common clinical symptoms of high-altitude pulmonary edema (HAPE), enabling early identification and an easy-to-execute self-risk prediction tool. METHODS: A total of 614 patients who consulted People’s Hospital of Tibet Autonomous Region between January 2014 and April 2022 were enrolled. Out of those, 508 patients (416 males and 92 females) were diagnosed with HAPE and 106 were patients without HAPE (33 females and 72 males). They were randomly distributed into training (n=431) and validation (n=182) groups. Univariate and multivariate analysis were used to screen predictors of HAPE selected from the 36 predictors; nomograms were established based on the results of multivariate analysis. The receiver operating characteristic curve (ROC) was developed to obtain the area under the ROC curve (AUC) of the predictive model, and its predictive power was further evaluated by calibrating the curve, while the Decision Curve Analysis (DCA) was developed to evaluate the clinical applicability of the model, which was visualised by nomogram. RESULTS: All six predictors were significantly associated with the incidence of HAPE, and two models were classified according to whether the value of SpO(2) (percentage of oxygen in the blood) was available in the target population. Both could accurately predict the risk of HAPE. In the validation cohort, the AUC of model 1 was 0.934 with 95% CI (0.848 to 1.000), and model 2 had an AUC of 0.889, 95% CI (0.779 to 0.999). Calibration plots showed that the predicted and actual HAPE probabilities fitted well with internal validation, and the clinical decision curve shows intervention in the risk range of 0.01–0.98, resulting in a net benefit of nearly 99%. CONCLUSION: The recommended prediction model (nomogram) could estimate the risk of HAPE with good precision, high discrimination and possible clinical applications for patients with HAPE. More importantly, it is an easy-to-execute scoring tool for individuals without medical professionals’ support. |
format | Online Article Text |
id | pubmed-10626841 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-106268412023-11-07 Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study Suona, Yangzong Gesang, Luobu Danzeng, Zhuoga Ci, Bai Zhaxi, Quzong Huang, Ju Zhang, Rui BMJ Open Respiratory Medicine OBJECTIVE: To develop the first prediction model based on the common clinical symptoms of high-altitude pulmonary edema (HAPE), enabling early identification and an easy-to-execute self-risk prediction tool. METHODS: A total of 614 patients who consulted People’s Hospital of Tibet Autonomous Region between January 2014 and April 2022 were enrolled. Out of those, 508 patients (416 males and 92 females) were diagnosed with HAPE and 106 were patients without HAPE (33 females and 72 males). They were randomly distributed into training (n=431) and validation (n=182) groups. Univariate and multivariate analysis were used to screen predictors of HAPE selected from the 36 predictors; nomograms were established based on the results of multivariate analysis. The receiver operating characteristic curve (ROC) was developed to obtain the area under the ROC curve (AUC) of the predictive model, and its predictive power was further evaluated by calibrating the curve, while the Decision Curve Analysis (DCA) was developed to evaluate the clinical applicability of the model, which was visualised by nomogram. RESULTS: All six predictors were significantly associated with the incidence of HAPE, and two models were classified according to whether the value of SpO(2) (percentage of oxygen in the blood) was available in the target population. Both could accurately predict the risk of HAPE. In the validation cohort, the AUC of model 1 was 0.934 with 95% CI (0.848 to 1.000), and model 2 had an AUC of 0.889, 95% CI (0.779 to 0.999). Calibration plots showed that the predicted and actual HAPE probabilities fitted well with internal validation, and the clinical decision curve shows intervention in the risk range of 0.01–0.98, resulting in a net benefit of nearly 99%. CONCLUSION: The recommended prediction model (nomogram) could estimate the risk of HAPE with good precision, high discrimination and possible clinical applications for patients with HAPE. More importantly, it is an easy-to-execute scoring tool for individuals without medical professionals’ support. BMJ Publishing Group 2023-11-03 /pmc/articles/PMC10626841/ /pubmed/37923352 http://dx.doi.org/10.1136/bmjopen-2023-074161 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Respiratory Medicine Suona, Yangzong Gesang, Luobu Danzeng, Zhuoga Ci, Bai Zhaxi, Quzong Huang, Ju Zhang, Rui Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
title | Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
title_full | Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
title_fullStr | Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
title_full_unstemmed | Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
title_short | Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
title_sort | predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study |
topic | Respiratory Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10626841/ https://www.ncbi.nlm.nih.gov/pubmed/37923352 http://dx.doi.org/10.1136/bmjopen-2023-074161 |
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