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A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study

BACKGROUND: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged L...

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Autores principales: Tan, Juntao, Zhang, Zhengyu, He, Yuxin, Yu, Yue, Zheng, Jing, Liu, Yunyu, Gong, Jun, Li, Jianjun, Wu, Xin, Zhang, Shengying, Lin, Xiantian, Zhao, Yuxi, Wu, Xiaoxin, Tang, Songjia, Chen, Jingjing, Zhao, Wenlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9903472/
https://www.ncbi.nlm.nih.gov/pubmed/36750951
http://dx.doi.org/10.1186/s12967-023-03959-1
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author Tan, Juntao
Zhang, Zhengyu
He, Yuxin
Yu, Yue
Zheng, Jing
Liu, Yunyu
Gong, Jun
Li, Jianjun
Wu, Xin
Zhang, Shengying
Lin, Xiantian
Zhao, Yuxi
Wu, Xiaoxin
Tang, Songjia
Chen, Jingjing
Zhao, Wenlong
author_facet Tan, Juntao
Zhang, Zhengyu
He, Yuxin
Yu, Yue
Zheng, Jing
Liu, Yunyu
Gong, Jun
Li, Jianjun
Wu, Xin
Zhang, Shengying
Lin, Xiantian
Zhao, Yuxi
Wu, Xiaoxin
Tang, Songjia
Chen, Jingjing
Zhao, Wenlong
author_sort Tan, Juntao
collection PubMed
description BACKGROUND: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799–0.808), 0.794 (95% CI 0.788–0.800), 0.754 (95% CI 0.739–0.770), and 0.743 (95% CI 0.722–0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface (https://cytjt007.shinyapps.io/prolonged_los/) was developed to provide convenient access for users. CONCLUSION: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-03959-1.
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spelling pubmed-99034722023-02-08 A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study Tan, Juntao Zhang, Zhengyu He, Yuxin Yu, Yue Zheng, Jing Liu, Yunyu Gong, Jun Li, Jianjun Wu, Xin Zhang, Shengying Lin, Xiantian Zhao, Yuxi Wu, Xiaoxin Tang, Songjia Chen, Jingjing Zhao, Wenlong J Transl Med Research BACKGROUND: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799–0.808), 0.794 (95% CI 0.788–0.800), 0.754 (95% CI 0.739–0.770), and 0.743 (95% CI 0.722–0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface (https://cytjt007.shinyapps.io/prolonged_los/) was developed to provide convenient access for users. CONCLUSION: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-03959-1. BioMed Central 2023-02-07 /pmc/articles/PMC9903472/ /pubmed/36750951 http://dx.doi.org/10.1186/s12967-023-03959-1 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
Tan, Juntao
Zhang, Zhengyu
He, Yuxin
Yu, Yue
Zheng, Jing
Liu, Yunyu
Gong, Jun
Li, Jianjun
Wu, Xin
Zhang, Shengying
Lin, Xiantian
Zhao, Yuxi
Wu, Xiaoxin
Tang, Songjia
Chen, Jingjing
Zhao, Wenlong
A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
title A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
title_full A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
title_fullStr A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
title_full_unstemmed A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
title_short A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
title_sort novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010–2022) multicenter retrospective case–control study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9903472/
https://www.ncbi.nlm.nih.gov/pubmed/36750951
http://dx.doi.org/10.1186/s12967-023-03959-1
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