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A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database

OBJECTIVE: Bone and bone marrow are the third most frequent sites of metastases from many cancers and are associated with low survival and high morbidity rates. Currently, there are no effective bedside tools to predict the morbidity risk of these patients in general intensive care units (ICUs). The...

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Autores principales: Miao, Guiqiang, Li, Zhaohui, Chen, Linjian, Li, Wenyong, Lan, Guobo, Chen, Qiyuan, Luo, Zhen, Liu, Ruijia, Zhao, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957308/
https://www.ncbi.nlm.nih.gov/pubmed/35345774
http://dx.doi.org/10.2147/IJGM.S352761
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author Miao, Guiqiang
Li, Zhaohui
Chen, Linjian
Li, Wenyong
Lan, Guobo
Chen, Qiyuan
Luo, Zhen
Liu, Ruijia
Zhao, Xiaodong
author_facet Miao, Guiqiang
Li, Zhaohui
Chen, Linjian
Li, Wenyong
Lan, Guobo
Chen, Qiyuan
Luo, Zhen
Liu, Ruijia
Zhao, Xiaodong
author_sort Miao, Guiqiang
collection PubMed
description OBJECTIVE: Bone and bone marrow are the third most frequent sites of metastases from many cancers and are associated with low survival and high morbidity rates. Currently, there are no effective bedside tools to predict the morbidity risk of these patients in general intensive care units (ICUs). The main objective of this study was to establish and validate a nomogram to predict the morbidity risk of patients with bone and bone marrow metastases. METHODS: Data on patients with bone and bone marrow metastases were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The patients were divided into training and validation cohorts. The data were analyzed using univariate and multivariate Cox regression methods. Factors significantly and independently prognostic of survival were used to construct a nomogram predicting 30-day morbidity. The nomogram was validated by various methods, including Harrell’s concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA). RESULTS: The study included 610 patients in the training cohort and 262 in the validation cohort. Multivariate Cox regression analysis showed that temperature, SpO(2), Sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), comorbidities with coagulopathy, white blood cell count, heart rate, and respiratory rate were independent predictors of patient survival. The resulting nomogram had good discriminative ability, as shown by high AUCs, and was well calibrated, as demonstrated by calibration curves. Improvements in NRI and IDI values suggested that the nomogram was superior to the SOFA scoring system. DCA curves revealed that the nomogram showed good value in clinical applications. CONCLUSION: This prognostic nomogram, based on demographic and laboratory parameters, was predictive of the 30-day morbidity rate in patients with secondary malignant neoplasms of the bone and bone marrow, suggesting its applicability in clinical practice.
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spelling pubmed-89573082022-03-27 A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database Miao, Guiqiang Li, Zhaohui Chen, Linjian Li, Wenyong Lan, Guobo Chen, Qiyuan Luo, Zhen Liu, Ruijia Zhao, Xiaodong Int J Gen Med Original Research OBJECTIVE: Bone and bone marrow are the third most frequent sites of metastases from many cancers and are associated with low survival and high morbidity rates. Currently, there are no effective bedside tools to predict the morbidity risk of these patients in general intensive care units (ICUs). The main objective of this study was to establish and validate a nomogram to predict the morbidity risk of patients with bone and bone marrow metastases. METHODS: Data on patients with bone and bone marrow metastases were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The patients were divided into training and validation cohorts. The data were analyzed using univariate and multivariate Cox regression methods. Factors significantly and independently prognostic of survival were used to construct a nomogram predicting 30-day morbidity. The nomogram was validated by various methods, including Harrell’s concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA). RESULTS: The study included 610 patients in the training cohort and 262 in the validation cohort. Multivariate Cox regression analysis showed that temperature, SpO(2), Sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), comorbidities with coagulopathy, white blood cell count, heart rate, and respiratory rate were independent predictors of patient survival. The resulting nomogram had good discriminative ability, as shown by high AUCs, and was well calibrated, as demonstrated by calibration curves. Improvements in NRI and IDI values suggested that the nomogram was superior to the SOFA scoring system. DCA curves revealed that the nomogram showed good value in clinical applications. CONCLUSION: This prognostic nomogram, based on demographic and laboratory parameters, was predictive of the 30-day morbidity rate in patients with secondary malignant neoplasms of the bone and bone marrow, suggesting its applicability in clinical practice. Dove 2022-03-22 /pmc/articles/PMC8957308/ /pubmed/35345774 http://dx.doi.org/10.2147/IJGM.S352761 Text en © 2022 Miao et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Miao, Guiqiang
Li, Zhaohui
Chen, Linjian
Li, Wenyong
Lan, Guobo
Chen, Qiyuan
Luo, Zhen
Liu, Ruijia
Zhao, Xiaodong
A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database
title A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database
title_full A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database
title_fullStr A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database
title_full_unstemmed A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database
title_short A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database
title_sort novel nomogram for predicting morbidity risk in patients with secondary malignant neoplasm of bone and bone marrow: an analysis based on the large mimic-iii clinical database
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957308/
https://www.ncbi.nlm.nih.gov/pubmed/35345774
http://dx.doi.org/10.2147/IJGM.S352761
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