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Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population
Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to N...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691506/ https://www.ncbi.nlm.nih.gov/pubmed/33244062 http://dx.doi.org/10.1038/s41598-020-77693-9 |
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author | Xu, Qiang Chen, Hangjun Chen, Sihai Shan, Jing Xia, Guoming Cao, Zhiyou Liu, Xuqiang Dai, Min |
author_facet | Xu, Qiang Chen, Hangjun Chen, Sihai Shan, Jing Xia, Guoming Cao, Zhiyou Liu, Xuqiang Dai, Min |
author_sort | Xu, Qiang |
collection | PubMed |
description | Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to NONFH, but patients with alcohol- and steroid-related NONFH are not at all taken into account in this study. A training cohort of 790 patients (n = 434, NONFH; n = 356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination [area under the receiver operating characteristic curve (AUC) = 0.80] and calibration. Good discrimination (AUC = 0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis. |
format | Online Article Text |
id | pubmed-7691506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76915062020-11-27 Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population Xu, Qiang Chen, Hangjun Chen, Sihai Shan, Jing Xia, Guoming Cao, Zhiyou Liu, Xuqiang Dai, Min Sci Rep Article Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to NONFH, but patients with alcohol- and steroid-related NONFH are not at all taken into account in this study. A training cohort of 790 patients (n = 434, NONFH; n = 356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination [area under the receiver operating characteristic curve (AUC) = 0.80] and calibration. Good discrimination (AUC = 0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis. Nature Publishing Group UK 2020-11-26 /pmc/articles/PMC7691506/ /pubmed/33244062 http://dx.doi.org/10.1038/s41598-020-77693-9 Text en © The Author(s) 2020 Open Access This 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/. |
spellingShingle | Article Xu, Qiang Chen, Hangjun Chen, Sihai Shan, Jing Xia, Guoming Cao, Zhiyou Liu, Xuqiang Dai, Min Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population |
title | Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population |
title_full | Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population |
title_fullStr | Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population |
title_full_unstemmed | Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population |
title_short | Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population |
title_sort | development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in chinese population |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691506/ https://www.ncbi.nlm.nih.gov/pubmed/33244062 http://dx.doi.org/10.1038/s41598-020-77693-9 |
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