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Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study
BACKGROUND: Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667746/ https://www.ncbi.nlm.nih.gov/pubmed/34966600 http://dx.doi.org/10.7717/peerj.12626 |
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author | Zhong, Jiaojiao He, Yunan Ma, Jianchi Lu, Siyao Wu, Yushi Zhang, Junmin |
author_facet | Zhong, Jiaojiao He, Yunan Ma, Jianchi Lu, Siyao Wu, Yushi Zhang, Junmin |
author_sort | Zhong, Jiaojiao |
collection | PubMed |
description | BACKGROUND: Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related to malignancy in dermatomyositis patients has not been widely implemented in clinical practice. Herein, we established a predictive nomogram model for the diagnosis of dermatomyositis associated with malignancy. METHODS: We retrospectively analyzed 240 cases of dermatomyositis patients admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2002 to December 2019. According to the year of admission, the first 70% of the patients were used to establish a training cohort, and the remaining 30% were assigned to the validation cohort. Univariate analysis was performed on all variables, and statistically relevant variables were further included in a multivariate logistic regression analysis to screen for independent predictors. Finally, a nomogram was constructed based on these independent predictors. Bootstrap repeated sampling calculation C-index was used to evaluate the model’s calibration, and area under the curve (AUC) was used to evaluate the model discrimination ability. RESULTS: Multivariate logistic analysis showed that patients older than 50-year-old, dysphagia, refractory itching, and elevated creatine kinase were independent risk factors for dermatomyositis associated with malignancy, while interstitial lung disease was a protective factor. Based on this, we constructed a nomogram using the above-mentioned five factors. The C-index was 0.780 (95% CI [0.690–0.870]) in the training cohort and 0.756 (95% CI [0.618–0.893]) in the validation cohort, while the AUC value was 0.756 (95% CI [0.600–0.833]). Taken together, our nomogram showed good calibration and was effective in predicting which dermatomyositis patients were at a higher risk of developing malignant tumors. |
format | Online Article Text |
id | pubmed-8667746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86677462021-12-28 Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study Zhong, Jiaojiao He, Yunan Ma, Jianchi Lu, Siyao Wu, Yushi Zhang, Junmin PeerJ Dermatology BACKGROUND: Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related to malignancy in dermatomyositis patients has not been widely implemented in clinical practice. Herein, we established a predictive nomogram model for the diagnosis of dermatomyositis associated with malignancy. METHODS: We retrospectively analyzed 240 cases of dermatomyositis patients admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2002 to December 2019. According to the year of admission, the first 70% of the patients were used to establish a training cohort, and the remaining 30% were assigned to the validation cohort. Univariate analysis was performed on all variables, and statistically relevant variables were further included in a multivariate logistic regression analysis to screen for independent predictors. Finally, a nomogram was constructed based on these independent predictors. Bootstrap repeated sampling calculation C-index was used to evaluate the model’s calibration, and area under the curve (AUC) was used to evaluate the model discrimination ability. RESULTS: Multivariate logistic analysis showed that patients older than 50-year-old, dysphagia, refractory itching, and elevated creatine kinase were independent risk factors for dermatomyositis associated with malignancy, while interstitial lung disease was a protective factor. Based on this, we constructed a nomogram using the above-mentioned five factors. The C-index was 0.780 (95% CI [0.690–0.870]) in the training cohort and 0.756 (95% CI [0.618–0.893]) in the validation cohort, while the AUC value was 0.756 (95% CI [0.600–0.833]). Taken together, our nomogram showed good calibration and was effective in predicting which dermatomyositis patients were at a higher risk of developing malignant tumors. PeerJ Inc. 2021-12-09 /pmc/articles/PMC8667746/ /pubmed/34966600 http://dx.doi.org/10.7717/peerj.12626 Text en ©2021 Zhong et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Dermatology Zhong, Jiaojiao He, Yunan Ma, Jianchi Lu, Siyao Wu, Yushi Zhang, Junmin Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
title | Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
title_full | Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
title_fullStr | Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
title_full_unstemmed | Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
title_short | Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
title_sort | development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study |
topic | Dermatology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667746/ https://www.ncbi.nlm.nih.gov/pubmed/34966600 http://dx.doi.org/10.7717/peerj.12626 |
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