Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis
Abstract Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug effica...
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-02-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-023-00757-3 |
| _version_ | 1797641560178819072 |
|---|---|
| author | Jing Xu Jiarui Ou Chen Li Zheng Zhu Jian Li Hailun Zhang Junchen Chen Bin Yi Wu Zhu Weiru Zhang Guanxiong Zhang Qian Gao Yehong Kuang Jiangning Song Xiang Chen Hong Liu |
| author_facet | Jing Xu Jiarui Ou Chen Li Zheng Zhu Jian Li Hailun Zhang Junchen Chen Bin Yi Wu Zhu Weiru Zhang Guanxiong Zhang Qian Gao Yehong Kuang Jiangning Song Xiang Chen Hong Liu |
| author_sort | Jing Xu |
| collection | DOAJ |
| description | Abstract Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients’ clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan–Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy. |
| first_indexed | 2024-03-11T13:47:25Z |
| format | Article |
| id | doaj.art-9342fc47b9e9481c939f7e824b67075d |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T13:47:25Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-9342fc47b9e9481c939f7e824b67075d2023-11-02T10:03:55ZengNature Portfolionpj Digital Medicine2398-63522023-02-016111110.1038/s41746-023-00757-3Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritisJing Xu0Jiarui Ou1Chen Li2Zheng Zhu3Jian Li4Hailun Zhang5Junchen Chen6Bin Yi7Wu Zhu8Weiru Zhang9Guanxiong Zhang10Qian Gao11Yehong Kuang12Jiangning Song13Xiang Chen14Hong Liu15Department of Dermatology, Xiangya Hospital, Central South UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityMonash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversityDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolMonash Biomedicine Discovery Institute and Department of Microbiology, Monash UniversityDepartment of Research and Development, Beijing GAP Biotechnology Co., LtdDepartment of Dermatology, Xiangya Hospital, Central South UniversityDepartment of Clinical Laboratory, Xiangya Hospital, Central South UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityDepartment of Rheumatology and Immunology, Xiangya Hospital, Central South UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityMonash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityDepartment of Dermatology, Xiangya Hospital, Central South UniversityAbstract Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients’ clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan–Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.https://doi.org/10.1038/s41746-023-00757-3 |
| spellingShingle | Jing Xu Jiarui Ou Chen Li Zheng Zhu Jian Li Hailun Zhang Junchen Chen Bin Yi Wu Zhu Weiru Zhang Guanxiong Zhang Qian Gao Yehong Kuang Jiangning Song Xiang Chen Hong Liu Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis npj Digital Medicine |
| title | Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis |
| title_full | Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis |
| title_fullStr | Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis |
| title_full_unstemmed | Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis |
| title_short | Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis |
| title_sort | multi modality data driven analysis of diagnosis and treatment of psoriatic arthritis |
| url | https://doi.org/10.1038/s41746-023-00757-3 |
| work_keys_str_mv | AT jingxu multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT jiaruiou multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT chenli multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT zhengzhu multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT jianli multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT hailunzhang multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT junchenchen multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT binyi multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT wuzhu multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT weiruzhang multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT guanxiongzhang multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT qiangao multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT yehongkuang multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT jiangningsong multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT xiangchen multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis AT hongliu multimodalitydatadrivenanalysisofdiagnosisandtreatmentofpsoriaticarthritis |