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Establishment of Biliary Atresia Prognostic Classification System via Survival-Based Forward Clustering - A New Biliary Atresia Classification.
Xu, Chen; Qin, Xing; Dai, Shuyang; Shen, Zhen; Yang, Yifan; Huang, Yanlei; Sun, Song; Zheng, Shan; Wu, Mengyun; Chen, Gong.
Afiliación
  • Xu C; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
  • Qin X; School of Statistics and Information, Shanghai University of International Business and Economics, 1900 Wenxiang Road, Shanghai, 201620, China.
  • Dai S; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
  • Shen Z; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
  • Yang Y; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
  • Huang Y; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
  • Sun S; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
  • Zheng S; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China. szheng@shmu.edu.cn.
  • Wu M; School of Statistics and Management, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai, 200433, China. wu.mengyun@mail.shufe.edu.cn.
  • Chen G; Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China. chengongzlp@hotmail.com.
Indian J Pediatr ; 2023 Dec 04.
Article en En | MEDLINE | ID: mdl-38047995
OBJECTIVES: To develop a machine learning algorithm with prognosis data to identify different clinical phenotypes of biliary atresia (BA) and provide instructions for choosing treatment schemes. METHODS: Six hundred thirty-nine cases of type III BA were retrospectively collected from the Children's Hospital of Fudan University from Jan 1st, 2017 to Dec 1st, 2019 as a training dataset, and a survival-based forward clustering method, which can also be used to predict the subtype of a new patient was developed to identify BA subtypes. RESULTS: A total of 2 clusters were identified (cluster 1 = 324 and cluster 2 = 315), where cluster 2 had a lower 2 y native liver survival post-Kasai rate. The infant patients in cluster 2 have higher weight, liver, and spleen volume, wider portal vein width, and older operative age; worse coagulation and liver function results; higher grade of liver fibrosis and detection rate of hepatic portal fibrous mass, and higher recent infection detection rate of herpes simplex virus type I. With the proposed prognostic classification system, the authors predicted the subtypes of the 187 cases of type III BA in a testing dataset collected from the whole year of 2020. The p-value computed from the log-rank testing for the Kaplan-Meier survival curves of the predicted two testing groups was 0.0113. CONCLUSIONS: This classification system would be a convenient tool to choose appropriate treatment and accelerate the choice-making between clinicians and infant patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Indian J Pediatr Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: India
Texto completo
Añadir a Mi BVS
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Indian J Pediatr Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: India