Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study
Objectives To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.Methods A retrospective study was conducted on primary AIHA cases who visited the Affiliated Hospital of Southwest Medical University and Xuyong C...
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Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2506482 |
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| author | Pan Li Chuanqi Zhong Xianjun Huang Zhi Cai Tianhong Guo |
| author_facet | Pan Li Chuanqi Zhong Xianjun Huang Zhi Cai Tianhong Guo |
| author_sort | Pan Li |
| collection | DOAJ |
| description | Objectives To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.Methods A retrospective study was conducted on primary AIHA cases who visited the Affiliated Hospital of Southwest Medical University and Xuyong County People’s Hospital from May 2017 to May 2022. Cases were categorized as relapsed or non-relapsed based on the 1-year outcomes. Twenty-two features were analyzed to identify relapse risk factors. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to establish a predictive model. The C-index, Calibration curves, ROC, and Decision curve analysis (DCA) were used to evaluate the discriminatory, corrective, accurate, and clinical effectiveness of the predictive model.Results A total of 232 cases of primary AIHA were included, and five potential variables including ‘DAT results’, ‘Hb’, ‘Multiline therapy’, ‘Complicating ITP’, and ‘Complicating infection’, have been screened for constructing a 1-year relapse risk prediction nomogram for primary AIHA. The nomogram has a C-index of 0.852 (95% CI: 0.797–0.907), confirmed by bootstrapping validation as 0.829. The area under the ROC was 0.846. The DCA shows that when the threshold probability is in the range of 1 ∼ 91%.Conclusions By following the current diagnostic and treatment criteria for AIHA in China, we retrospectively collect a multitude of medical records and analyze several relevant variables of AIHA, construct a predictive model by machine learning. Using this 1-year relapse risk nomogram can effectively predict the risk of relapse within 1 year after remission of primary AIHA. |
| format | Article |
| id | doaj-art-026b7bed4e124891a31610b99d6f13e8 |
| institution | OA Journals |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-026b7bed4e124891a31610b99d6f13e82025-08-20T01:55:38ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2025.2506482Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort studyPan Li0Chuanqi Zhong1Xianjun Huang2Zhi Cai3Tianhong Guo4Department of Oncology, Xuyong County People’s Hospital, Luzhou, Sichuan, ChinaClinical Laboratory, Luzhou Second People’s Hospital, Luzhou, Sichuan, ChinaDepartment of Transfusion, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, ChinaClinical Laboratory Diagnostics, the Southwest Medical University, Luzhou, Sichuan, ChinaDepartment of Transfusion, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, ChinaObjectives To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.Methods A retrospective study was conducted on primary AIHA cases who visited the Affiliated Hospital of Southwest Medical University and Xuyong County People’s Hospital from May 2017 to May 2022. Cases were categorized as relapsed or non-relapsed based on the 1-year outcomes. Twenty-two features were analyzed to identify relapse risk factors. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to establish a predictive model. The C-index, Calibration curves, ROC, and Decision curve analysis (DCA) were used to evaluate the discriminatory, corrective, accurate, and clinical effectiveness of the predictive model.Results A total of 232 cases of primary AIHA were included, and five potential variables including ‘DAT results’, ‘Hb’, ‘Multiline therapy’, ‘Complicating ITP’, and ‘Complicating infection’, have been screened for constructing a 1-year relapse risk prediction nomogram for primary AIHA. The nomogram has a C-index of 0.852 (95% CI: 0.797–0.907), confirmed by bootstrapping validation as 0.829. The area under the ROC was 0.846. The DCA shows that when the threshold probability is in the range of 1 ∼ 91%.Conclusions By following the current diagnostic and treatment criteria for AIHA in China, we retrospectively collect a multitude of medical records and analyze several relevant variables of AIHA, construct a predictive model by machine learning. Using this 1-year relapse risk nomogram can effectively predict the risk of relapse within 1 year after remission of primary AIHA.https://www.tandfonline.com/doi/10.1080/07853890.2025.2506482Autoimmune haemolytic anemiarelapsemachine learningnomograms |
| spellingShingle | Pan Li Chuanqi Zhong Xianjun Huang Zhi Cai Tianhong Guo Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study Annals of Medicine Autoimmune haemolytic anemia relapse machine learning nomograms |
| title | Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study |
| title_full | Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study |
| title_fullStr | Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study |
| title_full_unstemmed | Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study |
| title_short | Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study |
| title_sort | construction of a predictive model for relapse of primary autoimmune hemolytic anemia a retrospective cohort study |
| topic | Autoimmune haemolytic anemia relapse machine learning nomograms |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2025.2506482 |
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