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Yang P, Liu Z, Lu F, Sha Y, Li P, Zheng Q, Wang K, Zhou X, Zeng X, Wu Y. Machine learning models predicts risk of proliferative lupus nephritis. Front Immunol 2024; 15:1413569. [PMID: 38919623 PMCID: PMC11196753 DOI: 10.3389/fimmu.2024.1413569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Objective This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe. Methods This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers. Results Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN. Conclusion This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.
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Affiliation(s)
- Panyu Yang
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Jintang First People’s Hospital, Chengdu, China
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Zhongyu Liu
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Fenjian Lu
- Center for Reproductive Medicine, The Third People’s Hospital of Chengdu, Chengdu, China
| | - Yulin Sha
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Penghao Li
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Qu Zheng
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Kefen Wang
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Zhou
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoxi Zeng
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yongkang Wu
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Jintang First People’s Hospital, Chengdu, China
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Rousseau AJ, Geubbelmans M, Valkenborg D, Burzykowski T. Explainable artificial intelligence. Am J Orthod Dentofacial Orthop 2024; 165:491-494. [PMID: 38555171 DOI: 10.1016/j.ajodo.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 04/02/2024]
Affiliation(s)
- Axel-Jan Rousseau
- Center for Statistics, Hasselt University, Hasselt, Belgium; Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Melvin Geubbelmans
- Center for Statistics, Hasselt University, Hasselt, Belgium; Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Dirk Valkenborg
- Center for Statistics, Hasselt University, Hasselt, Belgium; Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Tomasz Burzykowski
- Center for Statistics, Hasselt University, Hasselt, Belgium; Data Science Institute, Hasselt University, Hasselt, Belgium; Department of Biostatistics and Medical Informatics, Medical University of Bialystok, Bialystok, Poland.
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Becker T, Geubbelmans M, Rousseau AJ, Valkenborg D, Burzykowski T. Boosting. Am J Orthod Dentofacial Orthop 2024; 165:122-124. [PMID: 38154850 DOI: 10.1016/j.ajodo.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 12/30/2023]
Affiliation(s)
| | | | | | | | - Tomasz Burzykowski
- Center for Statistics, Hasselt University, Belgium; Medical University of Białystok, Białystok, Poland.
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