Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy
BackgroundImmunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis ma...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1224631/full |
| _version_ | 1827877225777594368 |
|---|---|
| author | Lin-Lin Xu Di Zhang Di Zhang Di Zhang Hao-Yi Weng Hao-Yi Weng Hao-Yi Weng Li-Zhong Wang Li-Zhong Wang Li-Zhong Wang Ruo-Yan Chen Ruo-Yan Chen Ruo-Yan Chen Gang Chen Gang Chen Gang Chen Su-Fang Shi Li-Jun Liu Xu-Hui Zhong Shen-Da Hong Li-Xin Duan Ji-Cheng Lv Xu-Jie Zhou Hong Zhang |
| author_facet | Lin-Lin Xu Di Zhang Di Zhang Di Zhang Hao-Yi Weng Hao-Yi Weng Hao-Yi Weng Li-Zhong Wang Li-Zhong Wang Li-Zhong Wang Ruo-Yan Chen Ruo-Yan Chen Ruo-Yan Chen Gang Chen Gang Chen Gang Chen Su-Fang Shi Li-Jun Liu Xu-Hui Zhong Shen-Da Hong Li-Xin Duan Ji-Cheng Lv Xu-Jie Zhou Hong Zhang |
| author_sort | Lin-Lin Xu |
| collection | DOAJ |
| description | BackgroundImmunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy.MethodsA baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (Tpre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus Tbio), and clinical variables and Tpre (base model plus Tpre) were developed separately in 1,168 patients with regular follow-up to evaluate whether Tpre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using Tpre.ResultsThe features selected by AUCRF for the Tpre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the Tpre was 0.82 (95% CI: 0.80–0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75–0.97). When the Tbio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75–0.97) to 0.92 (95% CI: 0.85–0.98); P = 0.03]. There was no difference in AUC between the base model plus Tpre and the base model plus Tbio [0.90 (95% CI: 0.82–0.99) vs. 0.92 (95% CI: 0.85–0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using Tpre was 0.93 (95% CI: 0.87–0.99) in the external validation set.ConclusionA pathology T-score prediction (Tpre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores. |
| first_indexed | 2024-03-12T17:33:29Z |
| format | Article |
| id | doaj.art-3ef67e3f2090412ca28d09f174dacb13 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-12T17:33:29Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-3ef67e3f2090412ca28d09f174dacb132023-08-04T14:05:00ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-08-011410.3389/fimmu.2023.12246311224631Machine learning in predicting T-score in the Oxford classification system of IgA nephropathyLin-Lin Xu0Di Zhang1Di Zhang2Di Zhang3Hao-Yi Weng4Hao-Yi Weng5Hao-Yi Weng6Li-Zhong Wang7Li-Zhong Wang8Li-Zhong Wang9Ruo-Yan Chen10Ruo-Yan Chen11Ruo-Yan Chen12Gang Chen13Gang Chen14Gang Chen15Su-Fang Shi16Li-Jun Liu17Xu-Hui Zhong18Shen-Da Hong19Li-Xin Duan20Ji-Cheng Lv21Xu-Jie Zhou22Hong Zhang23Renal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, ChinaWeGene, Shenzhen Zaozhidao Technology, Shenzhen, ChinaShenzhen WeGene Clinical Laboratory, Shenzhen, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, ChinaWeGene, Shenzhen Zaozhidao Technology, Shenzhen, ChinaShenzhen WeGene Clinical Laboratory, Shenzhen, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, ChinaWeGene, Shenzhen Zaozhidao Technology, Shenzhen, ChinaShenzhen WeGene Clinical Laboratory, Shenzhen, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, ChinaWeGene, Shenzhen Zaozhidao Technology, Shenzhen, ChinaShenzhen WeGene Clinical Laboratory, Shenzhen, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, ChinaWeGene, Shenzhen Zaozhidao Technology, Shenzhen, ChinaShenzhen WeGene Clinical Laboratory, Shenzhen, ChinaRenal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, ChinaRenal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, ChinaDepartment of Pediatrics, Peking University First Hospital, Beijing, ChinaInstitute of Medical Technology, Health Science Center of Peking University, Beijing, ChinaThe Sichuan Provincial Key Laboratory for Human Disease Gene Study, Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaRenal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, ChinaRenal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, ChinaRenal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, ChinaBackgroundImmunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy.MethodsA baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (Tpre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus Tbio), and clinical variables and Tpre (base model plus Tpre) were developed separately in 1,168 patients with regular follow-up to evaluate whether Tpre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using Tpre.ResultsThe features selected by AUCRF for the Tpre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the Tpre was 0.82 (95% CI: 0.80–0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75–0.97). When the Tbio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75–0.97) to 0.92 (95% CI: 0.85–0.98); P = 0.03]. There was no difference in AUC between the base model plus Tpre and the base model plus Tbio [0.90 (95% CI: 0.82–0.99) vs. 0.92 (95% CI: 0.85–0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using Tpre was 0.93 (95% CI: 0.87–0.99) in the external validation set.ConclusionA pathology T-score prediction (Tpre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1224631/fullIgA nephropathymachine learningOxford classification systemprediction modelend-stage kidney disease |
| spellingShingle | Lin-Lin Xu Di Zhang Di Zhang Di Zhang Hao-Yi Weng Hao-Yi Weng Hao-Yi Weng Li-Zhong Wang Li-Zhong Wang Li-Zhong Wang Ruo-Yan Chen Ruo-Yan Chen Ruo-Yan Chen Gang Chen Gang Chen Gang Chen Su-Fang Shi Li-Jun Liu Xu-Hui Zhong Shen-Da Hong Li-Xin Duan Ji-Cheng Lv Xu-Jie Zhou Hong Zhang Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy Frontiers in Immunology IgA nephropathy machine learning Oxford classification system prediction model end-stage kidney disease |
| title | Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy |
| title_full | Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy |
| title_fullStr | Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy |
| title_full_unstemmed | Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy |
| title_short | Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy |
| title_sort | machine learning in predicting t score in the oxford classification system of iga nephropathy |
| topic | IgA nephropathy machine learning Oxford classification system prediction model end-stage kidney disease |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1224631/full |
| work_keys_str_mv | AT linlinxu machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT dizhang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT dizhang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT dizhang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT haoyiweng machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT haoyiweng machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT haoyiweng machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT lizhongwang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT lizhongwang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT lizhongwang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT ruoyanchen machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT ruoyanchen machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT ruoyanchen machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT gangchen machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT gangchen machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT gangchen machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT sufangshi machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT lijunliu machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT xuhuizhong machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT shendahong machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT lixinduan machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT jichenglv machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT xujiezhou machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy AT hongzhang machinelearninginpredictingtscoreintheoxfordclassificationsystemofiganephropathy |