Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915529540763648 |
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| author | Ma, Jingying Wang, Jinwei Lu, Lanlan Sun, Yexiang Feng, Mengling Zhang, Feifei Shen, Peng Jiang, Zhiqin Hong, Shenda Zhang, Luxia |
| author_facet | Ma, Jingying Wang, Jinwei Lu, Lanlan Sun, Yexiang Feng, Mengling Zhang, Feifei Shen, Peng Jiang, Zhiqin Hong, Shenda Zhang, Luxia |
| contents | Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. Most existing models are static and fail to capture temporal trends in disease progression, limiting their ability to inform timely interventions. We address this gap by developing a dynamic model that leverages common longitudinal clinical indicators from real-world Electronic Health Records (EHRs) for real-time kidney failure prediction.
Findings: A retrospective cohort of 4,587 patients from Yinzhou, China, was used for model development (2,752 patients for training, 917 patients for validation) and internal validation (918 patients), while external validation was conducted on a prospective PKUFH cohort (934 patients). The model demonstrated competitive performance across datasets, with an AUROC of 0.9311 (95%CI, 0.8873-0.9749) in the internal validation cohort and 0.8141 (95%CI, 0.7728-0.8554) in the external validation cohort, alongside progressively improving dynamic predictions, good calibration, and clinically consistent interpretability. KFDeep has been deployed on an open-access website and in primary care settings.
Interpretation: The KFDeep model enables dynamic prediction of kidney failure without increasing clinical examination costs. It has been integrated into existing hospital systems, providing physicians with a continuously updated decision-support tool in routine care. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_16388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation Ma, Jingying Wang, Jinwei Lu, Lanlan Sun, Yexiang Feng, Mengling Zhang, Feifei Shen, Peng Jiang, Zhiqin Hong, Shenda Zhang, Luxia Machine Learning Applications Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. Most existing models are static and fail to capture temporal trends in disease progression, limiting their ability to inform timely interventions. We address this gap by developing a dynamic model that leverages common longitudinal clinical indicators from real-world Electronic Health Records (EHRs) for real-time kidney failure prediction. Findings: A retrospective cohort of 4,587 patients from Yinzhou, China, was used for model development (2,752 patients for training, 917 patients for validation) and internal validation (918 patients), while external validation was conducted on a prospective PKUFH cohort (934 patients). The model demonstrated competitive performance across datasets, with an AUROC of 0.9311 (95%CI, 0.8873-0.9749) in the internal validation cohort and 0.8141 (95%CI, 0.7728-0.8554) in the external validation cohort, alongside progressively improving dynamic predictions, good calibration, and clinically consistent interpretability. KFDeep has been deployed on an open-access website and in primary care settings. Interpretation: The KFDeep model enables dynamic prediction of kidney failure without increasing clinical examination costs. It has been integrated into existing hospital systems, providing physicians with a continuously updated decision-support tool in routine care. |
| title | Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2501.16388 |