Chen Kai-Chieh, Lee Shang-Yang, Tsai Dung-Jang, Ko Kai-Hsiung, Hsu Yi-Chih, Chang Wei-Chou, Fang Wen-Hui, Lin Chin, Hsu Yu-Juei
Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Min-Chun E. Rd., Sec. 6, Neihu 114, Taipei, Taiwan, Republic of China.
Military Digital Medical Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China.
J Imaging Inform Med. 2025 Apr 2. doi: 10.1007/s10278-025-01489-4.
Chronic kidney disease (CKD) remains a major public health concern, requiring better predictive models for early intervention. This study evaluates a deep learning model (DLM) that utilizes raw chest X-ray (CXR) data to predict moderate to severe kidney function decline. We analyzed data from 79,219 patients with an estimated Glomerular Filtration Rate (eGFR) between 65 and 120, segmented into development (n = 37,983), tuning (n = 15,346), internal validation (n = 14,113), and external validation (n = 11,777) sets. Our DLM, pretrained on CXR-report pairs, was fine-tuned with the development set. We retrospectively examined data spanning April 2011 to February 2022, with a 5-year maximum follow-up. Primary and secondary endpoints included CKD stage 3b progression, ESRD/dialysis, and mortality. The overall concordance index (C-index) values for the internal and external validation sets were 0.903 (95% CI, 0.885-0.922) and 0.851 (95% CI, 0.819-0.883), respectively. In these sets, the incidences of progression to CKD stage 3b at 5 years were 19.2% and 13.4% in the high-risk group, significantly higher than those in the median-risk (5.9% and 5.1%) and low-risk groups (0.9% and 0.9%), respectively. The sex, age, and eGFR-adjusted hazard ratios (HR) for the high-risk group compared to the low-risk group were 16.88 (95% CI, 10.84-26.28) and 7.77 (95% CI, 4.77-12.64), respectively. The high-risk group also exhibited higher probabilities of progressing to ESRD/dialysis or experiencing mortality compared to the low-risk group. Further analysis revealed that the high-risk group compared to the low/median-risk group had a higher prevalence of complications and abnormal blood/urine markers. Our findings demonstrate that a DLM utilizing CXR can effectively predict CKD stage 3b progression, offering a potential tool for early intervention in high-risk populations.
慢性肾脏病（CKD）仍然是一个主要的公共卫生问题，需要更好的预测模型以便进行早期干预。本研究评估了一种深度学习模型（DLM），该模型利用胸部X线（CXR）原始数据来预测中度至重度肾功能下降。我们分析了79219例估计肾小球滤过率（eGFR）在65至120之间的患者的数据，将其分为开发集（n = 37983）、调整集（n = 15346）、内部验证集（n = 14113）和外部验证集（n = 11777）。我们的DLM在CXR-报告对数据上进行预训练，并使用开发集进行微调。我们回顾性分析了2011年4月至2022年2月的数据，最长随访时间为5年。主要和次要终点包括CKD 3b期进展、终末期肾病/透析和死亡率。内部验证集和外部验证集的总体一致性指数（C指数）值分别为0.903（95%CI，0.885 - 0.922）和0.851（95%CI，0.819 - 0.883）。在这些数据集中，高危组5年进展至CKD 3b期的发生率分别为19.2%和13.4%，显著高于中危组（5.9%和5.1%）和低危组（0.9%和0.9%）。与低危组相比，高危组经性别、年龄和eGFR调整后的风险比（HR）分别为16.88（95%CI，10.84 - 26.28）和7.77（95%CI，4.77 - 12.64）。与低危组相比，高危组进展至终末期肾病/透析或死亡的概率也更高。进一步分析显示，与低/中危组相比，高危组并发症以及血液/尿液标志物异常的患病率更高。我们的研究结果表明，利用CXR的DLM能够有效预测CKD 3b期进展，为高危人群的早期干预提供了一种潜在工具。
