International urology and nephrology · 2025
Nomograms with arterial spin labeling for diagnosing early-stage chronic kidney disease.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ma Yongkang, Guo Ran, Wu Jiazhen, Xu Huihui, Zhang Chengxiu, Zhou Lingwei, Ye Xinlei, Wang Qian, Kuehn Bernd, Fu Caixia, Liu Mengxiao, Wen Qingqing, Mao Tingting, Yang Guang, Yang Shuohui
Аннотация
PURPOSE: Chronic kidney disease (CKD) is a global health issue, and early detection and intervention improve prognosis. We sought to construct diagnostic models and nomograms for early staging of CKD using renal blood flow (RBF) from magnetic resonance imaging (MRI) arterial spin labeling, combined with clinical and laboratory data.
METHODS: A total of 205 participants (training cohort: 124, internal test cohort: 32, and external test cohort: 49), including 48 healthy volunteers (HVs) and 157 CKD stage (S) 1-2 patients undergoing RBF MRI examination, were enrolled. Cortical and medullary RBF were measured, and clinical and laboratory data were recorded. Diagnostic models and nomograms were constructed for differentiating early-stage CKD (S1-2 and S1) patients from HVs using clinical, laboratory characteristics, and RBF values. Area under the curve (AUC), decision curve analysis (DCA), and calibration curve were employed to evaluate the performance, clinical utility, and predictive accuracy of the models.
RESULTS: AUCs for differentiating CKD S1-2 and S1 patients from HVs were 0.841 [95% confidence interval (CI) 0.704-0.978] and 0.900 (95% CI 0.739-1.000) in the internal test cohort, and 0.933 (95% CI 0.853-1.000) and 0.895 (95% CI 0.762-1.000) in the external test cohort. The calibration curve and DCA confirmed that nomograms of the combined models demonstrated good concordance between observed and predicted probabilities and better clinical benefit.
CONCLUSION: The combined model and nomogram built using MRI RBF, clinical, and laboratory data could distinguish patients in the early stages of CKD from healthy subjects.
METHODS: A total of 205 participants (training cohort: 124, internal test cohort: 32, and external test cohort: 49), including 48 healthy volunteers (HVs) and 157 CKD stage (S) 1-2 patients undergoing RBF MRI examination, were enrolled. Cortical and medullary RBF were measured, and clinical and laboratory data were recorded. Diagnostic models and nomograms were constructed for differentiating early-stage CKD (S1-2 and S1) patients from HVs using clinical, laboratory characteristics, and RBF values. Area under the curve (AUC), decision curve analysis (DCA), and calibration curve were employed to evaluate the performance, clinical utility, and predictive accuracy of the models.
RESULTS: AUCs for differentiating CKD S1-2 and S1 patients from HVs were 0.841 [95% confidence interval (CI) 0.704-0.978] and 0.900 (95% CI 0.739-1.000) in the internal test cohort, and 0.933 (95% CI 0.853-1.000) and 0.895 (95% CI 0.762-1.000) in the external test cohort. The calibration curve and DCA confirmed that nomograms of the combined models demonstrated good concordance between observed and predicted probabilities and better clinical benefit.
CONCLUSION: The combined model and nomogram built using MRI RBF, clinical, and laboratory data could distinguish patients in the early stages of CKD from healthy subjects.