- 著者
-
Hiroyuki Yamamoto
Kyoko Yamamoto
Katsumi Yoshida
Chiyohiko Shindoh
Kyoko Takeda
Masami Monden
Hiroko Izumo
Hiroyuki Niinuma
Yutaro Nishi
Koichiro Niwa
Yasuhiro Komatsu
- 出版者
- Tohoku University Medical Press
- 雑誌
- The Tohoku Journal of Experimental Medicine (ISSN:00408727)
- 巻号頁・発行日
- vol.237, no.3, pp.201-207, 2015 (Released:2015-10-24)
- 参考文献数
- 24
- 被引用文献数
-
2
2
Chronic kidney disease (CKD) is a global public health issue, and strategies for its early detection and intervention are imperative. The latest Japanese CKD guideline recommends that patients without diabetes should be classified using the urine protein-to-creatinine ratio (PCR) instead of the urine albumin-to-creatinine ratio (ACR); however, no validation studies are available. This study aimed to validate the PCR-based CKD risk classification compared with the ACR-based classification and to explore more accurate classification methods. We analyzed two previously reported datasets that included diabetic and/or cardiovascular patients who were classified into early CKD stages. In total, 860 patients (131 diabetic patients and 729 cardiovascular patients, including 193 diabetic patients) were enrolled. We assessed the CKD risk classification of each patient according to the estimated glomerular filtration rate and the ACR-based or PCR-based classification. The use of the cut-off value recommended in the current guideline (PCR 0.15 g/g creatinine) resulted in risk misclassification rates of 26.0% and 16.6% for the two datasets. The misclassification was primarily caused by underestimation. Moderate to substantial agreement between each classification was achieved: Cohen’s kappa, 0.56 (95% confidence interval, 0.45-0.69) and 0.72 (0.67-0.76) in each dataset, respectively. To improve the accuracy, we tested various candidate PCR cut-off values, showing that a PCR cut-off value of 0.08-0.10 g/g creatinine resulted in improvement in the misclassification rates and kappa values. Modification of the PCR cut-off value would improve its efficacy to identify high-risk populations who will benefit from early intervention.