Zhao Xianhui, Zheng Caiyun, Su Qitong, Lu Dongli, Wu Shiqin, Jiang Zhenghua, Wu Zhaochun
Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian, China.
Fuqing City Hospital Affiliated to Fujian Medical University, Nanping, Fujian, China.
BMC Nephrol. 2025 Sep 2;26(1):512. doi: 10.1186/s12882-025-04445-0.
Elevated serum phosphate levels are strongly associated with an increased risk of all-cause mortality in patients with chronic kidney disease (CKD). The aim of this study was to identify independent risk factors for hyperphosphatemia in patients with non-dialysis CKD and use the findings to develop and validate a predictive model for assessing hyperphosphatemia risk.
Data of patients with CKD discharged from the Department of Nephrology between January 2021 and December 2023 were retrospectively analyzed. Potential predictors were screened from an array of clinical variables using least absolute shrinkage and selection operator regression in conjunction with 10-fold cross-validation. A multivariate logistic regression model was constructed to identify independent risk factors for predicting hyperphosphatemia. The C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis were used to evaluate model predictive power, discriminability, accuracy, and clinical utility. Internal validation was implemented through a comparison of results from a validation set and the entire dataset.
This study included 216 patients, with 134 (62.04%) individuals who developed hyperphosphatemia. Logistic regression revealed that hemoglobin, blood urea nitrogen, serum creatinine, and parathyroid hormone were independently correlated with hyperphosphatemia. The nomogram C-index was 0.916 (95% confidence interval [CI]: 0.872-0.961). The model demonstrated excellent discriminative ability in the independent validation set (area under the curve [AUC] = 0.953, 95% CI: 0.909-0.998), with the full dataset analysis showing concordant results (AUC = 0.923, 95% CI: 0.889-0.958). The decision and clinical impact curves showed the clinical value of our nomogram for patients with CKD and hyperphosphatemia.
The nomogram model was highly accurate in identifying CKD subpopulations at an elevated risk of serum phosphorus metabolic disorders. Our model can be utilized for prospective monitoring and preventive intervention. Furthermore, through individualized risk assessments, the model can contribute to the development of customized treatment strategies that have the potential to markedly improve long-term prognosis.
血清磷水平升高与慢性肾脏病（CKD）患者全因死亡风险增加密切相关。本研究旨在确定非透析CKD患者高磷血症的独立危险因素，并利用研究结果开发和验证一个评估高磷血症风险的预测模型。
回顾性分析2021年1月至2023年12月从肾内科出院的CKD患者的数据。使用最小绝对收缩和选择算子回归结合10倍交叉验证，从一系列临床变量中筛选潜在预测因素。构建多变量逻辑回归模型以识别预测高磷血症的独立危险因素。使用C指数、受试者工作特征曲线、校准曲线和决策曲线分析来评估模型的预测能力、辨别力、准确性和临床实用性。通过比较验证集和整个数据集的结果进行内部验证。
本研究纳入216例患者，其中134例（62.04%）发生高磷血症。逻辑回归显示，血红蛋白、血尿素氮、血清肌酐和甲状旁腺激素与高磷血症独立相关。列线图C指数为0.916（95%置信区间[CI]：0.872 - 0.961）。该模型在独立验证集中显示出优异的辨别能力（曲线下面积[AUC]=0.953，95%CI：0.909 - 0.998），全数据集分析显示结果一致（AUC = 0.923，95%CI：0.889 - 0.958）。决策曲线和临床影响曲线显示了我们的列线图对CKD和高磷血症患者的临床价值。
列线图模型在识别血清磷代谢紊乱风险升高的CKD亚组方面具有高度准确性。我们的模型可用于前瞻性监测和预防性干预。此外，通过个性化风险评估，该模型有助于制定定制化治疗策略，有可能显著改善长期预后。
