基于疾病状况和干预方法，为慢性肾脏病 3-5 期患者开发和验证预后预测模型：中国的回顾性队列研究。
Developing and validating a prognostic prediction model for patients with chronic kidney disease stages 3-5 based on disease conditions and intervention methods: a retrospective cohort study in China.
机构信息
Department of Nephrology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Chronic Disease Management Outpatient, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
出版信息
BMJ Open. 2022 May 30;12(5):e054989. doi: 10.1136/bmjopen-2021-054989.
OBJECTIVES
To develop and validate a nomogram model to predict chronic kidney disease (CKD) stages 3-5 prognosis.
DESIGN
A retrospective cohort study. We used univariate and multivariate Cox regression analysis to select the relevant predictors. To select the best model, we evaluated the prediction models' accuracy by concordance index (C-index), calibration curve, net reclassification index (NRI) and integrated discrimination improvement (IDI). We evaluated the clinical utility by decision curve analysis.
SETTING
Chronic Disease Management (CDM) Clinic in the Nephrology Department at the Guangdong Provincial Hospital of Chinese Medicine.
PARTICIPANTS
Patients with CKD stages 3-5 in the derivation and validation cohorts were 459 and 326, respectively.
PRIMARY OUTCOME MEASURE
Renal replacement therapy (haemodialysis, peritoneal dialysis, renal transplantation) or death.
RESULTS
We built four models. Age, estimated glomerular filtration rate and urine protein constituted the most basic model A. Haemoglobin, serum uric acid, cardiovascular disease, primary disease, CDM adherence and predictors in model A constituted model B. Oral medications and predictors in model A constituted model C. All the predictors constituted model D. Model B performed well in both discrimination and calibration (C-index: derivation cohort: 0.881, validation cohort: 0.886). Compared with model A, model B showed significant improvement in the net reclassification and integrated discrimination (model A vs model B: NRI: 1 year: 0.339 (-0.011 to 0.672) and 2 years: 0.314 (0.079 to 0.574); IDI: 1 year: 0.066 (0.010 to 0.127), p<0.001 and 2 years: 0.063 (0.008 to 0.106), p<0.001). There was no significant improvement between NRI and IDI among models B, C and D. Therefore, we selected model B as the optimal model.
CONCLUSIONS
We constructed a prediction model to predict the prognosis of patients with CKD stages 3-5 in the first and second year. Applying this model to clinical practice may guide clinical decision-making. Also, this model needs to be externally validated in the future.
TRIAL REGISTRATION NUMBER
ChiCTR1900024633 (http://www.chictr.org.cn).
目的
开发和验证一种列线图模型，以预测慢性肾脏病（CKD）3-5 期的预后。
设计
回顾性队列研究。我们使用单因素和多因素 Cox 回归分析来选择相关的预测因素。为了选择最佳模型，我们通过一致性指数（C 指数）、校准曲线、净重新分类指数（NRI）和综合判别改善（IDI）来评估预测模型的准确性。我们通过决策曲线分析评估了该模型的临床实用性。
地点
广东省中医院肾病科慢病管理（CDM）门诊。
参与者
分别纳入来自推导队列和验证队列的 CKD 3-5 期患者 459 例和 326 例。
主要结局指标
肾脏替代治疗（血液透析、腹膜透析、肾移植）或死亡。
结果
我们构建了 4 个模型。年龄、估计肾小球滤过率和尿蛋白构成了最基本的模型 A。血红蛋白、血尿酸、心血管疾病、原发病、CDM 依从性和模型 A 中的预测因素构成了模型 B。模型 A 中的口服药物和预测因素构成了模型 C。所有预测因素构成了模型 D。模型 B 在判别和校准方面表现良好（推导队列：C 指数 0.881，验证队列：0.886）。与模型 A 相比，模型 B 在净重新分类和综合判别方面有显著改善（模型 A 与模型 B：NRI：1 年：0.339（-0.011 至 0.672）和 2 年：0.314（0.079 至 0.574）；IDI：1 年：0.066（0.010 至 0.127），p<0.001 和 2 年：0.063（0.008 至 0.106），p<0.001）。在模型 B、C 和 D 之间，NRI 和 IDI 之间没有显著差异。因此，我们选择模型 B 作为最优模型。
结论
我们构建了一个预测模型，以预测 CKD 3-5 期患者在第 1 年和第 2 年的预后。将该模型应用于临床实践可能有助于指导临床决策。此外，该模型还需要在未来进行外部验证。
临床试验注册号
ChiCTR1900024633（http://www.chictr.org.cn）。
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