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首页> 外文期刊>ClinicoEconomics and Outcomes Research >A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database
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A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database

机译：使用大型行政统一统一数据库进行慢性肾脏疾病进展预测模型

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Background:To create an appropriate chronic kidney disease (CKD) management program, we developed a predictive model to identify patients in a large administrative claims database with CKD stages 3 or 4 who were at high risk for progression to kidney failure.Methods:The predictive model was developed and validated utilizing a subset of patients with CKD stages 3 or 4 derived from a large Aetna claims database. The study spanned 36 months, comprised of a 12-month (2015) baseline period and a 24-month (2016-2017) prediction period. All patients were ≥18 years of age and continuously enrolled for 36 months. Multivariate logistic regression was used to develop models. Prediction model performance measures included area under the receiver operating characteristic curve (AUROC), calibration, and gain and lift charts.Results:Of the 74,114 patients identified as having CKD stages 3 or 4 during the baseline period, 2476 (3.3%) had incident kidney failure during the prediction period. The predictive model included the effect of numerous variables, including age, gender, CKD stage, hypertension (HTN), diabetes mellitus (DM), congestive heart failure, peripheral vascular disease, anemia, hyperkalemia (HK), prospective episode risk group score, and poor adherence to renin-angiotensin-aldosterone system inhibitors. The strongest predictors of progression to kidney failure were CKD stage (4 vs 3), HTN, DM, and HK. The ROC and calibration analyses in the validation sample demonstrated good predictive accuracy (AUROC=0.844) and calibration. The top two prediction deciles identified 70.8% of patients who progressed to kidney failure during the prediction period.Conclusion:This novel predictive model had good accuracy for identifying, from a large national database, patients with CKD who were at high risk of progressing to kidney failure within 2 years. Early identification using this model could potentially lead to improved health outcomes and reduced healthcare expenditures in this at-risk population.? 2021 Dai et al.
机译：背景：要创造适当的慢性肾病（CKD）管理计划，我们开发了一种预测模型，以识别大型行政索赔数据库中的患者，其中CKD阶段3或4患者对肾脏衰竭进行高风险。方法：预测性：预测性利用来自大型AETNA要求数据库的CKD阶段3或4的CKD阶段3或4的患者的患者开发和验证了模型。该研究截止了36个月，由12个月（2015年）基准期和24个月（2016-2017）预测期间组成。所有患者均≥18岁，不断注册36个月。多变量逻辑回归用于开发模型。预测模型性能措施包括接收器操作特征曲线（AUROC），校准和增益和提升图表中的区域预测期间的肾功能衰竭。预测模型包括许多变量的效果，包括年龄，性别，CKD阶段，高血压（HTN），糖尿病（HTN），充血性心力衰竭，外周血血管疾病，贫血，高钾血症（HK），预期发作风险群体得分，粘附不良，对肾素 - 血管紧张素 - 醛固酮系统抑制剂。进展的最强预测因子对肾功能衰竭是CKD阶段（4 Vs 3），HTN，DM和HK。验证样本中的ROC和校准分析显示出良好的预测精度（AUROC = 0.844）和校准。前两种预测减法鉴定了70.8％的患者在预测期间进展到肾脏发生故障。结论：这种新颖的预测模型从大型国家数据库识别CKD的患者识别较高的肾脏2年内失败。使用此模型的早期识别可能导致在风险群体中提高健康结果和减少医疗保健支出。 2021 Dai等人。

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