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Predicting the risks of kidney failure and death in adults with moderate to severe chronic kidney disease: multinational, longitudinal, population based, cohort study
The BMJ ( IF 93.6 ) Pub Date : 2024-04-15 , DOI: 10.1136/bmj-2023-078063
Ping Liu 1 , Simon Sawhney 2 , Uffe Heide-Jørgensen 3 , Robert Ross Quinn 1 , Simon Kok Jensen 3 , Andrew Mclean 2 , Christian Fynbo Christiansen 3 , Thomas Alexander Gerds 4 , Pietro Ravani 5
Affiliation  

Objective To train and test a super learner strategy for risk prediction of kidney failure and mortality in people with incident moderate to severe chronic kidney disease (stage G3b to G4). Design Multinational, longitudinal, population based, cohort study. Settings Linked population health data from Canada (training and temporal testing), and Denmark and Scotland (geographical testing). Participants People with newly recorded chronic kidney disease at stage G3b-G4, estimated glomerular filtration rate (eGFR) 15-44 mL/min/1.73 m2. Modelling The super learner algorithm selected the best performing regression models or machine learning algorithms (learners) based on their ability to predict kidney failure and mortality with minimised cross-validated prediction error (Brier score, the lower the better). Prespecified learners included age, sex, eGFR, albuminuria, with or without diabetes, and cardiovascular disease. The index of prediction accuracy, a measure of calibration and discrimination calculated from the Brier score (the higher the better) was used to compare KDpredict with the benchmark, kidney failure risk equation, which does not account for the competing risk of death, and to evaluate the performance of KDpredict mortality models. Results 67 942 Canadians, 17 528 Danish, and 7740 Scottish residents with chronic kidney disease at stage G3b to G4 were included (median age 77-80 years; median eGFR 39 mL/min/1.73 m2). Median follow-up times were five to six years in all cohorts. Rates were 0.8-1.1 per 100 person years for kidney failure and 10-12 per 100 person years for death. KDpredict was more accurate than kidney failure risk equation in prediction of kidney failure risk: five year index of prediction accuracy 27.8% (95% confidence interval 25.2% to 30.6%) versus 18.1% (15.7% to 20.4%) in Denmark and 30.5% (27.8% to 33.5%) versus 14.2% (12.0% to 16.5%) in Scotland. Predictions from kidney failure risk equation and KDpredict differed substantially, potentially leading to diverging treatment decisions. An 80-year-old man with an eGFR of 30 mL/min/1.73 m2 and an albumin-to-creatinine ratio of 100 mg/g (11 mg/mmol) would receive a five year kidney failure risk prediction of 10% from kidney failure risk equation (above the current nephrology referral threshold of 5%). The same man would receive five year risk predictions of 2% for kidney failure and 57% for mortality from KDpredict. Individual risk predictions from KDpredict with four or six variables were accurate for both outcomes. The KDpredict models retrained using older data provided accurate predictions when tested in temporally distinct, more recent data. Conclusions KDpredict could be incorporated into electronic medical records or accessed online to accurately predict the risks of kidney failure and death in people with moderate to severe CKD. The KDpredict learning strategy is designed to be adapted to local needs and regularly revised over time to account for changes in the underlying health system and care processes. We are not able to make our dataset available to other researchers due to our contractual arrangements with the provincial health ministry (Alberta Health), who are the data custodians. Researchers may make requests to obtain a similar dataset at .

中文翻译:


预测患有中度至重度慢性肾病的成人的肾衰竭和死亡风险:跨国、纵向、基于人群的队列研究



目的 训练和测试超级学习器策略,用于预测中度至重度慢性肾病(G3b 至 G4 期)患者的肾衰竭和死亡风险。设计跨国、纵向、基于人群的队列研究。设置 关联来自加拿大(训练和时间测试)、丹麦和苏格兰(地理测试)的人口健康数据。参与者 新记录的 G3b-G4 期慢性肾病患者,估计肾小球滤过率 (eGFR) 15-44 mL/min/1.73 m2。建模超级学习器算法根据预测肾衰竭和死亡率的能力,以最小化交叉验证预测误差(Brier 评分,越低越好)来选择性能最佳的回归模型或机器学习算法(学习器)。预先指定的学习者包括年龄、性别、eGFR、蛋白尿、有或没有糖尿病以及心血管疾病。预测准确性指数是根据 Brier 评分(越高越好)计算的校准和区分度的指标,用于将 KDpredict 与基准肾衰竭风险方程进行比较,该方程不考虑死亡的竞争风险,并评估 KDpredict 死亡率模型的性能。结果 纳入了 67 942 名加拿大人、17 528 名丹麦人和 7740 名患有 G3b 至 G4 期慢性肾病的苏格兰居民(中位年龄 77-80 岁;中位 eGFR 39 mL/min/1.73 m2)。所有队列的中位随访时间为五到六年。肾衰竭的发生率为每 100 人年 0.8-1.1 例,死亡率为每 100 人年 10-12 例。 KDpredict 在预测肾衰竭风险方面比肾衰竭风险方程更准确:五年预测准确度指数为 27.8%(95% 置信区间为 25.2% 至 30.0%)。丹麦的这一比例为 6%),而丹麦为 18.1%(15.7% 至 20.4%);苏格兰为 30.5%(27.8% 至 33.5%),而苏格兰为 14.2%(12.0% 至 16.5%)。肾衰竭风险方程和 KDpredict 的预测存在很大差异,可能导致不同的治疗决策。一名 80 岁男性,其 eGFR 为 30 mL/min/1.73 m2,白蛋白与肌酐比率为 100 mg/g (11 mg/mmol),则五年肾衰竭风险预测为 10%肾衰竭风险方程(高于当前肾科转诊阈值 5%)。同一个人会从 KDpredict 中获得 2% 的肾衰竭风险预测和 57% 的死亡率五年风险预测。 KDpredict 的具有四个或六个变量的个体风险预测对于这两种结果都是准确的。使用较旧数据重新训练的 KDpredict 模型在使用时间上不同的更新数据进行测试时提供了准确的预测。结论 KDpredict 可以纳入电子病历或在线访问,以准确预测中重度 CKD 患者肾衰竭和死亡的风险。 KDpredict 学习策略旨在适应当地需求,并随着时间的推移定期修订,以适应基础卫生系统和护理流程的变化。由于我们与作为数据保管人的省卫生部(艾伯塔省卫生局)签订了合同安排,我们无法向其他研究人员提供我们的数据集。研究人员可能会请求获取类似的数据集.
更新日期:2024-04-16
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