本文由小咖机器人翻译整理

期刊来源：BMJ

原文链接：https://doi.org/10.1136/bmj-2023-078063

摘要内容如下：

客观

训练和测试一种超级学习者策略，用于预测患有中度至重度慢性肾脏疾病（G3B至G4期）的人的肾衰竭和死亡率的风险。

设计

多国、纵向、基于人群的队列研究。

设置

将加拿大（培训和时间测试）、丹麦和苏格兰（地理测试）的人口健康数据联系起来。

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）为15-44 mL/min/1.73 m2。

建模

超级学习者算法选择表现最好的回归模型或机器学习算法（学习者），基于他们预测肾衰竭和死亡率的能力，具有最小化的交叉验证预测误差（Brier评分，越低越好）。预先指定的学习者包括年龄、性别、EGFR、蛋白尿、伴或不伴糖尿病和心血管疾病。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指标，用于将KDPredict与基准肾衰竭风险方程（不考虑死亡的竞争风险）进行比较，并评估KDPredict死亡率模型的性能。

结果

67942名加拿大人、17528名丹麦人和7740名苏格兰居民患有G3B至G4阶段的慢性肾病（中位年龄77-80岁；中位EGFR为39 mL/min/1.73 m2）。所有队列的中位随访时间为5-6年。肾衰竭发病率为0.8-1.1/100人年，死亡率为10-12/100人年。在预测肾衰竭风险方面，KDPredict比肾衰竭风险方程更准确：在丹麦，五年预测准确度指数为27.8%（95%置信区间25.2%-30.6%）对18.1%（15.7%-20.4%）；在苏格兰，为30.5%（27.8%-33.5%）对14.2%（12.0%-16.5%）。肾衰竭风险方程和KDPredict的预测差异很大，可能导致治疗决策的分歧。根据肾衰竭风险方程，EGFR为30 mL/min/1.73 m2且白蛋白与肌酐比值为100 mg/G（11 mg/mmol）的80岁男性的5年肾衰竭风险预测值为10%（高于当前肾脏科转诊阈值5%）。KDPredict对同一个人5年内肾衰竭的风险预测为2%，死亡率为57%。KDPredict的四个或六个变量的个体风险预测对两种结果都是准确的。使用旧数据重新训练的KDPredict模型在时间上不同的、更新的数据中测试时提供了准确的预测。

结论

KDPredict可以纳入电子医疗记录或在线访问，以准确预测中度至重度CKD患者的肾衰竭和死亡风险。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，以适应基本卫生系统和护理流程的变化。

英文原文如下：

Abstracts

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.

-----------分割线---------

点击链接：https://www.mediecogroup.com/community/user/vip/categories/ ，成为医咖会员，获取12项专属权益。