Supplementary Material for: Estimation of Baseline Serum Creatinine with Machine Learning

<b><i>Introduction:</i></b> Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine. <b><i>Methods:</i></b> We developed and internally validated a gradient boosting model on patients admitted in Mayo Clinic intensive care units from 2005 to 2017 to predict baseline creatinine. The model was externally validated on the Medical Information Mart for Intensive Care III (MIMIC III) cohort in all ICU admissions from 2001 to 2012. The predicted baseline creatinine from the model was compared with measured serum creatinine levels. We compared the performance of our model with that of the backcalculated estimated serum creatinine from the Modification of Diet in Renal Disease (MDRD) equation. <b><i>Results:</i></b> Following ascertainment of eligibility criteria, 44,370 patients from the Mayo Clinic and 6,112 individuals from the MIMIC III cohort were enrolled. Our model used 6 features from the Mayo Clinic and MIMIC III datasets, including the presence of chronic kidney disease, weight, height, and age. Our model had significantly lower error than the MDRD backcalculation (mean absolute error [MAE] of 0.248 vs. 0.374 in the Mayo Clinic test data; MAE of 0.387 vs. 0.465 in the MIMIC III cohort) and higher correlation (intraclass correlation coefficient [ICC] of 0.559 vs. 0.050 in the Mayo Clinic test data; ICC of 0.357 vs. 0.030 in the MIMIC III cohort). <b><i>Discussion/Conclusion:</i></b> Using machine learning models, baseline serum creatinine could be estimated with higher accuracy than the backcalculated estimated serum creatinine level.

引言：将当前血清肌酐水平与基线值进行对比，对急性肾损伤的检测至关重要。本研究提出了一种基于回归的机器学习模型，用于预测基线血清肌酐水平。方法：我们基于2005年至2017年入住梅奥诊所（Mayo Clinic）重症监护病房的患者数据，开发并完成内部验证，构建了一款梯度提升模型以预测基线肌酐水平。本研究使用2001年至2012年所有重症监护病房收治的医疗信息集市III（Medical Information Mart for Intensive Care III, MIMIC III）队列数据，对该模型开展外部验证。将模型预测得到的基线肌酐水平与实测血清肌酐水平进行对比，并将本模型的性能与通过肾脏疾病饮食改良（Modification of Diet in Renal Disease, MDRD）公式反算得到的估算血清肌酐水平进行了比较。结果：经纳入标准筛选后，梅奥诊所队列共纳入44370例患者，MIMIC III队列共纳入6112例个体。本模型采用了梅奥诊所与MIMIC III数据集的6项特征，包括慢性肾脏病患病情况、体重、身高与年龄。本模型的误差显著低于MDRD反算方法：梅奥诊所测试集的平均绝对误差（mean absolute error, MAE）为0.248 vs 0.374，MIMIC III队列的平均绝对误差为0.387 vs 0.465；同时本模型的相关性更高：梅奥诊所测试集的组内相关系数（intraclass correlation coefficient, ICC）为0.559 vs 0.050，MIMIC III队列的组内相关系数为0.357 vs 0.030。讨论与结论：相较于通过反算得到的估算血清肌酐水平，使用机器学习模型可更精准地估算基线血清肌酐水平。