Using Autoencoders for Imputing Missing Data in eGFR Decline Trajectories of Patients with CKD

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ASN Kidney Week



Using machine learning (ML) approaches to impute missing data has not been explored in CKD progression. We investigated the utility of a data-driven imputation to improve downstream classifier prediction of rapid eGFR decline in the CURE-CKD registry.


We analyzed CKD patients at UCLA (N=13,206) over a 2-year period. We used: 1) the dataset with missing data; and 2) a censored subset with no missing data. We introduced 33% and 66% missingness by removing values by removing values either missing completely at random (MCAR); missing at random (MAR); or missing not at random (MNAR). We included: eGFR, hemoglobin (HbA1c), systolic blood pressure (SBP), number of ambulatory and inpatient visits, age, sex, ethnicity, rurality status, diagnosis of hypertension, diabetes mellitus (DM), pre-DM, and use of renin angiotensin aldosterone system inhibitors. We introduced missingness on SBP and HbA1c to mirror the original dataset. We imputed missing values using an autoencoder ML model. To predict a 40% eGFR decline over 2 years, we developed random forest models using the full and resultant imputed datasets.


On the full subset, the MNAR imputation method achieved a root mean squared error (RMSE) of 0. The MAR method achieved RMSE of 3.8 at 33% missingness and 5.4 at 66%. MCAR achieved RMSE of 38.5 at 33% missingness and 56.4 at 66%. Using the random forest model to predict rapid decline on the fully observed subset without removing and imputing data achieved a receiver operating characteristic (ROC) area under the curve (AUC) mean of 80.8%±1.1 and precision/recall (PR)-AUC mean of 23.9%±1.5; the same as our methodology on MNAR, which is explained by the RMSE of 0, shown in Table 1.


Our method accurately imputes clinical data values while accounting for uncertainty caused by missing values.

Clinical Institute

Kidney & Diabetes