CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model

Abstract

Rationale & objective: Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression.

Study design: Retrospective observational study.

Setting & participants: The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m².

Predictors: Patient demographic and laboratory characteristics.

Outcomes: Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years.

Analytical approach: Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline.

Results: The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex.

Limitations: The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model.

Conclusions: Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression.

Plain-language summary: Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

Graphical abstract

Figure 1

Participant flow diagram. The flow diagram depicts the number of adult participants between 18 and 75 years of age in the original dataset before removal because of exclusion criteria. Omitted participants included those with less than 12 months of data, less than 3 eGFR and serum albumin values, less than 1 UACR value, and initial eGFR <15 or >89 mL/min/1.73 m². The final data set included 110,264 participants.

Figure 2

Receiver-operator characteristic curves of classifier models showing prediction performance for >30% decline in eGFR. The 2-variable classifier included initial eGFR and eGFR slope. The 7-variable classifier included initial eGFR, eGFR slope, UACR, initial serum albumin, serum albumin slope, age, and sex. eGFR, estimated glomerular filtration rate; UACR, urine albumin-creatinine ratio.

Figure 3

Variable importance. eGFR, estimated glomerular filtration rate; UACR, urine albumin-creatinine ratio.