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CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model
Joseph Aoki1, Cihan Kaya1, Omar Khalid1
1Sonic Healthcare USA.
Insights
A new machine learning model accurately predicts chronic kidney disease (CKD) progression using common lab results. This tool aids early detection and management of kidney disease, improving patient outcomes.
Area of Science:
- Nephrology
- Data Science
- Biostatistics
Background:
- Chronic kidney disease (CKD) is a significant global health issue associated with high morbidity and mortality.
- Predictive models for CKD progression are lacking, especially for early disease stages.
- Early identification of CKD progression is crucial for timely intervention and management.
Purpose of the Study:
- To develop and validate machine learning models for predicting CKD progression using readily available demographic and laboratory data.
- To assess the performance of these models across the spectrum of CKD.
- To identify key predictors of accelerated kidney function decline.
Main Methods:
- A retrospective observational study utilizing deidentified laboratory data from 110,264 adult patients over 5 years.
- Machine learning models, specifically random forest survival methods, were employed.
- Predictors included demographic and laboratory characteristics, with a focus on estimated glomerular filtration rate (eGFR) slope and urine albumin-creatinine ratio.
Main Results:
- A 7-variable risk classifier achieved an area under the curve (AUC) of 0.85 for predicting >30% eGFR decline within 5 years.
- The most significant predictor of CKD progression was the eGFR slope.
- Other key predictors included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex.
Conclusions:
- The developed machine learning classifier accurately predicts significant eGFR decline in patients with CKD.
- The model effectively utilizes easily obtainable laboratory data for risk prediction.
- This tool has the potential to enhance early recognition and optimize management strategies for patients at risk of CKD progression.
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.
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