Abstract
Background and ObjectivesChronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict.MethodsThis is an observational, retrospective, single-centre study. The cohort was patients attending hospital and nephrology clinic at The Canberra Hospital from September 1996 to March 2018. Demographic data, vital signs, kidney function test, proteinuria, and serum glucose were extracted. The model was trained on the featurised time series data with XGBoost. Its performance was compared against six nephrologists and the Kidney Failure Risk Equation (KFRE).ResultsA total of 12,371 patients were included, with 2,388 were found to have an adequate density (three eGFR data points in the first 2 years) for subsequent analysis. Patients were divided into 80%/20% ratio for training and testing datasets.ML model had superior performance than nephrologist in predicting ESKD within 2 years with 93.9% accuracy, 60% sensitivity, 97.7% specificity, 75% positive predictive value. The ML model was superior in all performance metrics to the KFRE 4- and 8-variable models.eGFR and glucose were found to be highly contributing to the ESKD prediction performance.ConclusionsThe computational predictions had higher accuracy, specificity and positive predictive value, which indicates the potential integration into clinical workflows for decision support.
Highlights
Chronic kidney disease (CKD) is a major cause of morbidity and mortality globally, having a reported prevalence of 11–13% [1]
We describe the training of a machine learning (ML) model that was capable of predicting which CKD patients will progress to end stage kidney disease (ESKD) within 2 years
In addition to birth year, sex and date of death, this dataset consists of 17 timestamped clinical measures including creatinine and derived estimated glomerular filtration rate (eGFR) (168,500 points each), glucose (145,961 points), sitting and standing blood pressure (42,818 points), heart rate (28,741 points), weight (47,981 points), height (35,421 points) and derived Body Mass Index (BMI), HbA1c (15,349 points), urine protein-creatinine ratio (14,777 points) and 24-h proteinuria (883 points)
Summary
Chronic kidney disease (CKD) is a major cause of morbidity and mortality globally, having a reported prevalence of 11–13% [1]. Whilst CKD may culminate in end stage kidney disease (ESKD), rate of progression is highly. ML Improves ESKD Prediction variable and difficult to predict. ESKD is associated with a marked increase in mortality and morbidity: it is a terminal condition without renal replacement therapy (RRT) in the form of haemodialysis, peritoneal dialysis, or kidney transplantation. As ESKD approaches, patients and clinicians are required to make difficult decisions [3]. Any method which will improve the ability to correctly identify patients who will require RRT is highly desirable. Chronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict
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