Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

Abstract

ObjectivesTrajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach.DesignRetrospective single-centre cohort study.SettingsTertiary referral university hospital in Toyoake city, Japan.ParticipantsA total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years.Primary outcomeOur main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters.ResultsOur clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics.ConclusionsThe random forest model could be useful in identifying patients with extremely rapid eGFR decline.Trial registrationUMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches

“…The clinical outcomes investigated in these studies primarily focus on predicting the progression of CKD to end-stage renal disease (ESRD) or the initiation of renal replacement therapy (RRT). Some studies also aim to predict the aggravation or progression of diabetic kidney disease (DKD), proteinuria, or the rapid decline in estimated glomerular filtration rate (eGFR) [ 9 , 10 , 18 , 23 ]. In terms of main findings, the studies generally report promising results in utilizing AI/ML techniques for predicting CKD progression, with many models achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve scores.…”

“…In terms of main findings, the studies generally report promising results in utilizing AI/ML techniques for predicting CKD progression, with many models achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve scores. Several studies highlight the importance of incorporating longitudinal data, baseline characteristics, and specific biomarkers or clinical features in improving prediction performance [ 7 , 10 , 16 , 23 ].…”

“…The identification of key predictive features and biomarkers is another crucial aspect of these studies. Various factors, such as age, gender, comorbidities (e.g., hypertension, diabetes), baseline eGFR, proteinuria, serum albumin, hemoglobin levels, and inflammatory markers like C-reactive protein, were found to be significant predictors of CKD progression across multiple studies [ 10 , 16 , 17 , 23 ]. This knowledge can guide clinicians in focusing their efforts on monitoring and managing these critical risk factors, potentially slowing disease progression and delaying the need for RRT.…”