Abstract: TH-PO1022
Decision Tree Prediction Models for the Development of ESRD in Immunoglobulin A Nephropathy
Session Information
- Glomerular Diseases: Minimal Change Disease, FSGS, IgAN
November 07, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Glomerular Diseases
- 1203 Glomerular Diseases: Clinical, Outcomes, and Trials
Authors
- Han, Xin, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Tang, Yi, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Qin, Wei, West China Hospital of Sichuan University, Chengdu, Sichuan, China
Background
IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide and up to 40% will develop end-stage renal disease (ESRD) within 20 years.
Methods
The aim of this study was to develop a predictive model to predict the risk of progressing to ESRD by using decision tree algorithm. Data were evaluated from biopsy-proven IgAN patients in West China hospital, Sichuan University in China between 2009 to 2017. 2 final models were selected by Gini index and the area under receiver-operating characteristic (ROC) curve.
Results
Clinical model was developed by only clinical factors, as pathological model was conducted by both clinical and pathological factors. In clinical model (Figure 1), recursive partitioning indicated that the best single predictor of renal deterioration was proteinuria, followed by severe urine acid for patients with severe proteinuria (> 0.98 g/d). Systolic blood pressure (SBP) and estimated glomerular filtration rate (eGFR) were placed in the third tier of the decision tree model. T and S score were put at high levels in pathological model (Figure 2) and followed with eGFR at third level. Proteinuria and SBP were placed at forth level. Nephrotic syndrome was presented at last. The accuracy of clinical and pathological model were 0.85 and 0.86, respectively. The ROC of clinical model was 0.83, compared with pathological model with 0.85.
Conclusion
Decision model can practically predict the risk of incidence of ESRD in IgAN patients. Model with pathological factors can be more accurate.
Figure 1. Clinical model of incidence of ESRD in IgAN (Line to left: yes; right: no)
Figure 2. Pathological model of incidence of ESRD in IgAN (Line to left: yes; right: no)