Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning
Yanqin Liu1, Yuanyue Lu1, Wangxing Li1
1Big data Center for Nephropathy, The Fifth Clinical Medical College of Shanxi Medical University, Shanxi Provincial People's Hospital, Taiyuan, China.
Background:
Established prognostic models of idiopathic membranous nephropathy (IMN) were limited to traditional modeling methods and did not comprehensively consider clinical and pathological patient data. Based on the electronic medical record (EMR) system, machine learning (ML) was used to construct a risk prediction model for the prognosis of IMN.
Methods:
Data from 418 patients with IMN were diagnosed by renal biopsy at the Fifth Clinical Medical College of Shanxi Medical University. Fifty-nine medical features of the patients could be obtained from EMR, and prediction models were established based on five ML algorithms. The area under the curve, recall rate, accuracy, and F1 were used to evaluate and compare the performances of the models. Shapley additive explanation (SHAP) was used to explain the results of the best-performing model.
Results:
One hundred and seventeen patients (28.0%) with IMN experienced adverse events, 28 of them had compound outcomes (ESRD or double serum creatinine (SCr)), and 89 had relapsed. The gradient boosting machine (LightGBM) model had the best performance, with the highest AUC (0.892 ± 0.052, 95% CI 0.840-0.945), accuracy (0.909 ± 0.016), recall (0.741 ± 0.092), precision (0.906 ± 0.027), and F1 (0.905 ± 0.020). Recursive feature elimination with random forest and SHAP plots based on LightGBM showed that anti-phospholipase A2 receptor (anti-PLA2R), immunohistochemical immunoglobulin G4 (IHC IgG4), D-dimer (D-DIMER), triglyceride (TG), serum albumin (ALB), aspartate transaminase (AST), β2-microglobulin (BMG), SCr, and fasting plasma glucose (FPG) were important risk factors for the prognosis of IMN. Increased risk of adverse events in IMN patients was correlated with high anti-PLA2R and low IHC IgG4.
Conclusions:
This study established a risk prediction model for the prognosis of IMN using ML based on clinical and pathological patient data. The LightGBM model may become a tool for personalized management of IMN patients.
Insights
Machine learning models predict idiopathic membranous nephropathy (IMN) prognosis using electronic health records. The LightGBM model accurately identifies key risk factors for adverse events in IMN patients.
Area of Science:
- Nephrology
- Medical Informatics
- Machine Learning
Background:
- Established prognostic models for idiopathic membranous nephropathy (IMN) lacked comprehensive data integration.
- Traditional methods did not fully utilize clinical and pathological patient data.
- Electronic medical record (EMR) systems offer a rich data source for improved prognostic modeling.
Purpose of the Study:
- To develop a machine learning (ML)-based risk prediction model for IMN prognosis.
- To leverage comprehensive clinical and pathological data from EMRs.
- To improve the accuracy and personalization of IMN patient management.
Main Methods:
- Utilized data from 418 IMN patients diagnosed via renal biopsy.
- Extracted 59 medical features from EMRs.
- Developed and compared five ML algorithms, evaluating performance using AUC, recall, accuracy, and F1 scores.
- Employed Shapley additive explanation (SHAP) for model interpretability.
Main Results:
- The LightGBM model demonstrated superior performance with the highest AUC (0.892), accuracy (0.909), recall (0.741), and F1 score (0.905).
- Key predictors identified by SHAP include anti-phospholipase A2 receptor (anti-PLA2R), immunohistochemical immunoglobulin G4 (IHC IgG4), D-dimer (D-DIMER), triglyceride (TG), serum albumin (ALB), and others.
- High anti-PLA2R and low IHC IgG4 levels correlated with an increased risk of adverse events in IMN patients.
Conclusions:
- A novel ML-based risk prediction model for IMN prognosis was successfully established using clinical and pathological data.
- The LightGBM model shows promise as a tool for personalized management of IMN patients.
- Accurate identification of risk factors can guide clinical decision-making and improve patient outcomes.
