Predicting Urine Albumin-to-Creatinine Ratio In Patients With Idiopathic Membranous Nephropathy By Machine Learning And Analysis Of Prognostic Factors

Background and ObjectiveIdiopathic membranous nephropathy(IMN)is one of the main causes of nephrotic syndrome in adults.The course of IMN is quite variable.Urine albumin-to-creatinine ratio(UACR)is a main indicator to evaluate the severity of IMN and it is also an important reference for drug regimen.So,this study,a single center retrospective analysis of IMN patients,aimed to predict the UACR and analyze the risk factors of prognosis,which could potentially help to evaluate the progression and therapeutic effect in newly diagnosed IMN patients.MethodsThis study involved 383 IMN patients diagnosed by renal biopsy in Qilu Hospital of Shandong University.The real-world patients’ demographics,conclusion of renal biopsy and laboratory data were collected retrospectively from electronic medical record(EMR).The data of 300 IMN randomly selected patients from March 2013 to April 2021 were used as the training set.The data of 83 selected IMN patients from January March 2013 to April 2021 were regarded as the test set.Six different ML algorithms and a stack algorithm were used to predict post-therapeutic UACR in patients with IMN.The UACR predicted by ML algorithms was compared with the ground truth.ResultThe ML algorithms were able to accurately predict UACR of IMN patients 6 months in advance.The stack algorithm(Ridge and Lasso)performed best in UACR predictions.The mean absolute errors(MAEs)of UACR predictions based on the baseline data with respect to the ground truth were 1.75(95%CI:1.53,1.97),1.71(1.48,1.91)and 1.31(1.05,1.59)for the 1-,3-and 6-month predictions,respectively;and the MSEs of the UACR predictions with respect to the ground truth were 4.74(4.52,4.96),6.06(5.79,6.33)and 4.79(4.54,5.04)for the 1-,3-and 6-month predictions,respectively.Baseline UACR,baseline serum immunoglobulin G level,and thyroid function were found to be significant predictors of early prognosis of IMN by machine learning.ConclusionIn this retrospective study,a ML model was developed and validated for individual prediction of UACR in IMN patients.The ML model could be used in clinical practice to guide clinicians to screen patients with poor prognosis and optimize treatment readily.