Abstract
BackgroundUrinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up.ObjectiveTo quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches.MethodsThe clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked.ResultsThe linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important.ConclusionsBlood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice.
Highlights
Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD)
Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up
Through statistical power analysis of the urinary protein values, the sample size in our study was competent for further procedures with power at 1
Summary
Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). Renal prognosis predictive models in CKD patients may be helpful in identifying those at high risk who may. With the use of such models, most patients with risks of having proteinuria less than 1 g/24 h can be stratified as low risk and can potentially be treated solely by their primary outpatient follow-up, whereas those at high risk (proteinuria more than 1 g/24 h) can be referred to urgent care by an inpatient management registration. Models predicting renal progression may identify patients at low risk for renal failure in the 5 years, for whom advanced treatment may be inappropriate [6]. Models using proteinuria need to collect the 24-h urine, which is inconvenient, especially in outpatient clinics
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