Construction and Evaluation of Risk Prediction Model for Hyperphosphatemia in Hemodialysis Patients
Objective To analyze the risk factors of hyperphosphatemia in patients with uremia during hemodialysis,construct a no-mogram model,and verify the prediction effect of the model.Methods Patients receiving maintenance hemodialysis in the First College of Clinical Medical Science,China Three Gorges University(Yichang Central People's Hospital)from January2019 to May2022 were en-rolled,and their clinical data of hemodialysis were collected.The optimal risk predictor subset of hyperphosphatemia were obtained by minimum absolute contraction and selection operator(Lasso)regression,and 10-fold cross validation method.Multivariate Logistic re-gression analysis was used to determine the risk predictors of hyperphosphatemia,and the prediction model was established.Receiver op-erating characteristic(ROC)curves,consistency index(C-index),calibration curve,and decision curve analysis were used to evaluate the predictive power,differentiation,calibration,and clinical utility of the prediction model.Results Among 200 hemodialysis patients,166 cases with hyperphosphatemia occurred,with an incidence of 83%.The results of multivariate Logistic regression analysis showed that parathyroid hormone,serum creatinine and transferrin saturation were independent risk factors for hyperphosphatemia in hemodialysis pa-tients.A nomogram model was established based on the above influencing factors.and it demonstrated that the area under the curve of hy-perphosphatemia in patients with uremia during hemodialysis was 0.824(95%CI:0.750-0.897),and the C-index was up to 0.784 after internal verification,with good differentiation and consistency.Conclusion Based on the risk factors of hyperphosphatemia in pa-tients with uremia during hemodialysis,the establishment of a nomogram prediction model can provide a theoretical basis for clinicians to evaluate the incidence of hyperphosphatemia in hemodialysis patients,which has clinical guiding value.
HemodialysisHyperphosphatemiaRisk prediction model
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