| Background:Secondary hyperparathyroidism(SHPT)is a common problem encountered in the management of patients with chronic kidney disease(CKD).Phosphate accumulation,reduced active vitamin D,increased fibroblast growth factor-23(FGF-23)and decreased calcium ion concentration promote the synthesis and secretion of parathyroid hormone(PTH).Elevated PTH devotes renal bone disease,vascular calcification and increased mortality.Therefore,it is extermely important to predict the possibility of secondary hyperparathyroidism in patients with chronic kidney disease early.Purpose:Screening the risk factors of CKD combined with secondary hyperthyroidism,then developing and validating the risk prediction model of chronic kidney disease with secondary hyperthyroidism.Methods:The clinical data of 1332 patients with CKD confirmed in the Department of Nephrology of the First Hospital of Jilin University from October 2017 to March 2019 were retrospectively analyzed.General clinical data included gender,age,height,weight,history of hypertension,history of diabetes,history of cardiovascular disease,history of smoking and drinking and history of dialysis and dialysis mode.Laboratory information consisted of hemoglobin(Hb),aspartate aminotransferase(AST),alanine aminotransferase(ALT),alkaline phosphatase(ALP),serum albumin(ALB),uric acid(UA),Triglyceride(TG),cholesterol(TC),high density lipoprotein cholesterol(HDL-C),low density lipoprotein cholesterol(LDL-C),blood calcium(Ca),blood phosphorus(P),blood urea nitrogen(BUN),serum creatinine(Scr),carbon dioxide binding capacity(CO2CP),parathyroid hormone(PTH),and left ventricular ejectionfraction(LVEF).Statistical analysis was performed by SPSS 25.0.The measurement data conforming to the normal distribution were expressed as mean ± standard deviation(X ± S),and comparison between groups was performed by t test.Measurement data that did not meet the normal distribution were expressed as the median(quartile),and comparisons between groups were performed using the Mann-Whitney U test.Count data were expressed in number of cases(percentage),and comparisons between groups were made using chi-square(χ2)test or Fisher’s exact test.Multivariate Logistic regression analysis was conducted to screen the risk factors associated with secondary hyperparathyroidism in patients with chronic kidney disease.A multivariate logistic regression analysis was used to construct a predictive model to distinguish patients with chronic kidney disease from secondary hyperparathyroidism.The R software(3.6.1)was selected to draw the receiving operator characteristic(ROC),and the area under the curve(AUC)was calculated to evaluate the performance of the model.The closer the AUC value is to 1,the better the discriminative ability of the prediction model.It was generally considered that AUC> 0.75 indicated that the model has better discriminating ability.At the same time,a Calibration plot was drawn to evaluate the consistency of the prediction model.P <0.05 was considered statistically significant.Results:1.General data analysis of CKD patients: A total of 1332 patients were collected,802 males,accounting for 60.21%;530 females,accounting for 39.79%.Mean age:54.88±15.38 years.There were 905 cases without dialysis,accounting for 67.94%;343 cases with hemodialysis,accounting for 25.75%;84 cases with peritoneal dialysis,accounting for 6.31%.7 patients with stage CKD 1-2,accounting for 0.5%;78 patients with stage CKD 3,accounting for 5.86%;128 patients with stage CKD 4,accounting for 9.64%;1119 patients with stage CKD 5,accounting for 84.0%.In terms of past history,509 cases of diabetes,accounting for 38.21%,and 1124 cases of hypertension,accounting for 84.38%.602 cases of cardiovascular disease,accounting for 45.20%.There were 351 cases of smoking,accounting for 26.35%.There were 226 cases of drinking history,accounting for 16.97%.Among them,there were 953 cases of development cohort and 379 cases of validation cohort.The analysis results showed that there were differences between the two groups in smoking history,drinking history,aspartate aminotransferase,and alkaline phosphatase,and there were no significant difference in the remaining indicators between the two groups.2.Univariate analysis of development cohort: There are significant differences in age,dialysis mode,CKD stage,hemoglobin,aspartate aminotransferase,alkaline phosphatase,serum albumin,uric acid,blood urea nitrogen,blood calcium,blood phosphorus,corrected calcium,calcium-phosphorus product,and carbon dioxide binding capacity between the normal PTH group and the elevated PTH group(P<0.05).3.Multivariate Logistic regression analysis of development cohort: According to the results of univariate analysis,statistically significant factors were included in multivariate Logistic regression analysis,and 5 independent risk factors were screened out: CKD stage(OR=2.877,95% CI : 1.986-4.169,P<0.01),alkaline phosphatase(OR=1.010,95%CI: 1.004-1.015,P=0.001),blood urea nitrogen(OR=1.039,95%CI:1.005-1.073,P=0.023),serum albumin(OR=1.073,95%CI: 1.031-1.117,P =0.001),blood calcium(OR=0.035,95%CI: 0.11-0.112,P<0.01).It suggested that with the progress of CKD,the higher the alkaline phosphatase value,the higher the blood urea nitrogen value,the higher the serum albumin value,and the lower the blood calcium level,the greater the risk of SHPT.4.Construction of risk prediction model: Based on the results of Logistic multivariate regression analysis of the modeling data set,5 independent risk factors(CKD stage,alkaline phosphatase,blood urea nitrogen,serum albumin,blood calcium)were included in the prediction model A.The prediction model B incorporates the CKD stage,alkaline phosphatase,blood urea nitrogen,serum albumin,blood calcium,and blood phosphorus.5.Construction of nomograms: Draw the ROC curves of prediction model A and model B.It was found that the ROC curves of the two models coincided with each other and the area under the curve had the same AUC(0.834).This study constructed anomogram for prediction model A.6.Evaluation of the model: The area AUC values under the ROC curve of the calculated development cohort and the validation cohort were 0.834(95% CI:0.800-0.867,P <0.001)and 0.813(95% CI: 0.788-0.891,P <0.001).The AUC values of both data sets were greater than 0.75,indicating that the line chart was better discriminating against SHPT.The calibration graphs of the development cohort and the validation cohort were drawn separately.It was found that the coincidence degree of the logistic calibration curve and the ideal curve in the two data sets was high.The P values of the calibration graphs were 0.813 and 0.784,indicating that the prediction model predicts the occurrence of SHPT and there was consistency between actual SHPT occurrences.Conclusions:1.Our research suggests: CKD stage,alkaline phosphatase,blood urea nitrogen,serum albumin,blood calcium are independent risk factors for chronic kidney disease with secondary parathyroidism.2.Establish a nomogram of the risk prediction of CKD combined with SHPT with the predictive factors of CKD stage,alkaline phosphatase,blood urea nitrogen,serum albumin,and blood calcium,then predict the CKD patients with SHPT possibility early. |
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