Early Diagnosis And A Nomogram Predictive Model Of Renal Impairment In Multiple Myeloma

Objective:Multiple myeloma(MM)is a common malignant tumor in the hematological system,whose main mechanism is a large number of monoclonal immunoglobulins produced by the abnormal plasma cells proliferation in the bone marrow,which can cause damage to multiple organs damage.Renal impairment(RI)is a serious complication of MM,and renal function level is one of the important factors to determine the prognosis of MM patients.Therefore,this study mainly retrospectively analyzed the clinical data of newly diagnosed MM patients in The Affiliated Hospital of Qingdao University,aiming to find the related risk factors of renal impairment in MM patients,and based on the risk factors,establish a Nomogram model for predicting renal impairment in multiple myeloma.To explore the verification and clinical application value of a Nomogram predictive model.It is beneficial to the early diagnosis of renal impairment in MM patients and provides an important reference for the early prevention and treatment of the disease.Methods:The clinical data of 958 patients with MM in The Affiliated Hospital of Qingdao University during June 1,2012 to October 31,2019 were collected retrospectively,including 197 cases of renal injury and 761 cases of non-renal impairment.According to the time of case inclusion,the MM patients were divided into the training group(n=672,134 cases of renal impairment and 538 cases of non-renal impairment)and the validation group(n=286,63 cases of renal impairment and 223 cases of non-renal impairment).Univariate and multivariate logistic regression equations were used to analyze the risk factors for renal impairment in patients with MM of training group.A Nomogram model for predicting renal impairment in MM was established according to the Akaike information criteria(AIC)and applied to the validation group for validation.The discriminant degree,calibration degree and clinical practicability of the model were verified and evaluated by receiver operating characteristic curve(ROC),Hosmer-Lemeshow test and decision curve analysis(DCA)respectively.Results:The incidence of renal impairment in patients with MM was 20.6%(197/958),Multivariate logistic regression results showed that the uric acid(OR=1.007,95%CI=1.005-1.009,P=0.000),proteinuria(OR=1.772,95%CI=1.271-2.471,P=0.001),hemoglobin(OR=0.974,95%CI=0.963-0.984,P=0.000),globulin(OR=0.968,95%CI=0.958-0.978,P=0.000),serum immunoglobulin free light chains κ/λ ratio(OR=3.651,95%CI= 1.897-7.025,P=0.000),serum corrected calcium(OR=2.498,95%CI=1.451-4.298,P=0.001),hematuria(OR=1.536,95%CI= 1.159-2.034,P=0.003)were independent factors for the renal impairment.The Nomogram model was constructed based on the above indicators,and the areas under ROC curve were 0.882 and0.928 respectively in the training group and the validation group.The Hosmer-Lemeshow test P=0.374(P>0.05)showed that the predicted probability of the model was in good agreement with the actual probability.DCA showed that the safety and clinical net benefit of the model were higher.Conclusion:The Nomogram model for predicting renal impairment in MM was based on hyperuricemia,proteinuria,anemia,hypoglobulinemia,abnormal serum immunoglobulin free light chains κ/λ ratio,hypercalcemia and hematuria.It has high accuracy in predicting renal impairment in patients with MM.It is beneficial to the early identification and diagnosis of renal impairment in patients with MM and has important clinical significance to improve the quality of life and survival rate of MM patients.