Differential Diagnosis Models for Multiple Myeloma Combined with Renal Injury and Chronic Kidney Disease or Nephrotic Syndrome

Abstract

When multiple myeloma(MM) is combined with renal injury, most patients are easily misdiagnosed as kidney diseases. This study aimed to establish a differential diagnosis model for MM combined with renal injury based on clinical information. A total of 77 patients with MM combined with renal injury were recruited as the case group, and 112 patients with kidney diseases were recruited as a control group. Support vector machine (SVM), decision tree (DT), and artificial neural network (ANN) models were developed based on significant clinical variables. Accuracy and area under the receiver operating characteristic curve (AUC) were used to evaluate each model. Accuracies of SVM, DT, and ANN were 0.843,0.902, and 0.941. The AUCs of SVM, DT, and ANN were 0.822,0.879, and 0.932. Lower extremity edema, bone pain, and lactate dehydrogenase (LDH) were common important indicators identified by SVM, DT and ANN models. When these three indicators were excluded, the ANN model prediction effect decreased significantly (P<0.05). The results suggest that the ANN model best predicts the differential diagnosis between MM combined with renal injury and chronic kidney disease/nephrotic syndrome. Important features contributing to identifying the diseases, including lower extremity edema, bone pain, and LDH, may assist in diagnosing such diseases in the future.