Hong Zou1, Rong Yang2, Zhong-Xian Liao1, Tian-di Qin1, Ping Chen3, Bei-Ying Zhang1, Ying-Ping Cao1, Hui-Fang Huang4. 1. Clinical Laboratory, Fujian Medical University Union Hospital, Fuzhou, China. 2. Department of Medical Record Management, Fujian Medical University Union Hospital, Fuzhou, China. 3. Fujian Provincial Key Laboratory of Hematology, Fujian Institute of Hematology, Fujian Medical University Union Hospital, Fuzhou, China. 4. The Central Laboratory, Fujian Medical University Union Hospital, Fuzhou, China.
Abstract
BACKGROUND: IgM monoclonal gammopathy can be present in a broad spectrum of diseases. We evaluated the value of serum markers in the differential diagnosis of Waldenstrom macroglobulinemia (WM) and other types of IgM monoclonal gammopathies. METHODS: We included patients who were first admitted to hospital and identified as having IgM monoclonal gammopathy by serum immunofixation electrophoresis (sIFE). We evaluated basic clinical features, sIFE, diagnosis, and serum markers. Furthermore, we applied the receiver operating characteristic (ROC) curve to analyze the differential diagnosis value of serum markers for WM. Finally, we used logistic regression and ROC curve to analyze the differential diagnosis value of multimarker combinations to identify WM. RESULTS: IgM monoclonal gammopathy was most frequently found in patients with Waldenstrom macroglobulinemia, followed by monoclonal gammopathy of undetermined significance (MGUS), B-cell non-Hodgkin Lymphoma (B-NHL), and multiple myeloma (MM). Serum markers showed significant differences among the four diseases. The diagnostic markers LDH, IgM, IgG, IgA, and serum light chain К had higher diagnostic efficiency. Among these markers, serum IgM provided the highest diagnostic efficiency. Additionally, the combined use of all five serum markers provided the most effective diagnosis. CONCLUSIONS: The five serum markers, LDH, IgM, IgG, IgA, and К, each yielded a specific efficacy in differential diagnosis of WM. The single marker with the highest diagnostic efficiency was the serum IgM level. However, a combination of multiple serum markers was better than the use of a single marker in diagnosing WM. The combined use of all five serum markers provided the most effective diagnosis, with an AUC of .952 and sensitivity and specificity of 87.8% and 86.9%, respectively.
BACKGROUND: IgM monoclonal gammopathy can be present in a broad spectrum of diseases. We evaluated the value of serum markers in the differential diagnosis of Waldenstrom macroglobulinemia (WM) and other types of IgM monoclonal gammopathies. METHODS: We included patients who were first admitted to hospital and identified as having IgM monoclonal gammopathy by serum immunofixation electrophoresis (sIFE). We evaluated basic clinical features, sIFE, diagnosis, and serum markers. Furthermore, we applied the receiver operating characteristic (ROC) curve to analyze the differential diagnosis value of serum markers for WM. Finally, we used logistic regression and ROC curve to analyze the differential diagnosis value of multimarker combinations to identify WM. RESULTS: IgM monoclonal gammopathy was most frequently found in patients with Waldenstrom macroglobulinemia, followed by monoclonal gammopathy of undetermined significance (MGUS), B-cell non-Hodgkin Lymphoma (B-NHL), and multiple myeloma (MM). Serum markers showed significant differences among the four diseases. The diagnostic markers LDH, IgM, IgG, IgA, and serum light chain К had higher diagnostic efficiency. Among these markers, serum IgM provided the highest diagnostic efficiency. Additionally, the combined use of all five serum markers provided the most effective diagnosis. CONCLUSIONS: The five serum markers, LDH, IgM, IgG, IgA, and К, each yielded a specific efficacy in differential diagnosis of WM. The single marker with the highest diagnostic efficiency was the serum IgM level. However, a combination of multiple serum markers was better than the use of a single marker in diagnosing WM. The combined use of all five serum markers provided the most effective diagnosis, with an AUC of .952 and sensitivity and specificity of 87.8% and 86.9%, respectively.
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