Prognostic Nomogram for the Overall Survival of Patients with Newly Diagnosed Multiple Myeloma

Biomed Res Int. 2019 Apr 8:2019:5652935. doi: 10.1155/2019/5652935. eCollection 2019.

Abstract

To establish a nomogram for predicting the overall survival (OS) of patients with newly diagnosed multiple myeloma (MM), 304 patients with newly diagnosed MM were recruited between June 1, 2010, and June 30, 2015, from the Beijing Chaoyang Hospital, Capital Medical University, and randomly divided into training (n=214) and validation (n=90) cohorts. The Kaplan-Meier method and the Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinical and laboratory parameters on survival. Significant prognostic factors were combined to build a nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves and compared with the five staging systems currently used for MM. Multivariate analysis of the training cohort revealed that the age at diagnosis, clonal bone marrow plasma cells, serum lactate dehydrogenase, serum β2-microglobulin, and del (17p) were independent risk factors for OS and were used to establish the nomogram. The C-index value of the nomogram for predicting OS was 0.749, which was significantly higher than the C-indices of the five most common staging systems currently used for MM. In the validation cohort, the C-index for nomogram-based predictions was 0.711 for OS, and the nomogram discrimination was better than the above mentioned five staging systems (P<0.001). All calibration curves revealed good consistency between predicted and actual survivals. The proposed nomogram is more accurate in predicting the prognoses of patients with newly diagnosed MM.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cohort Studies
  • Female
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Middle Aged
  • Multiple Myeloma / metabolism
  • Multiple Myeloma / pathology*
  • Neoplasm Staging / methods
  • Nomograms
  • Prognosis
  • Proportional Hazards Models