**Abstract**

Survival rates for newly diagnosed multiple myeloma have increased to a remarkable 8–12 years. Novel agents, autologous stem cell transplantation, monoclonal antibodies, improvements in supportive care and attention to minimal residual disease negative all have aided this remarkable journey. With these treatments we are identifying tools to achieve complete remissions. Prognostic factors have an important role in selecting proper patient approaches for trial designs. Prognostic and predictive clinical biomarkers have shaped staging and treatment selections for newly diagnosed multiple myeloma. Here we review the Early Mortality Prediction Matrix to identify those at risk of an early death (<6 months) incorporating both disease biology with patient fitness. We also review current standards of care for multiple myeloma and provide a three and five-year overall survival prediction matrix. We review benefits for MRD negativity and Next-Gen Sequencing. These tools will help clinicians improve upon reducing early mortality in newly diagnosed multiple myeloma patients and provide further framework for improving survival by assessing clinical, biologic and individual multiple myeloma patients.

**Keywords:** newly diagnosed multiple myeloma, prediction matrix, prognostic factors, prediction factors, progression-free survival, early mortality, novel agents, next-gen sequencing, overall survival

### **1. Introduction-early mortality**

The current era of advances in multiple myeloma (MM) identifies a subset of newly diagnosed multiple myeloma (NDMM) patients with early mortality (EM) within the first 6 months of diagnosis [1–6].

Prognostic and predictive risk factors have been identified by the International Myeloma Working Group (IMWG) based upon the LDH, international staging system (ISS), Stage III disease and adverse cytogenetics [7]. Limitations of this study include patients limited to autologous stem cell transplantation (SCT)

which included only 40% of all patients. Prediction matrix models based upon those created for cardiovascular disease and rheumatoid arthritis [8, 9] which can calculate the risk of specific outcomes such as mortality allowing the differential weighting of risk factors. We can identify patients at risk with NDMM for EM to provide insight in applying different treatment approaches. Prediction tools have been applied in other hematologic malignancies to predict EM. In AML, prediction factors have improved treatment paradigms [10]. In diffuse large B-cell lymphoma, cell of origin and molecular markers along with PET scans have provided earlier treatment interventions to improve outcomes [11, 12].

Real World patients often differ from those enrolled in clinical trials. These patients tend to be older and less fit, have more co-morbidities and less often SCT candidates [13]. An observational patient registry allows broad patient characteristics and treatment outcomes while assessing NDMM patient characteristics, biology, co-morbidities and treatments for progression-free survival (PFS) and overall survival (OS) [13–16].
