Abstract

Background

Prior analyses have advocated that mortality from major cancer has declined reflecting continuing progress in cancer prevention, early detection and treatment. Survival estimates are typically presented as the probability of surviving a given length of time after the diagnosis. In contrast, conditional survival describes the probabilities of surviving y additional years given patients survived x years. Conditional survival provides additional information about how the risk of death may change over time, taking into account, how long someone has already survived. In multiple myeloma, many prognostic parameters have been proposed to predict survival, but results on conditional survival are lacking.

Methods

We evaluated 816 consecutive multiple myeloma patients treated at our department between 1997-2011 with almost complete long-term follow-up. Patients' data were assessed via electronic medical record (EMR) retrieval within an innovative research data warehouse. Our platform, the University of Freiburg Translational Research Integrated Database Environment (U-RIDE), acquires and stores all patient data contained in the EMR at our hospital and provides immediate advanced text searching capacity. In an initial step, we assessed age, gender, disease stage (Durie&Salmon [D&S]), time of death and last follow-up. Moreover, we determined 5-year conditional survival as the probability of surviving at least 5 more years as a function of years a patient had already survived since initial diagnosis (i.e. 5-year conditional survival for those, who survived 0, 1, 2, 3, 4 and 5 years after initial diagnosis). Five year conditional survival was stratified according to gender, stage, age and other risk variables.

Results

The OS probabilities at 5-/10-years were 50% and 25%, respectively. The 5-year-conditional survival remained constant over the years (50%, even at 5 years). As expected, D&S stage I vs. II+III showed different 5-year conditional survival estimates of 75% vs. 42%, respectively for those who survived 1 year after diagnosis. Similarly, age subgroups <60, 60-70 and >70 years showed notably different 5-year conditional survival estimates, but also remained constant over the course of time with ∼63%, 51% and 27%, respectively. Via multivariable Cox model including gender, admission period (<2001, 2001-2007, >2007), age and disease stage by D&S, the latter two were of statistical significance (p<0.001).To distinctively identify long-term survivors via conditional survival, we performed a comprehensive analysis of various prognostic factors, including disease-related factors, such as single components of the D&S (e.g. hemoglobin, calcium, creatinine and osteolyses) and International Staging System, laboratory variables (e.g. LDH, type of paraprotein) and host-related risks. The latter includes comorbid conditions, such as performance status and organ function. Via multivariable analysis, we identified an impaired Karnofsky Performance Status <80%, ≥2 osteolyses, hemoglobin <10g/dl, ß2-microglobulin ≥5.5mg/l and LDH ≥200U/l as significant additional risk factors (Table 1). Extensive cytogenetic analyses are currently included in our risk assessment (according to Moreau P. ASH 2012:#598 and Avet-Loiseau H. JCO 2012).

Table 1

Multivariable Cox proportional hazard model incorporating the covariables selected by boosting

Variables  HR 95%- CI p-value 
KPS (%) 80-100
<80 
1
2.4 
-
1.01-5.68 
0.05 
Osteolyses <2
≥2 
1
2.17 
-
1.52-3.10 
<0.001 
Hemoglobin ≤10
>10 
1
0.58 
-
0.4-0.84 
0.004 
ß2-MG <5.5
≥5.5 
1
1.73 
-
1.08-2.77 
0.02 
LDH <200
≥200 
1
1.39 
-
1.01-1.92 
0.05 
eGFR ≥90
60-89
<60 
1
1.22
1.28 
-
0.79-1.87
0.76-2.14 
0.88 
Albumin <3.5
≥3.5 
1
0.87 
-
0.61-1.23 
0.42 
Lung disease no-mild
moderate-severe 
1
1.31 
-
0.87-1.98 
0.20 
Freiburg Comorbidity Index 0.69 
1-3 1.21 0.47-3.08 
ISS I-II 0.77 
III 1.07 0.68-1.68 
Variables  HR 95%- CI p-value 
KPS (%) 80-100
<80 
1
2.4 
-
1.01-5.68 
0.05 
Osteolyses <2
≥2 
1
2.17 
-
1.52-3.10 
<0.001 
Hemoglobin ≤10
>10 
1
0.58 
-
0.4-0.84 
0.004 
ß2-MG <5.5
≥5.5 
1
1.73 
-
1.08-2.77 
0.02 
LDH <200
≥200 
1
1.39 
-
1.01-1.92 
0.05 
eGFR ≥90
60-89
<60 
1
1.22
1.28 
-
0.79-1.87
0.76-2.14 
0.88 
Albumin <3.5
≥3.5 
1
0.87 
-
0.61-1.23 
0.42 
Lung disease no-mild
moderate-severe 
1
1.31 
-
0.87-1.98 
0.20 
Freiburg Comorbidity Index 0.69 
1-3 1.21 0.47-3.08 
ISS I-II 0.77 
III 1.07 0.68-1.68 
Conclusions

Conditional survival analyses constitute an attractive tool to predict outcome, supplements existing measures and may guide cancer survivors in planning their future. Our ongoing risk assessment via inclusion of additional variables and cytogenetics aims to define long- vs. short-term survivors in a multiple myeloma specific risk model and should help to provide clinically relevant prognostic information (e.g accurate estimate of cause-specific survival), and to implement preventive and interventional strategies.

Disclosures:

No relevant conflicts of interest to declare.

Author notes

*

Asterisk with author names denotes non-ASH members.