Conditional Survival Analysis in a Large Cohort of Multiple Myeloma Patients Provides Detailed Information about Long-Term Survival and Constitutes a Different Way to Identify Patients with Extended Life Expectancy
Authors: Kleber Martina , Hieke Stefanie , Koch Bernd , Ihorst Gabriele , Wäsch Ralph , Schumacher Martin , Engelhardt Monika 
Published: October 2019

Abstract 4982

Introduction:

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 still lacking.

Methods:

We evaluated 816 consecutive multiple myeloma patients treated at our department between 1997–2011. 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- and 10- years were 50% and 25%, respectively. The 5-year conditional survival probabilities remained almost constant over the years a patient had already survived after initial diagnosis (∼53%). According to baseline variables, conditional survival estimates showed no gender differences. As expected, D&S stage I vs. stage II+III showed substantially different 5-year conditional survival estimates over the years for those who survived 1 year after initial diagnosis (75% vs. 42%, respectively). 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. The multivariable Cox model, including gender, year of admission (before 2001, 2001–2007 and after 2007), D&S (stage II-III) and age (>70 years) showed increased hazard ratio (HR) for both latter groups of 2. 2 (95% CI 1. 8–2. 7; <0. 0001) and 3. 5 (95% CI: 2. 7–4. 4, <0. 0001), respectively. Ongoing analyses aim to distinctively identify long-term survivors via conditional survival. In order to obtain a comprehensive analysis of relevant prognostic factors, we have focused on variables with high degree of completeness. These include 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 risk factors. The latter include comorbid conditions such as performance status and organ function. Of note, over the study period, admission of patients <60 years decreased from 60% to 34%, but increased for those ≥70 years from 10% to 35%, respectively, illustrating that not only young and fit, but also elderly patients are increasingly treated within large referral and university centers and that patient cohorts and risks do not remain constant over time.

Conclusions:

In this study, involving a large cohort of multiple myeloma patients, analyses stratified by age and stage revealed substantially different conditional survival estimates. Conditional survival seems an attractive tool to predict outcome over time, supplements existing measures and may guide cancer survivors in planning their future. The combination of the main prognostic factors of the ongoing analysis in a multiple myeloma specific risk model, may define long-term survivors via conditional survival more distinctively and will be presented at the meeting.

Disclosures:

No relevant conflicts of interest to declare.