Predicting Early Mortality in Multiple Myeloma Patients Treated with Novel Agents

Abstract

BACKGROUND: Despite remarkable therapeutic advances in the last 2 decades and a major improvement in survival, a number of multiple myeloma (MM) patients (pts) present a short-term outcome. AIMS: Our aim was to identify the main factors (baseline characteristics, response to therapy, relapse features) determining early mortality (EM) among a cohort of newly diagnosed symptomatic MM pts treated with novel agents. METHODS: We conducted a national multicenter retrospective study, including a cohort of symptomatic MM pts diagnosed between January/2010 and June/2017, treated with novel agents (bortezomib, thalidomide or lenalidomide) with the maximum age of 75 years-old and living <3 years (y) after diagnosis. We considered EM as pts living <12 months (m). All pts were dead at the time of the analysis. Data were collected from medical registries and databases. Response to treatment was evaluated according to the International Myeloma Working Group (IMWG) consensus criteria (2016). Statistical analysis was performed using STATA v.14.2 and significant levels were set at p<0.05. All risk factors with p<0.15 in the univariate model were further entered into the multivariate analysis. RESULTS: A total of 142 pts were included in the study, 58% male and the median age at diagnosis was 65 y (27-75). IgG was the most frequent subtype (41%), followed by IgA (32%) and light-chain κ (14%) and λ (9%). At diagnosis, renal impairment (RI) was present in 32%, extramedullary disease (EMD) in 18% and bone disease in 69% of the pts; 58.3% were in stage III and 30.6% in stage II (ISS). Fluorescent in situ hybridization analysis was performed in 76 pts, 45% presenting high-risk cytogenetic abnormalities (HRC) [del(17p) and/or t(4;14) and/or t(14;16)]. Hypertension was present at diagnosis in 32% and diabetes in 18% pts. First-line therapy (1stL) included novel agents in 97% of the pts (64% bortezomib-based (Bor), 23% thalidomide-based (Thal) and 10% bortezomib plus IMID-based (BorIM). Response evaluation showed an overall response rate (ORR) of 73% (12% CR; 25% VGPR; 36% PR); 27% were refractory. Median time to response was 3.2m. Median number of therapy lines was 2 (1-3); 65% of the pts were refractory or progressed after 1stL therapy, 18% developed extramedullary disease (EMD) and 5 pts progressed to plasma cell leukemia. In pts receiving a 2ndL therapy, treatment-free interval was 9.2m and the most used regimens were lenalidomide (33%), Thal (25%), Bor (14%) and BorIM (11%) -based. ORR to 2ndL was 19.5% (3.4% VGPR and 16.1% PR). Six-month, 1-year and 2-years mortality was 7%, 27% and 65%, respectively. Median time until death was 18.2m; 55% of the pts died directly from disease progression (DP) and 45% from other causes [infection in 68% (only 17% of whom in DP), and cardiovascular complications in 13%]. In our study, prior hypertension (HR 1.53; 95% CI 1.01-2.32; p=0.046) and relapse with EMD (HR 2.0; 95% CI 1.23-3.26; p=0.005) were associated with increased risk of death. Pts≥70y also showed an increased risk of death, although not statistically significant (HR 1.42; 95% CI 0.96-2.12; p=0.081). In our cohort, refractoriness to 1stL treatment was not related to a significant increased risk of death (HR 1.48; 95% CI 0.91-2.40; p=0.11). Moreover, age, HRC, ISS stage, RI, bone disease and lactate dehydrogenase levels didn´t show a mortality predictive value. When comparing pts living <1y (EM) with pts living 1-3y, we identified the lack of at least PR (42.1% vs 21.2%, respectively; p=0.013) and a shorter time to 2ndL treatment (1.2 vs 10.0m; p=0.033) as predictors of death within the first 12m. Finally, in a multivariate analysis only the age at diagnosis >70y (HR 2.11; 95% CI 1.22-3.65; p=0.008) and EMD at relapse/progression (HR 2.55; 95% CI 1.45-4.50; p=0.001) predicted higher risk of mortality. CONCLUSIONS: ORR to 1stL therapy was similar to the generally expected response rate, showing that IMWG response criteria are not adequate to predict short-term outcome in the era of novel agents. The same was observed with classical baseline risk stratification features, raising the importance of defining a more accurate strategy to predict survival in MM pts. Refractory disease after 2ndL (higher than generally reported) and infections were the leading contributors to early death in our pts cohort. These data may provide new opportunities to define patient-adapted treatment strategies in order to decrease EM and improve overall survival in MM. Disclosures No relevant conflicts of interest to declare.

A real‐world data analysis of predictors of early mortality after a diagnosis of multiple myeloma

“…To our knowledge, this is the largest real‐world study to examine early mortality rates and predictors of early mortality in the era of novel therapeutic approaches to managing MM. Our finding that those aged ≥70 years had a 3‐fold higher early mortality rate than those <70 years old is consistent with published reports 4,11,12,19,20,26–30 . As an example, Costa et al.…”

“…used data from the Connect MM Registry to examine predictors of early mortality (defined as death ≤ 6 months from diagnosis) and found that age > 75 years, higher ECOG PS, lower EuroQol‐5D mobility score, higher ISS stage, lower platelet count, and prior hypertension were predictive of early mortality 12 . Data from other studies have also been consistent with our findings and those of the Connect MM Registry in reporting a comprehensive list of predictors of early mortality among those with a new diagnosis of MM including (1) patient demographics (e.g., older age at diagnosis 4,11,12,19,20,26–31 and male sex 7,16,20,29 ), (2) presence of baseline functional impairments (e.g., poor PS 7,12,15,19,26,30,32 and reduced mobility 12 ), (3) disease‐specific and laboratory characteristics (ISS stages II and III, 7,11,12,26,28,30,33 renal impairment, 7,12,14,28,31,32 elevated β 2 ‐microglobulin levels, 4,13,15,19 elevated LDH, 7,11,13,15,16,33 hypercalcemia, 16,26,30 hypoalbuminemia, 4,13,15,16 hemoglobin, 7 and platelets, 12,14 ), presence of medical comorbidities (low body mass index, 10,14 history of coronary artery disease, 32 hypertension, 12,27 respiratory disease, 28 liver disease, 28 hepatitis C, 28 and overall higher comorbidity scores 14 ), (4) barriers to health care access (low household income 29 and geographic region 20,29 ), and (5) therapy‐related characteristics (e.g., absence of novel therapy use) 4,7 . Although high‐risk cytogenetic abnormalities are associated with shorter overall survival among patients with MM, we found no association with early mortality rates 22,34,35 .…”

“…Our finding that those aged ≥70 years had a 3-fold higher early mortality rate than those <70 years old is consistent with published reports. 4,11,12,19,20,[26][27][28][29][30] As an example, Costa et al examined early mortality rates (defined as death ≤ 12 months from diagnosis) in a population-based sample of those diagnosed with MM between 1993 and 2010 and found that those aged ≥65 years had a 2-fold higher early mortality rate than those <65 years old (35.3% vs. 17.6%). 29 In disparity in early mortality rates between those aged ≥65 years and those <65 years old (15% vs. 6.7%).…”

“…7 These earlier studies demonstrate the disproportionate burden of early deaths among older adults with newly diagnosed MM and suggest more concentrated efforts are needed to identify factors that contribute to early mortality and strategies to overcome them. These (1) patient demographics (e.g., older age at diagnosis 4,11,12,19,20,[26][27][28][29][30][31] and male sex 7,16,20,29 ), (2) presence of baseline functional impairments (e.g., poor PS 7,12,15,19,26,30,32 and reduced mobility 12 ), (…”

“…disease-specific and laboratory characteristics (ISS stages II and III, 7,11,12,26,28,30,33 renal impairment, 7,12,14,28,31,32 elevated β 2 -microglobulin levels, 4,13,15,19 elevated LDH, 7,11,13,15,16,33 hypercalcemia, 16,26,30 hypoalbuminemia, 4,13,15,16 hemoglobin, 7 and platelets, 12,14 ), presence of medical comorbidities (low body mass index, 10,14 history of coronary artery disease, 32 hypertension, 12,27 respiratory disease, 28 liver disease, 28 hepatitis C, 28 and overall higher comorbidity scores 14 ), (4) barriers to health care access (low household income 29 and geographic region 20,29 ), and (5) therapy-related characteristics (e.g., absence of novel therapy use). 4,7 Although high-risk cytoge...…”