Development of a Risk Prediction Model for 6-Month Early Mortality in Patients with Systemic Light Chain Amyloidosis Treated with Daratumumab-Based Frontline Therapy

Background:

Despite substantial improvements in early mortality (EM) for patients with systemic light chain amyloidosis (AL), rates remained high in the bortezomib-era, at approximately 25% (Muchtar et al. Blood. 2017). However, the therapeutic landscape in AL amyloidosis has changed with the ANDROMEDA trial, which led to the approval of daratumumab in combination with bortezomib, cyclophosphamide, and dexamethasone (Dara-VCd) in the frontline setting. The incidence and predictors of EM in the context of daratumumab-based frontline therapy have not been studied.

Methods:

We performed an international multicenter retrospective cohort study of all consecutive patients with newly diagnosed AL treated with frontline Dara-VCd or Dara-Vd, and followed until 12/31/2023. EM was defined as death from any cause within 6 months of treatment initiation. Our primary objective was to identify the rate and significant predictors of EM. Univariate logistic regression was used to identify significant predictors of EM, which were then included in a multivariate logistic regression model. The receiver operator characteristic (ROC) analysis was used to determine the best threshold values that discriminated EM rates for the following continuous variables: age, dFLC (involved-uninvolved), eGFR, NT-proBNP, albumin, and high-sensitivity Troponin-T (hs-TnT). Other variables tested in univariate analysis were the following: ECOG performance status, NYHA functional class, sex, race, light chain isotype, percent bone marrow plasma cells, status of the following cytogenetic abnormalities—t(11;14), +1q, hyperdiploidy, del(13q), high-risk translocations, and del(17p), and the number of organs involved (2 vs 1). To build the clinical prediction score, each variable was weighted according to the β coefficient (natural logarithm of odds ratio) obtained from the multivariable regression. The β coefficients were rounded to the nearest 0.5 and multiplied by 2 to obtain an even number.

Results

The study cohort included 335 AL patients treated with daratumumab-based frontline therapy (92.2% Dara-VCd, 7.8% Dara-Vd), with all surviving patients having at least 6 months of follow-up. The median age at diagnosis was 66.5 years (range, 59.4 – 71.3), and 6.7% (n = 22) were Non-Hispanic blacks. Median dFLC at diagnosis was 22.91 mg/dL (range, 0.2-1093.4), and median NT-proBNP was 2415 pg/mL (range, 13-70,000). Mayo 2004 stages IIIA/IIIB represented 44% of the total cohort, with 17% having ECOG ≥ 2 and 25.4% having NYHA Class III/IV symptoms.

The cumulative incidence of EM was 10.7%, with the incidence in patients with Mayo 2004 stage I, II, IIIA, and IIIB being 0%, 8%, 12.5%, and 25% respectively. On univariate analysis with unadjusted odds ratio (OR), the following variables significantly predicted EM: Age ≥ 75 (OR=2.8, p=0.013) ECOG ≥ 2, (OR 4.9, p<0.001), NYHA Class ≥ III (OR=4.95, p<0.001), dFLC ≥9 mg/dL (OR, 10.18, p=0.023), eGFR <33 mL/min/1.73 m2 (OR=2.7, p=0.026), NT-proBNP >5276 pg/mL (OR=7.69, p <0.001), hs-TnT >70 ng/L (OR=5.88, p<0.001), and absence of t(11; 14) (OR=2.33, p=0.041). These variables were entered into multivariate regression, except for ECOG and NYHA, which were combined to create a single variable (values ranging from 0-8), and hs-TnT was dropped due to substantial missing data as centers used different troponin assays. NT-proBNP was rounded to 5300 pg/ml for ease of clinical application. Based on the multivariate analysis, the following scores were assigned to each variable: Age ≥75 (2.0), ECOG+NYHA (1.0 x combined score), NT-proBNP >5,300 pg/ml (2.0), and absence of t(11;14) (3.0). This led to a score ranging from 1-15, and two risk-groups: low-risk (score <9; 87.5%) and high-risk (score ≥9; 12.5%). The respective cumulative incidence of EM was 6.1% versus 50% (p<0.001). The c-index of the model was 0.725 (95% CI, 0.676-0.774).

Conclusion:

Using variables that are readily available in the clinic, we have developed a two-tiered risk-prediction model for EM in AL amyloidosis in the context of daratumumab-based frontline therapy. The model needs to be externally validated in an independent dataset.