Comparison of Troponin T and N-Terminal-Pro-Brain Natriuretic Peptides In Two Models of Treatment Related Mortality In AL Amyloidosis Patients Following Autologous Stem Cell Transplantation
Authors: Leung Nelson , Kumar Shaji , Dispenzieri Angela , Lacy Martha , Buadi Francis , Dingli David , Hogan William J , Gastineau Dennis , Gertz Morie 
Published: October 2019

Abstract
Abstract 3572
High dose melphalan and autologous stem cell transplantation (ASCT) is an effective treatment for light chain amyloidosis (AL) but high treatment related mortality (TRM) limits its use. Both cardiac troponin T (cTnT) and N-terminal-pro-brain natriuretic peptide (NT-pro-BNP) are sensitive predictors of high risk patients. Aim of this study is to compare two models of TRM, one using cTnT and the other BNP. Patients who underwent ASCT between 7/1996 and 7/2009 at the Mayo Clinic were analyzed retrospectively. Variables were chosen for ease of measurement and reproducibility thus echocardiographic parameters were excluded. Each additional risk factor must contribute to a significant increase in TRM. Also, since NT-pro-BNP and cTnT are already highly associated with mortality, the other risk factors must be independent predictors. Univariate analysis revealed serum albumin, B-2-microglobulin, cTnT, NT-pro-BNP, and uric acid were associated with TRM while age, sex, alkaline phosphatase, serum creatinine, CRP, proteinuria were not (Table 1). Multivariate analysis showed albumin and uric acid were independent risk factors to cTnT and NT-pro-BNP (Table 2). Of the 412 patients, 347 (8.9% TRM) could be evaluated by the cTnT, albumin and uric acid (TAU) model and 282 (9.2%) TRM could be evaluated by the NT-pro-BNP, albumin, uric acid (BAU) model. Both models showed increasing TRM with additional risk factors beyond 1 risk factor (Table 3 and 4). Albumin and uric acid were found to be significant contributors in both models. Patients with elevated cTnT alone had a 0% TRM vs 23.4% if they had 1 additional risk factor, p = 0.03. Similarly, TRM was 3.7% in patients with elevated NT-pro-BNP alone but 18.0% if they had 1 other risk factor, p = 0.05. NT-pro-BNP and cTnT could not be used in the same model because cTnT becomes non-significant. Both models were superior to visceral organ involvement which had TRM of 5.3% with 1, 9.2% with 2 and 16.7% with 3 organ involvement. Both models were also predictive of overall survival (Figure 1 and Figure 1b). Our results show that these models show that albumin and uric acid with either cTnT or NT-pro-BNP can accurately predict TRM in AL patients undergoing ASCT. Multivariate analysis shows that the NT-pro-BNP containing model may be superior since cTnT loses it significance when added to the model. Our results suggest these models could be very helpful in identifying high risk patients not suitable for ASCT.
Figure 1a Overall survival evaluated by Kaplan-Meier method on 347 patients using the TAU model. Mortality increased significantly with the number of risk factors, p < 0.001. Solid line represents patients with 0 risk factors, dotted line represents patients with 1 risk factor, dashed line represents those with 2 risk factors and dotted dashed line represent those with 3 risk factors. Figure 1a. Overall survival evaluated by Kaplan-Meier method on 347 patients using the TAU model. Mortality increased significantly with the number of risk factors, p < 0.001. Solid line represents patients with 0 risk factors, dotted line represents patients with 1 risk factor, dashed line represents those with 2 risk factors and dotted dashed line represent those with 3 risk factors.
Figure 1b Overall survival evaluated by Kaplan-Meier method on 282 patients using the BAU model. Overall survival was significantly longer in patients with less risk factors, p < 0.001. Solid line represents patients with 0 risk factors, dotted line represents patients with 1 risk factor, dashed line represents those with 2 risk factors and dotted dashed line represent those with 3 risk factors. Figure 1b. Overall survival evaluated by Kaplan-Meier method on 282 patients using the BAU model. Overall survival was significantly longer in patients with less risk factors, p < 0.001. Solid line represents patients with 0 risk factors, dotted line represents patients with 1 risk factor, dashed line represents those with 2 risk factors and dotted dashed line represent those with 3 risk factors.
Table 1. Patient characteristics Control TRM p-value N 318 29 Age 58 (25–73) 55 (35–75) 0.54 Sex (male) 58.2% 72.4% 0.13 Albumin (g/dl) 2.7 (0.8–4.4) 2.2 (0.8–3.7) 0.02 Alkaline phosphatase (U/L) 89 (28–1014) 86 (31–1394) 0.22 b2m (mg/ml) 2.6 (1.0–35.1) 3.2 (1.3–15.2) 0.007 NT-pro-BNP (pg/ml) 564 (12–35000) 1661 (21–35001) 0.03 CRP (mg/L) 0.3 (0.01–16.6) 0.3 (0.05–3.2) 0.34 cTnT (ng/ml) 0.01 (0–1.0) 0.03 (0.01–0.22) <0.001 Scr (mg/dl) 1.1 (0.4–12) 1.2 (0.8–2.3) 0.09 Proteinuria (g/d) 3.4 (0.01–35.4) 5.1 (0.1–20.5) 0.10 Uric acid (mg/dl) 6.2 (1–14.8) 7.5 (3.7–13.2) 0.02 Organ Involvement 1 94.7% 5.3% 0.04 2 90.7% 9.3% 3 83.3% 16.7%
Table 2. Multivariate analysis using proportional hazard model cutoffs >Risk Ratio >p-value Model TAU     cTnT 0.04 ng/ml 2.78 0.01     Albumin 3.3 g/dl 3.11 0.03     Uric acid 8.5 mg/dl 2.69 0.02 Model BAU     NT-pro-BNP 1270 pg/ml 3.77 0.003 Albumin 3.3 g/dl 3.53 0.04 Uric acid 8.5 mg/dl 3.11 0.03
Table 3. Model of cTnT, albumin and uric acid TAU Risk Factors n TRM p-value Risk Ratio 0 69 2.9% 1 205 5.9% 0.33 2 61 16.4% 0.009 2.98 3 12 41.7% 0.05 8.52
Table 4. Model of NT-pro-BNP, albumin and uric acid BAU Risk Factors >n >TRM >p-value >Risk Ratio 0 28 0% 1 161 5.6% 0.20 2 82 14.6% 0.02 2.7 3 11 45.5% 0.01 10.8

Disclosures:
Kumar: Celgene: Consultancy, Research Funding; Millennium: Research Funding; Merck: Consultancy, Research Funding; Novartis: Research Funding; Genzyme: Consultancy, Research Funding; Cephalon: Research Funding. Dispenzieri:Celgene: Honoraria, Research Funding; Binding Site: Honoraria. Lacy:Celgene: Research Funding.