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. 2023 Dec;203(5):840-851.
doi: 10.1111/bjh.19055. Epub 2023 Aug 24.

The Simplified Comorbidity Index predicts non-relapse mortality in reduced-intensity conditioning allogeneic haematopoietic cell transplantation

Affiliations

The Simplified Comorbidity Index predicts non-relapse mortality in reduced-intensity conditioning allogeneic haematopoietic cell transplantation

Shlomo Elias et al. Br J Haematol. 2023 Dec.

Abstract

Comorbidity assessment before allogeneic haematopoietic cell transplantation (allo-HCT) is essential for estimating non-relapse mortality (NRM) risk. We previously developed the Simplified Comorbidity Index (SCI), which captures a small number of 'high-yield' comorbidities and older age. The SCI was predictive of NRM in myeloablative CD34-selected allo-HCT. Here, we evaluated the SCI in a single-centre cohort of 327 patients receiving reduced-intensity conditioning followed by unmanipulated allografts from HLA-matched donors. Among the SCI factors, age above 60, mild renal impairment, moderate pulmonary disease and cardiac disease were most frequent. SCI scores ranged from 0 to 8, with 39%, 20%, 20% and 21% having scores of 0-1, 2, 3 and ≥4 respectively. Corresponding cumulative incidences of 3-year NRM were 11%, 16%, 22% and 27%; p = 0.03. In multivariable models, higher SCI scores were associated with incremental risks of all-cause mortality and NRM. The SCI had an area under the receiver operating characteristic curve of 65.9%, 64.1% and 62.9% for predicting 1-, 2- and 3-year NRM versus 58.4%, 60.4% and 59.3% with the haematopoietic cell transplantation comorbidity index. These results demonstrate for the first time that the SCI is predictive of NRM in patients receiving allo-HCT from HLA-matched donors after reduced-intensity conditioning.

Keywords: Simplified Comorbidity Index; allogeneic haematopoietic cell transplantation; comorbidity; non-relapse mortality.

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Conflict of interest statement

Disclosure of Conflicts of Interest

Samantha Brown received a salary support from AACR Project GENIE Biopharma Collaborative. Sergio Giralt receives research funding from Miltenyi Biotec, Takeda Pharmaceutical Co., Celgene Corp., Amgen Inc., Sanofi, Johnson and Johnson, Inc., Actinium Pharmaceuticals, Inc., and is on the Advisory Boards for: Kite Pharmaceuticals, Inc., Celgene Corp., Sanofi, Novartis, Johnson and Johnson, Inc., Amgen Inc., Takeda Pharmaceutical Co., Jazz Pharmaceuticals, Inc., Actinium Pharmaceuticals, Inc. Ioannis Politikos serves as a DSMB member for ExcellThera, has received research support from Merck, and honoraria from Precisionheor. Oscar B Lahoud served on the Advisory board for MorphoSys, Inc. Miguel-Angel Perales reports honoraria from Adicet, Allovir, Caribou Biosciences, Celgene, Bristol-Myers Squibb, Equilium, Exevir, Incyte, Karyopharm, Kite/Gilead, Merck, Miltenyi Biotec, MorphoSys, Nektar Therapeutics, Novartis, Omeros, OrcaBio, Syncopation, VectivBio AG, and Vor Biopharma. He serves on DSMBs for Cidara Therapeutics, Medigene, and Sellas Life Sciences, and the scientific advisory board of NexImmune. He has ownership interests in NexImmune, Omeros and OrcaBio. He has received institutional research support for clinical trials from Incyte, Kite/Gilead, Miltenyi Biotec, Nektar Therapeutics, and Novartis. Doris M Ponce has served as advisory board member for Evive Biotechnology (Shanghai) Ltd (formerly Generon [Shanghai] Corporation Ltd), she served as advisory board member or consultant of Sanofi Corporation, CareDx, Ceramedix, Incyte, and receives research funding from Takeda Corporation and Incyte. Gunjan Shah received research funding from Janssen, Amgen, BMS, and Beyond Spring and serves on DSMB for Arcellx. Michael Scordo served as a paid consultant for McKinsey & Company, Angiocrine Bioscience, Inc., and Omeros Corporation; received research funding from Angiocrine Bioscience, Inc., Omeros Corporation, and Amgen, Inc.; served on ad hoc advisory boards for Kite – A Gilead Company; and received honoraria from i3Health and Medscape for CME-related activity.

Figures

Figure 1.
Figure 1.. Prevalence and correlation between comorbidities in the patient cohort.
(A) Prevalence of different comorbidities in the patient cohort. (B) Co-incidence of pairs of comorbidities across the study cohort, as measured by Spearman’s correlation coefficient. Positive correlations are color-coded in blue and negative correlations in red. DM – diabetes mellitus; IBD – inflammatory bowel disease; HCT-CI - The hematopoietic cell transplantation comorbidity index; SCI – simplified comorbidity index.
Figure 2.
Figure 2.. Non-relapse mortality (NRM) associated with renal and pulmonary comorbidities.
(A) Cumulative incidence of NRM in patients with different degrees of renal (upper panel) and pulmonary (lower panel) function. (B) Association of continuous variables which are shown on the x-axis and NRM, as analyzed by spline analysis.
Figure 3.
Figure 3.. Distribution of the SCI and HCT-CI scores and associated outcomes.
(A-B) Distribution of the and SCI (A) and HCT-CI (B) scores in our patient cohort. (C-D) Association of the SCI (C) and HCT-CI (D) with NRM. (E-F) Association of the SCI (E) and HCT-CI (F) and with OS.

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