Novel MAGIC composite scores using both clinical symptoms and biomarkers best predict treatment outcomes of acute GVHD
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Novel MAGIC composite scores using both clinical symptoms and biomarkers best predict treatment outcomes of acute GVHD. / Akahoshi, Yu; Spyrou, Nikolaos; Weber, Daniela; Aguayo-Hiraldo, Paibel; Ayuk, Francis; Chanswangphuwana, Chantiya; Choe, Hannah K; Eder, Matthias; Etra, Aaron M; Grupp, Stephan A; Hexner, Elizabeth O; Hogan, William J; Kitko, Carrie L; Kraus, Sabrina; Al Malki, Monzr M; Merli, Pietro; Qayed, Muna; Reshef, Ran; Schechter, Tal; Ullrich, Evelyn; Vasova, Ingrid; Wölfl, Matthias; Zeiser, Robert; Baez, Janna; Beheshti, Rahnuma; Eng, Gilbert; Gleich, Sigrun; Katsivelos, Nikolaos; Kowalyk, Steven; Morales, George; Young, Rachel; Chen, Yi-Bin; Nakamura, Ryotaro; Levine, John E; Ferrara, James L M.
In: BLOOD, Vol. 144, No. 9, 29.08.2024, p. 1010-1021.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Novel MAGIC composite scores using both clinical symptoms and biomarkers best predict treatment outcomes of acute GVHD
AU - Akahoshi, Yu
AU - Spyrou, Nikolaos
AU - Weber, Daniela
AU - Aguayo-Hiraldo, Paibel
AU - Ayuk, Francis
AU - Chanswangphuwana, Chantiya
AU - Choe, Hannah K
AU - Eder, Matthias
AU - Etra, Aaron M
AU - Grupp, Stephan A
AU - Hexner, Elizabeth O
AU - Hogan, William J
AU - Kitko, Carrie L
AU - Kraus, Sabrina
AU - Al Malki, Monzr M
AU - Merli, Pietro
AU - Qayed, Muna
AU - Reshef, Ran
AU - Schechter, Tal
AU - Ullrich, Evelyn
AU - Vasova, Ingrid
AU - Wölfl, Matthias
AU - Zeiser, Robert
AU - Baez, Janna
AU - Beheshti, Rahnuma
AU - Eng, Gilbert
AU - Gleich, Sigrun
AU - Katsivelos, Nikolaos
AU - Kowalyk, Steven
AU - Morales, George
AU - Young, Rachel
AU - Chen, Yi-Bin
AU - Nakamura, Ryotaro
AU - Levine, John E
AU - Ferrara, James L M
N1 - © 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2024/8/29
Y1 - 2024/8/29
N2 - Acute graft-versus-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as the Minnesota risk identify standard and high-risk categories but lack a low-risk category suitable to minimize immunosuppressive strategies. We developed a new grading system that includes a low-risk stratum based on clinical symptoms alone and determined whether the incorporation of biomarkers would improve the model's prognostic accuracy. We randomly divided 1863 patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) who were treated for GVHD into training and validation cohorts. Patients in the training cohort were divided into 14 groups based on similarity of clinical symptoms and similar nonrelapse mortality (NRM); we used a classification and regression tree (CART) algorithm to create three Manhattan risk groups that produced a significantly higher area under the receiver operating characteristic curve (AUC) for 6-month NRM than the Minnesota risk classification (0.69 vs 0.64, P = .009) in the validation cohort. We integrated serum GVHD biomarker scores with Manhattan risk using patients with available serum samples and again used a CART algorithm to establish 3 MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs 0.70, P = .010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs 63% vs 30%, P < .001). We conclude that the MAGIC composite score more accurately predicts response to therapy and long-term outcomes than systems based on clinical symptoms alone and may help guide clinical decisions and trial design.
AB - Acute graft-versus-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as the Minnesota risk identify standard and high-risk categories but lack a low-risk category suitable to minimize immunosuppressive strategies. We developed a new grading system that includes a low-risk stratum based on clinical symptoms alone and determined whether the incorporation of biomarkers would improve the model's prognostic accuracy. We randomly divided 1863 patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) who were treated for GVHD into training and validation cohorts. Patients in the training cohort were divided into 14 groups based on similarity of clinical symptoms and similar nonrelapse mortality (NRM); we used a classification and regression tree (CART) algorithm to create three Manhattan risk groups that produced a significantly higher area under the receiver operating characteristic curve (AUC) for 6-month NRM than the Minnesota risk classification (0.69 vs 0.64, P = .009) in the validation cohort. We integrated serum GVHD biomarker scores with Manhattan risk using patients with available serum samples and again used a CART algorithm to establish 3 MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs 0.70, P = .010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs 63% vs 30%, P < .001). We conclude that the MAGIC composite score more accurately predicts response to therapy and long-term outcomes than systems based on clinical symptoms alone and may help guide clinical decisions and trial design.
KW - Humans
KW - Graft vs Host Disease/blood
KW - Biomarkers/blood
KW - Female
KW - Male
KW - Middle Aged
KW - Adult
KW - Prognosis
KW - Acute Disease
KW - Treatment Outcome
KW - Hematopoietic Stem Cell Transplantation/adverse effects
KW - Aged
KW - Algorithms
KW - Adolescent
KW - Young Adult
U2 - 10.1182/blood.2024025106
DO - 10.1182/blood.2024025106
M3 - SCORING: Journal article
C2 - 38968143
VL - 144
SP - 1010
EP - 1021
JO - BLOOD
JF - BLOOD
SN - 0006-4971
IS - 9
ER -
