Incidence, clinical presentation, risk factors, outcomes, and biomarkers in de novo late acute GVHD

Abstract

Late acute GVHD is defined as de novo acute GVHD presenting beyond 100 days after allogeneic hematopoietic cell transplantation (HCT) without manifestations of chronic GVHD. Data are limited regarding its characteristics, clinical course, and risk factors due to under recognition and changes in classification. We evaluated 3542 consecutive adult recipients of their first HCT at twenty-four Mount Sinai Acute GVHD International Consortium (MAGIC) centers between January 2014 and August 2021 to better describe the clinical evolution and outcomes of late acute GVHD. The cumulative incidence of classic acute GVHD that required systemic treatment was 35.2% and an additional 5.7% of patients required treatment for late acute GVHD. At the onset of symptoms, late acute GVHD was more severe than classic acute GVHD by both clinical and MAGIC Algorithm Probability (MAP) biomarker parameters, and showed a lower overall response rate at day 28. Both clinical and biomarker grading at the time of treatment stratified the risk of NRM in patients with classic and late acute GVHD, respectively, but long-term NRM and OS did not differ between patients with classic and late acute GVHD. Advanced age, female to male sex mismatch, and the use of reduced intensity conditioning were associated with development of late acute GVHD, whereas use of post-transplant cyclophosphamide-based GVHD prevention was protective mainly due to shifts in GVHD timing. As overall outcomes were comparable, our findings, while not definitive, suggest that similar treatment strategies, including eligibility for clinical trials, based solely on clinical presentation at onset are appropriate.

Flares of acute graft-versus-host disease: a Mount Sinai Acute GVHD International Consortium analysis

“…At flare onset, MAGIC biomarkers also successfully stratify patients for the risk of NRM similarly to their performance in de novo acute GVHD. 23 , 24 , 25 , 41 These findings are consistent with prior studies showing that biomarkers can predict outcomes at specified time points after initiation of treatment (eg, day 28) 13 and emphasize that the MAP reflects subclinical damage to the GI crypts when clinical symptoms are well controlled.…”

“…Biomarker scores predict NRM at several time points other than GVHD onset, 13 , 22 , 24 and we, therefore, assessed their predictive value at the time of flare. Serum samples were available in 98 of 210 patients (47%) at the time of flare.…”

“… 21 , 22 , 23 The MAGIC algorithm probability (MAP) was calculated as a single value between 0.001 and 0.999 according to the formula: log(–log[1 – MAP]) = –11.263 + 1.844(log 10 ST2) + 0.577(log 10 REG3α) and validated thresholds for Ann Arbor (AA) scores were used (AA1 < 0.14; 0.14 ≤ AA2 < 0.29; AA3 ≥ 0.29). 18 , 21 , 22 , 24 , 25 , 26 …”

“…We included the following variables as potential risk factors for GVHD flare in a multivariate analysis: maximum GVHD grade, maximum steroid dose before CR/VGPR, time to CR/VGPR, use of immunosuppressive agents other than steroids before CR/VGPR, response to treatment, and serum biomarkers MAP at CR/VGPR. We included additional covariates in the multivariate analysis based on their known prognostic significance on GVHD outcomes: 24 , 28 , 29 recipient age at HCT, sex mismatch, donor type, GVHD prophylaxis, HCT-CI, use of antithymocyte globulin or alemtuzumab, and conditioning regimen intensity. In the multivariate analysis which treated a flare as a time-dependent covariate for NRM, we reduced the number of covariates because of the small number of events.…”

“…In the multivariate analysis which treated a flare as a time-dependent covariate for NRM, we reduced the number of covariates because of the small number of events. These included maximum GVHD grade, maximum steroid dose before CR/VGPR, timing of achievement of CR/VGPR, use of immunosuppressive agents other than steroids before CR/VGPR, response to treatment, recipient age, 24 and HCT-CI. 29 Correlations among the variables in the multivariate models were tested to avoid multicollinearity ( supplemental Table 2 ).…”