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Record W7108721199 · doi:10.1182/blood-2025-6025

Propensity score matching analysis comparing the efficacy and long-term outcomes of belumosudil to the best available treatment as a historical control, used as second-line therapy or beyond for chronic GVHD after steroid failure.

2025· article· en· W7108721199 on OpenAlexaffabout

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsUniversity of British ColumbiaHôpital Maisonneuve-RosemontUniversity Health NetworkSaskatchewan Cancer AgencyPrincess Margaret Cancer CentreUniversity of CalgaryMcMaster UniversityUniversity of TorontoHamilton Health SciencesUniversité de MontréalUniversité LavalBC Cancer Agency
Fundersnot available
KeywordsPropensity score matchingConfoundingRandomized controlled trialClinical trialStatistical significanceSample size determinationMeta-analysisRetrospective cohort study

Abstract

fetched live from OpenAlex

Abstract Introduction ROCKstar study demonstrated that Belumosudil (BEL), a selective Rho-associated coiled-coil kinase 2 (ROCK2) inhibitor, has clinical efficacy in managing chronic graft-versus-host disease (cGvHD) in patients who failed 2nd line therapy or beyond. Multiple studies based on real-world experiences reported similar results of improved overall response rate and failure-free survival (FFS). A prospective randomized controlled trial (RCT) comparing BEL with best available therapy (BAT) as a control is still lacking. Propensity-score matching (PSM) analysis is a statistical methodology balancing out a bias coming from imbalanced distribution of patient characteristics at baseline between the variable of interest (e.g., treatment option). Thus, it could mimic RCT by comparing treatment outcomes indirectly after balancing biased covariates. The present study compared treatment outcomes between BEL-treated patients and cGvHD patients treated with BAT as a historical cohort. PSM was applied to control for biased confounding variables between the two groups. FFS, OS, and steroid dose reduction were evaluated as statistical endpoints. Patients and methods We retrospectively analyzed treatment outcomes in a total of 523 patients treated at second line or beyond, including 216 pts treated with BEL collected from 3 countries (Canada, Spain, and Germany) and 307 treated with BAT. For the BAT group as a historical control, we retrieved the clinical data of patients who developed chronic GvHD and were treated at Princess Margaret Cancer Centre between 2006 and 2014 before novel agents were available: 163 treatments (53.1%), 77 (25.1%), 36 (11.1%), and 33 (10.7%) were given as 2nd, 3rd, 4th, and ≥5th line, respectively. Treatment included prednisone in 284 (92.5%), mycophenolate in 145 (47.2%), azathioprine (AZA) in 144 (46.9%), a calcineurin inhibitor in 54 (17.6%), hydroxychloroquine in 51 (16.6%), extracorporeal photopheresis in 20 (6.5%), and rituximab in 10 (3.3%). A propensity score was calculated from the following unbalanced clinical factors: age (≥60 vs. <60), GvHD severity (severe vs. mild/moderate), HCT-CI score (≥3 vs. <3), and treatment line (≥4th vs. <4th). We extracted 84 patients (42 in each group) for comparison between BEL and BAT groups after balancing clinical factors. Results With a median follow-up in survivors of 16.3 months (0–102), the BEL group were older (34.3% vs. 17.9% ≥60 years, p<0.001), more frequent with severe cGvHD (80.1% vs. 19.2%, p<0.001), and at 4th line of treatment or beyond (75.9% vs. 21.8%, p<0.001) compared to the BAT group; patients in the BAT group had a higher HCT-CI score (35.7% vs. 16.8% ≥3, p<0.001). In terms of 12 months’ FFS rate, BEL group showed a 66.8% [58.9–73.5] vs 39.7% [33.7–45.7] in BAT group (p<0.001), whereas 12 months’ OS rates were 92.6% [86.6–95.9] and 84.9% [79.4–89.0] (p=0.006), respectively. No differences were found for FFS (p=0.400) or OS (p=0.126) when comparing patients who received AZA vs. those who did not in BAT group. At months 0, 3, and 6, 45.5% (46.2% vs. 0.7%, p<0.001), 45.3% (45.3% vs. 0%, p<0.001), and 35.1% (37.1% vs. 0%, p<0.001) more patients in BEL group could discontinue prednisone compared to BAT group, respectively After the PSM subgroup, no differences were found between the BEL vs. BAT group for age (42.9% vs. 45.2%, p=1), severe cGvHD (38.1% in both, p=1), HCT-CI ≥3 (16.7% vs. 14.3%, p=1), or 4th line of treatment and beyond (33.3% vs. 31.0%, p=1). The BEL group showed 72.0% [55.0–83.4] 12 months’ FFS rate vs 25.3% [10.6–43.1] for BAT (p<0.001), whereas 12 months’ OS rates were 92.1% [77.3–97.4] and 88.4% [60.8–97.0] (p=0.317), respectively. Both univariate (UVA) and multivariate analysis (MVA) (BEL vs. BAT, HCT-CI ≥3, severe cGvHD, age ≥ 60, and previous acute GvHD) for FFS confirmed BEL superiority over BAT (hazard ratio (HR) 0.288 [0.155–0.535], p<0.001; no differences for the other factors). For OS, UVA showed that BEL had a trend for a higher OS (HR 0.282, p=0.067), while severe cGvHD (HR 3.719, p=0.058) for lower OS; no differences were found in MVA. In the PSM subgroup, 33.3% (33% vs. 0%, p<0.001) and 47.1% (50% vs. 2.9%, p<0.001) more patients in the BEL group could discontinue prednisone at months 3 and 6, respectively, compared to the BAT group. Conclusion In conclusion, the current study confirmed that BEL was superior to BAT as second-line therapy or beyond in cGvHD patients after therapy failure concerning FFS and steroid tapering.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.133
GPT teacher head0.369
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
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