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Enregistrement 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 sur OpenAlexaffabout

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMathematics
ThématiqueAdvanced Causal Inference Techniques
Établissements canadiensUniversity of British ColumbiaHôpital Maisonneuve-RosemontUniversity Health NetworkSaskatchewan Cancer AgencyPrincess Margaret Cancer CentreUniversity of CalgaryMcMaster UniversityUniversity of TorontoHamilton Health SciencesUniversité de MontréalUniversité LavalBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésPropensity score matchingConfoundingRandomized controlled trialClinical trialStatistical significanceSample size determinationMeta-analysisRetrospective cohort study

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,027

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,133
Tête enseignante GPT0,369
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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