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Enregistrement W3000872688 · doi:10.1016/j.bbmt.2019.12.744

CT-Defined Fat Index Is a Prognostic Factor of Chronic Graft-Versus-Host Disease Outcomes in Adult Allogeneic Transplant Recipients

2020· article· en· W3000872688 sur OpenAlexaff
Asmita Mishra, Ram Thapa, Kevin Bigam, Martine Extermann, Rawan Faramand, Farhad Khimani, Xuefeng Wang, Vickie E. Baracos, Joseph A. Pidala

Notice bibliographique

RevueBiology of Blood and Marrow Transplantation · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueBone and Joint Diseases
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineLymphomaRetrospective cohort studyGastroenterologyHematopoietic stem cell transplantationHounsfield scaleGraft-versus-host diseaseTransplantationSurgeryComputed tomography

Résumé

récupéré en direct d'OpenAlex

BackgroundAdditional tools for risk-stratification of chronic graft vs. host disease (cGVHD) may enhance the established NIH consensus-based severity score. Radiographic body composition metrics arising from standardly performed CT-scans have previously shown significant association with treatment complications in other cancer populations. We aimed to characterize skeletal muscle (SM) and adiposity in cGVHD patients to discern potential association with subsequent mortality.MethodsA consecutive retrospective series of patients who underwent allogeneic hematopoietic cell transplantation (HCT) at our center from 2005-2016 and had the following criteria were evaluated: 1) diagnosis of either non-Hodgkin (NHL) or Hodgkin Lymphoma (HL) to enrich for available CT, and 2) had a history of cGVHD. Skeletal muscle index (SMI) and fat index (FI) were quantified on CT imaging at the 3rd lumbar (L3) and 4th thoracic (T4) vertebra. SM Hounsfield units (HU) were obtained to evaluate SM density. Cut points for SMI and FI were done via gender specific optimal stratification.ResultsA total of n=115 patients met the inclusion criteria for this analysis. The median age was 52 (range 22-69), and patients were predominantly male (n=71, 62%) and diagnosed with NHL (n=110, 96%). Onset cGVHD NIH overall severity was mild in N= 56 (49%), moderate in 44 (38%), and severe in 15 (13%). When considering all body composition parameters, high L3 fat index (FI) at D100 was associated with worsened OS (HR 2.83, 95% CI 1.30, 6.12, p=0.008), but SMI and SM HU were not associated with OS (p=NS). In multivariate analysis, high L3 FI was independently associated with increased mortality (HR 2.29, 95% CI 1.03-5.1, p=0.043) (Table 1), while SMI and HU were not (p=NS). In secondary analysis, we quantified the change in body composition in evaluable patients (n=72). Cachexia development was observed from pre- to post-HCT (median % weight change -5.5 [-50.1-13.3]. The majority of patients had decline in both FI and SMI (Figure 1). These changes were also seen in CT chest with high correlation between SMI and FI when comparing CT abdomen chest r=0.854 and r=0.798 respectively.ConclusionsIncreased FI is associated with worsened overall survival in cGVHD patients, while SMI and HU are not. These findings suggest that low skeletal muscle mass alone does not predict for poor outcomes in cGVHD as previously described in other cancers. Body composition analysis in a larger cGVHD cohort is needed to confirm these findings and examine risk-stratification within NIH severity groups. Additional tools for risk-stratification of chronic graft vs. host disease (cGVHD) may enhance the established NIH consensus-based severity score. Radiographic body composition metrics arising from standardly performed CT-scans have previously shown significant association with treatment complications in other cancer populations. We aimed to characterize skeletal muscle (SM) and adiposity in cGVHD patients to discern potential association with subsequent mortality. A consecutive retrospective series of patients who underwent allogeneic hematopoietic cell transplantation (HCT) at our center from 2005-2016 and had the following criteria were evaluated: 1) diagnosis of either non-Hodgkin (NHL) or Hodgkin Lymphoma (HL) to enrich for available CT, and 2) had a history of cGVHD. Skeletal muscle index (SMI) and fat index (FI) were quantified on CT imaging at the 3rd lumbar (L3) and 4th thoracic (T4) vertebra. SM Hounsfield units (HU) were obtained to evaluate SM density. Cut points for SMI and FI were done via gender specific optimal stratification. A total of n=115 patients met the inclusion criteria for this analysis. The median age was 52 (range 22-69), and patients were predominantly male (n=71, 62%) and diagnosed with NHL (n=110, 96%). Onset cGVHD NIH overall severity was mild in N= 56 (49%), moderate in 44 (38%), and severe in 15 (13%). When considering all body composition parameters, high L3 fat index (FI) at D100 was associated with worsened OS (HR 2.83, 95% CI 1.30, 6.12, p=0.008), but SMI and SM HU were not associated with OS (p=NS). In multivariate analysis, high L3 FI was independently associated with increased mortality (HR 2.29, 95% CI 1.03-5.1, p=0.043) (Table 1), while SMI and HU were not (p=NS). In secondary analysis, we quantified the change in body composition in evaluable patients (n=72). Cachexia development was observed from pre- to post-HCT (median % weight change -5.5 [-50.1-13.3]. The majority of patients had decline in both FI and SMI (Figure 1). These changes were also seen in CT chest with high correlation between SMI and FI when comparing CT abdomen chest r=0.854 and r=0.798 respectively. Increased FI is associated with worsened overall survival in cGVHD patients, while SMI and HU are not. These findings suggest that low skeletal muscle mass alone does not predict for poor outcomes in cGVHD as previously described in other cancers. Body composition analysis in a larger cGVHD cohort is needed to confirm these findings and examine risk-stratification within NIH severity groups.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,086
Score d'incertitude au seuil0,572

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0000,000

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 Opus0,020
Tête enseignante GPT0,260
Écart entre enseignants0,240 · 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 tête enseignante, 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
Publié2020
Routes d'admission1
Résumé présentoui

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