Muscle Depletion Is an Important and Clinically Relevant Predictor of Outcomes after Autologous Hematopoietic Cell Transplantation
Notice bibliographique
Résumé
Abstract Introduction: High intensity treatments such as autologous hematopoietic cell transplantation (HCT) can be curative for patients with relapsed/refractory lymphoma (Hodgkin [HL], non-Hodgkin [NHL]), but this needs to be balanced by the risk of non-relapse mortality (NRM) associated with HCT and potentially sub-optimal disease response with less intensive treatments. Measures of pre-treatment body composition such as quantity and quality of muscle are prognostic in patients with solid tumors, but their association with post-HCT outcomes is unknown. We examined the prognostic significance of muscle depletion prior to HCT, defined by having both low muscle quantity (lumbar skeletal muscle index [SMI]) and quality (muscle attenuation [MA]) on computed tomography (CT) imaging, in a population-based cohort of patients undergoing autologous HCT for lymphoma. Next, we examined the prognostic significance of muscle depletion after HCT in a subset of patients with normal muscle composition prior to HCT, allowing us to examine the impact of change in body composition over time. Methods: 440 consecutive patients with lymphoma, age ≥18y, who underwent a first HCT between 2009 and 2014 at a single institution were included in the study. Measures of muscle quantity (SMI) and quality (MA) were ascertained from pre- and post-HCT abdominal CT scans using image analysis software (SliceOmatic; Tomovision, Quebec, Canada). SMI was calculated as the ratio of skeletal muscle area (cm2) divided by height (m)2. Sex and body mass index (BMI)-specific cutoff values of low SMI and MA were used to identify patients with muscle depletion (J Clin Oncol 2013 31:1539). Measurements were made by trained researchers blinded to patient demographics and HCT outcome (Figure 1); 3rd lumbar vertebra was used as a landmark because of its high correlation with whole-body muscle mass (J Clin Oncol 2016 34:1339). This report is limited to 321 (73%) patients with CT scans performed ≤90 days from HCT. Cumulative incidence of NRM was calculated taking into consideration competing risk of disease-related mortality. Kaplan-Meier method was used to examine overall survival (OS). Multivariable Cox regression analysis was used to calculate the hazard ratio (HR) estimates and 95% confidence intervals (CI), adjusted for relevant covariates (demographics, diagnosis, pre-HCT Karnofsky performance score [KPS] and comorbidity index [HCT-CI]). Results: Sixty-two (19.3%) patients had muscle depletion pre-HCT. Median age at HCT was 53y (range: 18-78); 62.0% were male; 54.0% were non-Hispanic white; Diagnoses: HL (N=84 [26.2%]), NHL (N=237 [73.8%]); KPS ≤80 (N=87 [27.1%]); HCT-CI ≥3 (N=52 [16.2%]). Impact of pre-HCT muscle depletion: Patients with pre-HCT muscle depletion had significantly worse 5-y OS (56.4% vs. 77.8%, p<0.001; Figure 2) and higher NRM (11.4% vs. 5.1%, p=0.05) when compared to those with normal body composition. OS was especially poor for patients who were obese (BMI ≥30 kg/m2) and had muscle depletion (20.0% vs. 77.1%, p<0.001) pre-HCT. Median length of hospitalization was also significantly longer (27d vs. 23d; p=0.03) among patients with muscle depletion. Muscle depletion was associated with a 2.2-fold (HR=2.2 [CI: 1.0-4.5]) risk of NRM and 1.8-fold (HR=1.8 [CI: 1.1-3.1]) risk of all-cause mortality when compared to those with normal body composition. Impact of post-HCT muscle depletion: Among 223 patients with normal body composition prior to HCT, 24 (9.3%) developed muscle depletion after HCT, detected at a median 63d (range 27-165) from HCT. In these patients, there was a 3-fold (HR=3.1 [CI: 1.5-6.4]) risk of all-cause mortality compared to those who maintained normal muscle composition throughout HCT. Conclusion: Muscle depletion is an important and independent predictor of outcomes after HCT, with potential additional downstream impacts on health-economic outcomes such as length of hospitalization and the burden of chronic morbidity in long-term survivors. Taken together, these data form the basis for real-time decision making prior to HCT (e.g. pre-habilitation, less intensive treatment approaches), or during HCT (e.g. dietary optimization, increased supportive care services, resistance training), setting the stage for innovative strategies to improve outcomes after HCT. Disclosures Chen: Affimed: Research Funding; Merck & Co., Inc.: Consultancy, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Genentech Inc.: Consultancy; Millennium Pharmaceuticals: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding. Forman:Mustang Therapeutics: Other: Licensing Agreement, Patents & Royalties, Research Funding.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».