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Record 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 on OpenAlexaff
Asmita Mishra, Ram Thapa, Kevin Bigam, Martine Extermann, Rawan Faramand, Farhad Khimani, Xuefeng Wang, Vickie E. Baracos, Joseph A. Pidala

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineInternal medicineLymphomaRetrospective cohort studyGastroenterologyHematopoietic stem cell transplantationHounsfield scaleGraft-versus-host diseaseTransplantationSurgeryComputed tomography

Abstract

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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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.086
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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 teacher head, 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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Published2020
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