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Body Composition Predicts Survival in Allogeneic Hematopoietic Stem Cell Transplantation (HSCT) Recipients

2015· article· en· W2587928021 on OpenAlexaffabout
Asmita Mishra, Binglin Yue, Martine Extermann, Claudio Anasetti, Heather Jim, Jongphil Kim, Joseph A. Pidala, Vickie E. Baracos

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAdipose tissueHematopoietic stem cell transplantationInternal medicineSkeletal muscleTransplantationBody mass indexProspective cohort studyQuartileOncologyConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Innovative means to risk-stratify HSCT patients are needed. Previous studies in cancer patients suggest association between body composition and survival. However, this has not been previously described is allogeneic HSCT recipients. Furthermore, the correlation of body composition with pre- and post-HSCT physical activity in cancer patients remains undefined. We postulated that body composition prior to HSCT is associated with post-HSCT outcomes. Methods: Patients who had completed pre-HSCT physical function assessment as part of ongoing prospective clinical trial were identified. Analysis of self-reported and measured physical activity and their association with outcomes after transplantation is currently ongoing. Regional CT at the 4th thoracic vertebra (T4) was obtained for post-hoc analysis of body composition within this cohort. Using Slice-O-Matic software V4.3 (Tomovision, Magog, Quebec, Canada), both adipose and muscle tissue were quantified to obtain the respective cross sectional area (cm2). Fat and skeletal muscle were identified and quantified within the following CT Hounsfield unit thresholds: -29 to +150 for skeletal muscle and -190 to -30 for adipose tissue. Tissue boundaries were manually corrected as necessary. Cross sectional areas were subsequently normalized for stature (height2) to obtain a tissue index for both fat and muscle (cm2/m2). For this analysis, fat index (FI) and muscle index (MI) were divided into quartiles (group 0: ≤25%, group 1: 26-50%, group 2: 51=75%, group 3: ≥75%). Statistical significance was defined as p < 0.05 and all analyses were done on SAS 9.3. Results: All patients (n=50) enrolled on the pre-HSCT functionality trial between 02/2014 and 02/2015 were identified for this analysis. 3 patients were excluded for analysis: 2 subjects ultimately did not proceed to HSCT and 1 subject had a CT scan that was not evaluable. Thus, 47 subjects had evaluable data; baseline characteristics are summarized (Table 1). Median follow-up for survivors is 298 days (interquartile range (IQR): 272-368 days). Overall survival (OS) significantly differs between FI strata (log rank p value =0.0013) (Figure 1). MI is not associated with OS (p=NS). FI is inversely correlated with distance walked during 6-minute walk test pre-HSCT (r = -0.33, p = 0.027). FI and MI are not significantly associated with self-reported physical activity using the International Physical Activity Questionnaire (IPAQ) post-HSCT (day 1, 30, 90), or patient-reported quality of life (QOL). Conclusions: These data provide the first evidence supporting an association between CT-defined cross sectional adipose tissue index and survival following HSCT. This non-invasive, routinely employed imaging modality may provide a new avenue for enhanced risk-assessment for HSCT patients. Subsequent studies will examine this effect in larger populations, with specific attention to other established prognostic variables. Table 1. Baseline Characteristics Variables N (%) Age, yrs (median, range) 60 (24-75) Gender, male 30 (63.8%) KPS ≥90 42 (89.3%) HCT-CI≥3 29 (61.7) Diagnosis § AML § ALL § CLL § CML § MDS § HD § MM § MPS § NHL 12 (25.5%) 5 (10.6%) 3 (6.4%) 2 (4.3%) 9 (19.1%) 2 (4.3%) 3 (6.4%) 2 (4.3%) 9 (19.1%) Conditioning Intensity, Myeloablative 24 (51.1%) Armand Disease Risk at HSCT § Low § Intermediate § High/Very High 2 (4.3%) 26 (55.3%) 19 (40.4%) Donor Type § Matched Related Donor § Matched Unrelated Donor § Mismatched Unrelated Donor § Double Umbilical Cord Blood 13 (27.7%) 27 (57.4%) 6 (12.8%) 1 (2.1%) Disclosures No relevant conflicts of interest to declare.

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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 score0.136
Threshold uncertainty score0.393

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 head0.025
GPT teacher head0.260
Teacher spread0.235 · 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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Citations0
Published2015
Admission routes2
Has abstractyes

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