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Enregistrement W3211771177 · doi:10.1182/blood-2021-152383

Mobility and Muscle Strength in Recipients of Hematopoietic Cell Transplantation for Sickle Cell Disease: A Preliminary Report from Sickle Transplant Evaluation of Longterm and Late Effects Registry (STELLaR)

2021· article· en· W3211771177 sur OpenAlexaff
Anirudh Veludhandi, Tami John, Nitya Bakshi, Staci D. Arnold, Monica Bhatia, Peter A. Lane, Lillian R. Meacham, Kirshma Khemani, Sonali Chaudhury, Festus Olusola Olowoselu, Kimberly A. Kasow, Matthew M. Hsieh, Allistair Abraham, Gregory M.T. Guilcher, Lakshmanan Krishnamurti

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensAlberta Children's Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineTransplantationHematopoietic stem cell transplantationDiseaseAnemiaSickle cell anemiaPediatricsInternal medicinePhysical therapy

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Recipients of Hematopoietic Cell Transplantation (HCT) are at risk for treatment related late effects including cardiovascular disease and diabetes. Physical fitness including mobility and muscle strength are important predictors of cardiovascular health and quality of life. Physical fitness has been described in children and adults who are long term survivors of HCT for malignant disease [1]. Patients with sickle cell disease (SCD) are likely to be subject to unique morbidity post-HCT as a consequence of the pathophysiology of SCD which is characterized by anemia, inflammation, pain, bone infarcts and neurological complications. A systematic assessment of physical fitness is required to understand some of the long-term outcomes HCT for SCD. Methods We enrolled patients with SCD ≥ 1-year post-HCT in Sickle Transplantation Evaluating Long Term And Late Effects Registry (STELLaR). We assessed physical fitness including mobility using the timed up and go (TUG) test, and muscle strength using the handgrip strength (HGS) test. TUG scores were compared with published normative data for children (mean 9.2s; SD = 0.8) and young adults (mean 10.5s, SD = 1.1) [2]. TUG scores ≥ mean+ 2SD were considered abnormal (children: ≥10.8s, young adults ≥12.7s). The HGS of each patient was compared to published normative data of children and adults [3,4]. Patients ≤12 years were abnormal if they had a HGS of two standard deviations below the mean. Patients ≥13 years were considered abnormal if they had a HGS lower than the lower limit of the 85% age-sex standard. Additionally, patients were also assessed for chronic graft versus host disease (cGvHD) by means of the Lee Chronic GvHD Symptom Scale [5]. Patients rated their extent of being bothered by cGvHD symptoms on a scale from 1 to 5, with 1 being not at all and 5 being extremely. These scores were then organized into 7 categories of cGVHD symptoms and normalized to a 0-100 scale. We report the results of an interim analysis of physical fitness in these HCT recipients. Results Out of 121 patients enrolled in the study, 37 HCT recipients were assessed for mobility using the TUG test, and for muscle strength using the HGS test. Children were defined as being below the age of 18 and young adults between 18 and 40 years of age. Patient demographic and clinical results for TUG and HGS are presented in Table 1. Young adult participants were more likely to have abnormal TUG and HGS results than pediatric study participants. Patient self-reporting of overall (summary) cGvHD symptoms was found to be statistically significant between children and young adults (p=0.037) with young adults iappearing to be slightly more bothered by GvHD symptoms. There was no correlation in length of time in years between a patient's transplant and whether or not there was abnormal physical fitness (TUG and HGS) for both the young adult and children cohorts. Conclusions These data suggest the existence of impaired physical fitness in the long term in SCD patients who are HCT recipients. They provide the rationale for systematic evaluation of physical fitness and other risk factors for cardiovascular disease post-HCT. References 1. Slater, M. E., Steinberger, J., Ross, J. A., Kelly, A. S., Chow, E. J., Koves, I. H., ... & Baker, K. S. (2015). Physical activity, fitness, and cardiometabolic risk factors in adult survivors of childhood cancer with a history of hematopoietic cell transplantation. Biology of Blood and Marrow Transplantation, 21(7), 1278-1283. 2. Mangano, G. R., Valle, M. S., Casabona, A., Vagnini, A., & Cioni, M. (2020). Age-related changes in mobility evaluated by the timed up and go test instrumented through a single sensor. Sensors, 20(3), 719. 3. Omar, M. T., Alghadir, A. H., Zafar, H., & Al Baker, S. (2018). Hand grip strength and dexterity function in children aged 6-12 years: A cross-sectional study. Journal of Hand Therapy, 31(1), 93-101. 4. Webb, A. R., Newman, L. A., Taylor, M., & Keogh, J. B. (1989). Hand grip dynamometry as a predictor of postoperative complications reappraisal using age standardized grip strengths. Journal of Parenteral and Enteral Nutrition, 13(1), 30-33. 5. Lee, S. J., Cook, E. F., Soiffer, R., & Antin, J. H. (2002). Development and validation of a scale to measure symptoms of chronic graft-versus-host disease. Biology of Blood and Marrow Transplantation, 8(8), 444-452 Figure 1 Figure 1. Disclosures Guilcher: BlueBirdBio: Research Funding; Project Sickle Cure Study: Other: Principal Investigator, Research Funding.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
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 enseignants0,003
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,008
Tête enseignante GPT0,242
Écart entre enseignants0,233 · 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
Publié2021
Routes d'admission1
Résumé présentoui

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