MétaCan
Menu
← Retour à la cohorte
Enregistrement W4417001860 · doi:10.1182/blood-2025-7971

Qualitative interviews exploring the patient experience of fatigue in individuals with sickle cell disease (SCD), thalassemia, and pyruvate kinase (PK) deficiency

2025· article· en· W4417001860 sur OpenAlexaff
Biree Andemariam, Ninamaria Badalamenti, Rae Blaylark, Audra Boscoe, Lily Cannon, Ralph Colasanti, Janie Davis, Daniel Ford, Rachael F. Grace, Holly John, Charles R. Jonassaint, Sarah Knight, Kevin H.M. Kuo, Laurice Levine, Clarisse Lopes de Castro Lobo, Jake Macey, Susan Morris, Parija Patel, Tamara Schryver, Nirmish Shah, Sujit Sheth, Cassandra Trimnell, A. D. J. Watson, Jill Welle, Teonna Woolford, Raffaella Colombatti

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensThe Scarborough HospitalUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésInterimQuality of life (healthcare)DiseasePyruvate kinase deficiencyQualitative researchInterim analysisPyruvate kinasePatient experience

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Hereditary hemolytic anemias (HHAs), including sickle cell disease (SCD), thalassemia, and pyruvate kinase (PK) deficiency, are a group of rare inherited hematological disorders that impair proper function of red blood cells, leading to a wide range of symptoms, clinical complications, and reduced health-related quality of life (HRQoL). The Red Cell Revolution™ (RCR) is an advisory council supported by Agios Pharmaceuticals, made up of members from the SCD, thalassemia, and PK deficiency communities, including patients, caregivers, advocates, Agios representatives, and specialist clinicians. An RCR initiative on unmet research needs in SCD, thalassemia, and PK deficiency identified physical and emotional fatigue as key factors affecting HRQoL that require greater understanding. Fatigue is a complex symptom across these HHAs with far-reaching impacts that are poorly understood. We present interim results from a qualitative study evaluating the experience of fatigue in patients with these respective HHAs. Methods: Participants were aged ≥18 years (yrs), lived in the USA, with a clinician-confirmed diagnosis of SCD, thalassemia, or PK deficiency. An institutional review board-approved, semi-structured interview guide was developed collaboratively. Participants were interviewed about their experiences of disease-related fatigue—specifically, physical, mental, and emotional fatigue—and asked to rate the most bothersome impacts of fatigue. Interviews were conducted virtually and lasted ~45 minutes. Transcripts were analyzed in ATLAS.ti using content analysis for concept elicitation. Results: The interim sample (N=46: n=20 SCD; n=6 α- and n=7 β-thalassemia; n=13 PK deficiency) had a median (range) age of 43.0 yrs (19–74), diverse racial/ethnic backgrounds (43% Black or African American [n=20], 37% White [n=17]), 80% female (n=37), and 48% had completed higher education (n=22). Median (range) number of yrs since HHA diagnosis was 42.0 (8–60), and participants received a median (range) of 2.5 (0–44) blood transfusions in the previous 52 weeks. All participants reported experiencing fatigue, generally described as tiredness and low energy that could not be resolved with sleep or rest. Participants were able to distinguish between physical, mental, and emotional fatigue; however, words such as “exhausted” and “drained”overlapped across fatigue types. Physical fatigue was reported as a bodily experience of “low energy” or feeling “heavy/weak”, similar to general fatigue. Mental fatigue was described as cognitive impairment resulting in “brain fog” or feeling “drained”, leading to difficulty initiating, engaging with, and focusing on tasks. Emotional fatigue was reported as feeling depressed or irritable because of physical/mental fatigue, and the emotional toll of being unable to function like individuals without fatigue. The negative impacts of fatigue were pervasive: Of 46 participants, allreported impacts on daily activities (eg, chores, errands, self-care); 43 reported impacts on physical functioning (eg, exercise, mobility), relationships and social life (eg, activities/events), and sleep (eg, difficulty falling asleep, staying awake in the day); 42 reported impacts on work (eg, missing work, task performance); 41 reported impacts on cognition (eg, memory, concentration); 37 reported impacts on emotions (eg, sadness/depression, anxiety, anger); 33 reported impacts on hobbies (eg, traveling, reading); and 5 reported impacts on education (eg, missed education). Participants ranked cognitive problems as the most bothersome impact of fatigue, followed by impact on relationships and social life.Conclusion: Patients with SCD, thalassemia, and PK deficiency recognized and reported physical, mental, and emotional manifestations of fatigue; the cognitive impacts of fatigue were considered the most bothersome across all groups.The final analysis aims to include 20 participants from each HHA group to better understand key differences and similarities in experiences of fatigue among the three HHAs and across age groups. A single conceptual model will be created, depicting the experience of fatigue across SCD, thalassemia, and PK deficiency, which can be used to identify and assess different aspects of disease-related fatigue among these patients. In turn, this model can be used to help patients, caregivers, and clinicians address the unmet needs of fatigue burden in the overall disease management of HHAs.

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)0,014
score de la tête « metaresearch » (Gemma)0,020
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,076

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,020
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0090,009
Communication savante0,0030,004
Science ouverte0,0020,006
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,032
Tête enseignante GPT0,302
Écart entre enseignants0,270 · 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'étudeQualitatif
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
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetHemoglobinopathies and Related Disorders→Travaux en français237 207→