<i>Understanding the Lived Experience of Fatigue in Adolescents and Young Adults with Myeloproliferative Neoplasms: A Mixed Methods Study</i>
Notice bibliographique
Résumé
Background: Fatigue is a debilitating symptom of myeloproliferative neoplasms (MPN), profoundly affecting quality of life (QoL) and outcomes. While fatigue is pervasive across all age groups of MPN patients, our previous research indicated that adolescents and young adults (AYA) experience higher levels of fatigue compared to older adults (Poullet, 2023). This study aimed to explore the lived experience of fatigue in AYA using a mixed-methods approach in a large, real-world, population. Specifically, we sought to: i) identify age-related differences in fatigue; ii) delineate how fatigue manifests and impacts AYA patients; and iii) explore effective management strategies. Methods: Quantitative analysis: We analyzed patient-reported outcomes per MPN-Symptom Assessment Form Total Symptom Score (MPN-SAF TSS), a validated questionnaire grading 10 MPN symptoms (0-10), including fatigue (JCO, 2012). Recruitment: Quebec MPN Research Group registry (>20 centers). Eligibility: i) diagnosis of polycythemia vera (PV), essential thrombocytosis (ET), or myelofibrosis (MF) per WHO; ii) completion of 1+ MPN-SAF TSS (2013-2023). Patients were risk-stratified according to MPN specific risk score. Conventional statistics were used (JMP® Pro 14.1.0 software; SAS Institute, NC, USA). Qualitative analysis: In-depth individual interviews (n=12) were conducted with AYA (range 18-40 years) with MPN, using a semi-structured questionnaire. Interviews were transcribed and underwent iterative content analysis using QDA Miner 6.0.16 (Provalis Research, Montreal, Qc, Canada). Results: Age-associated differences: Analysis included 399 MPN-SAF TSS from 74 AYA patients (15 PV, 56 ET, 3 MF) and for comparison, 3706 MPN-SAF TSS from 710 older patients (non-AYA) (270 PV, 366 ET, 74 MF). AYA patients had a median age at diagnosis of 34 years (range 18-40); 68% female, and completed a median of 5 questionnaires per patient (range 1-16). High fatigue scores (>4) were reported by 57% of AYA patients (n=41) compared to 43% of non-AYA (n=302) (p=0.03). In both groups, high fatigue correlated with a clinically significant mean aggregate MPN-SAF TSS score (>20) (p=0.001-p<0.001). In AYA patients, high fatigue was independent of gender, whereas females predominantly reported higher fatigue among older patients (p<0.001). High fatigue levels in AYA did not correlate with MPN disease-specific risk score, while this association was observed in non-AYA cohorts (p=0.02). Interestingly, high fatigue in AYA clustered with specific MPN-SAF TSS subitems: early satiety (p=0.04), inactivity (p=0.04), and pruritus (p=0.02). Conversely, in older patients, high fatigue showed universal correlation with all other MPN-SAF TSS subitems (p=0.01-p<0.0001), with no discriminatory patterns. Expression of fatigue and impact: Fatigue emerged as the primary symptom impacting QoL among interviewed AYA patients. Participants often struggled to distinguish whether their fatigue stemmed from their disease or other aspects of life such as work, family, or aging. Dimensions of fatigue conveyed by participants included physical lethargy, weakness, concentration issues, and motivation loss, often with non-restorative sleep. The most significant impact reported was on work productivity, sometimes requiring career adjustments. Management: Interviewed AYA MPN patients found that pharmacological treatments aimed at controlling MPN-related biomarkers did not effectively alleviate their fatigue. Initial strategies such as coffee consumption, provided temporary relief. Regular physical activity was the most effective fatigue management strategy, with additional benefits from naps, healthy diet, and sleep routines. Conclusion: This mixed-methods study provides a comprehensive understanding of fatigue in AYA patients with MPN, highlighting several key findings. Fatigue is the predominant symptom driving MPN-SAF-TSS scores in AYA patients and clusters specifically with early satiety, inactivity and pruritus, exposing a unique pattern compared to older patients. Moreover, fatigue in AYA is independent of overall risk score, indicating potential undertreatment in this subgroup. Expression of fatigue is multidimensional and significantly impacts AYA patients' ability to work. Targeting fatigue with non-pharmacological interventions, particularly physical activity, could enhance QoL for AYA patients with MPN.
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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 | 0,012 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».