<i>Understanding the Lived Experience of Fatigue in Adolescents and Young Adults with Myeloproliferative Neoplasms: A Mixed Methods Study</i>
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".