<i>Prospective Symptom Burden Analysis in 784 Patients with Myeloproliferative Neoplasms: High Burden Correlates with Inflammatory/Genetic Biomarkers and Reduced Survival</i>
Bibliographic record
Abstract
Background: Myeloproliferative neoplasms (MPN) are associated with significant symptom burden, influencing patient quality of life and management. Thorough assessment of this burden is crucial; however, data from large populational registries are limited. Our objective was to characterize symptom burden in a large MPN cohort, determining: i) age-associated differences; ii) treatment effect; iii) biologic correlatives, and iv) impact on overall survival (OS) in a large, real-world population based setting. Methods: This is a multicenter prospective study analyzing patient-reported outcomes per MPN-Symptom Assessment Form Total Symptom Score (MPN-SAF TSS), a validated questionnaire grading (0-10) ten MPN symptoms ( JCO, 2012). Recruitment: Quebec CML-MPN Research Group registry (>20 community/academic centers). Eligibility: i) diagnosis of polycythemia vera (PV), essential thrombocytosis (ET), or myelofibrosis (MF) per WHO criteria; ii) completion of 1+ MPN-SAF TSS between 2013-2022. Conventional statistics were used (JMP® Pro 14.1.0 software; SAS Institute, NC, USA). Results: 4105 MPN-SAF TSS were completed by 784 patients (n= 285 PV, n=422 ET, n=77 MF). Median age at diagnosis was 63 years (range 19-96); female preponderance (56%). A median of 5 questionnaires were completed/patient (range 1-16). Mean symptom score was 16.7 (+/-12.3). A clinically significant total score, defined as >20, was reported by 33% of patients (n=261); most frequently women (p<0.0001) and MF cases (p=0.03). Symptom burden in the young: N=74 (9%) were age <40 years at diagnosis. At recruitment, mean aggregate and maximum MPN-SAF TSS scores were comparable across younger and older subsets (p=0.3-0.9) (Table 1), despite lower risk scores in the former (p<0.05). When scores were deconstructed, younger patients - vs older, had significantly higher levels of fatigue (p=0.03) and abdominal pain (0.007). Impact of therapy: Of those with high MPN-SAF scores, a considerable proportion were not treated (n=69/179, 39%). Higher mean scores were also reported in those having initiated therapy >36 months from diagnosis (p=0.0009). Of those with available MPN-SAF pre/post treatment, 37% showed deteriorating scores following cytoreduction, regardless of agent (p=0.4). Notably, those not on antiplatelet therapy had significantly higher mean (p=0.0006) and sub-item scores (excluding fever/night sweats). Over time, all interventions confounded, n=338 (43%) experienced worsening of MPN-SAF scores. Clinical biomarkers: Patients with anemia (hemoglobin <100 g/L) had significantly higher mean MPN-SAF scores and worse fatigue, inactivity, concentration, bone pain, and weight loss (all p<0.05). Increased C reactive protein (CRP) associated significantly with higher MPN-SAF scores (p=0.03), as well as fatigue, inactivity, early satiety, and bone pain (all p<0.05). Notably, patients with JAK2V617F variant allele frequency >50% (VAF) had higher average (p=0.03) and maximum (p=0.001) MPN-SAF, higher inactivity scores (p=0.02), and more frequent worsening scores over time (p=0.05). Survival impact: Higher mean MPN-SAF score had a significantly detrimental impact on OS (p=0.05) (Figure 1). Multivariate analysis revealed absence of antiplatelet therapy, age at diagnosis >65 years, male sex, and high-level inactivity to be independent predictors of inferior OS (HR 29, 6, 3 and 3 respectively). Conclusions: This is a large-scale study comprehensively appraising symptom burden in MPN patients, disclosing several novel findings. First, it unexpectedly exposes disproportionately severe symptoms (fatigue/abdominal pain) in younger patients. Second, it reveals deterioration of symptoms in the face of therapy in a significant number of patients, underscoring inadequacy of current therapies in symptom control. Third, it discloses anemia, elevated CRP, and JAK2 VAF>50% as putative biomarkers of symptom burden; potential adjuncts in identifying highly symptomatic subsets, particularly in situations where symptom assessment is challenging. Finally, it confirms negative survival impact of high symptom burden in a real-world setting. Overall this data exposes a critical unmet need for new therapeutic avenues aimed at alleviating symptom burden in patients with MPN. Further delineation of the inflammation-symptom relationship and future studies using novel prevention strategies are warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".