Sex-based disparities in MPN treatment patterns: A study of 1167 patients from the Quebec MPN registry
Bibliographic record
Abstract
Abstract Background: Sex and gender have emerged as fundamental modifiers of disease biology, phenotype, and prognosis in myeloproliferative neoplasms (MPN) (Blood Adv, 2020). Furthermore, substantially higher symptom burden has been documented in women vs men with MPN, including recent data from our group (publication pending, 2025), prompting inquiry into how these distinctions inform therapeutic strategy. Contemporary data on sex-driven differences in treatment courses and outcomes remain scarce and the impact of such patterns is often underappreciated. The objective of this study was to determine sex-specific treatment patterns in patients with MPN with the goal of better directing and personalizing management. Methods: Consecutive patients with WHO/ICC-defined polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) from the Quebec MPN Research Group Registry (13 academic/community centers) were included. Clinical data were abstracted from medical records spanning 3/2015 (registry deployment) to 1/2024. Conventional disease-specific risk stratification models were used. Clinical endpoints were defined per convention. Standard statistics were used (SAS version 9.4; Cary, NC, USA); all tests were two-sided with p<0.05 considered significant. Results: Women accounted for 55% of the 1167 patients studied. Median age at diagnosis was comparable across sexes (60/61 years in women/men (range 17-95); p=0.54). Median follow-up was 88 months (0.56-506), irrespective of sex. Disease-specific sex distribution showed a preponderance of women with ET (63%), a minority with MF (32%) (p<0.0001), and equal ratios for PV. Baseline demographics and comorbidities were balanced. Disease-specific models revealed similar risk profiles between women and men in all 3 MPN subtypes (p=0.1-0.49). Regarding therapy, an equivalent proportion of women were approached frontline with cytoreduction (vs observation) compared men (p=1.0). However, significantly fewer female PV and MF patients required phlebotomy or transfusions, respectively, over disease course compared to their male counterparts (p=0.0017 each). Considering MPN subtypes collectively, first-line cytoreductive regimens revealed significantly higher interferon exposure in women (2.6%) vs men (0.4%) (p<0.0001), and, conversely, skewing towards ruxolitinib use in men (9.2%) vs women (3.1%), concordant with the enrichment of male MF patients in the registry. Next, stratified by MPN subtype, cytoreductive agent patterns were found to be largely homogeneous across sexes: hydroxyurea predominated as first-line in both women and men in PV (women/men; 70.3%/75.2%; p=0.66) and ET (68.4%/70.5%; p=0.19), while ruxolitinib was the leading agent in MF (50%/52%; p=0.47). Of all patients on cytoreduction, 62.1% of women and 54.4% of men remained on frontline therapy at the time of data collection (p<0.0001). Remarkably, treatment discontinuation patterns revealed the reason for stopping/switching from first-line agent varied significantly according to sex. Of those who discontinued therapy, intolerance was the leading cause in women (18.9% vs 15.5% in men) (p<0.0001), while resistance and death were more common in men (respectively vs women 3.6% vs 1.7%; and 10.9% vs 4.3%) (p<0.0001). When overall length of drug exposure was scrutinized, this was found to be balanced between sexes for all agents except interferon, for which duration of treatment was significantly longer in men vs women (94.3 months vs 34.3; p=0.02). Conclusions: This is, to our knowledge, one of only available accounts of treatment patterns appraised under a sex-informed lens in the cadre of MPN. These large-scale, mature, real-world data confirm that in this schema – sex matters. Overall, key disparities in therapeutic strategies between women and men are disclosed, reflecting sex-biased enrichment of specific MPN subtypes. However, when subtype-stratified, remarkably uniform, sex-agnostic patterns were observed, despite well-documented higher symptom burden in women. This challenges the extent to which symptomatology, distinct in females, is justly informing treatment decisions. Finally, more women than men discontinued therapy due to intolerance, underscoring the need for better-tolerated agents as well as complementary treatment strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".