Tobacco Use in the Myeloproliferative Neoplasms: Patient Behavior, Opinions, and Care
Bibliographic record
Abstract
Abstract Background: Smoking increases the risk of Philadelphia-chromosome negative myeloproliferative neoplasm (MPN) development as well as increases the risk of thrombotic complications, the major cause of mortality in this population (Blood 2017 130:4199). Guidelines suggest that management of more indolent forms of MPNs include the assessment and mitigation of cardiovascular risk, which includes the management of smoking-related risks (NCCN Guidelines. 2017). In this analysis we sought to examine smoking outcomes and opinions from the perspective of the MPN patient, with the goal of helping to guide MPN patient care. Methods: The internet-based survey was developed by a team of MPN investigators and administered via Mayo Clinic's REDCap. Online recruitment was facilitated via multiple MPN-related webpages including the MPN Forum, MPN Net, MPN Research Foundation, and MPN Voice during June of 2018. Surveyed data included disease demographics such as thrombosis, medications, and smoking history. MPN-specific symptoms were assessed utilizing the MPN-10 (Blood. 2011 Jul 14;118(2):401-8). Data: Participant Demographics: Of the 607 patients who clicked the survey link, 435 patients were eligible for the survey and completed the questions regarding smoking history. Of these, 254 (58%) reported no history of tobacco use, 161 (37%) reported being ex-tobacco users, and 20 (5%) reported being current users. Among those who reported current tobacco use, 50% used premade cigarettes, 27% used self-rolled cigarettes, 20% reported vaping, and 3% reported chewing tobacco or snuff use. Respondents were most often from the US (67%), Australia (12%), the UK (6%) or Canada (6%). The distribution of respondents included 28% with myelofibrosis, 44% with polycythemia vera and 27% with essential thrombocythemia. Over one quarter (26%) of patients had experienced a prior thrombosis, with the most frequent thrombosis types being DVT (9%), stroke or transient ischemic attack (8%), abdominal vein thrombosis (5%), myocardial infarction (2%) and cerebral vein thrombosis (2%). When comparing current/former smokers to never smokers, no significant difference in thrombotic frequency was observed. Smoking Correlates: When evaluating MPN symptoms, current/former smokers reported higher severity of fatigue (mean 5.6 vs 5.0, p=0.02) and inactivity (mean 4.0 vs 3.1, p=0.03) compared to nonsmokers. Additionally, current/former smokers were more likely to experience early satiety (69% vs 58%, p=0.03), inactivity (80% vs 71%, p=0.04), concentration difficulties (82% vs 73%, p=0.04), and reduced quality of life (mean 6.1 vs 6.5, p=0.03) compared to nonsmokers. Although not significant, a trend of higher symptomatology was observed for former/current smokers compared to nonsmokers (MPN-10 total symptom score [TSS] mean 30.4 vs 27.0, p=0.07). Former/current smokers were also more likely than nonsmokers to report heavy alcohol consumption (>7 drinks per week in 9.6% vs 4.0%, p=0.03) and using opioids for pain management (24% vs 11%, p=0.001). Smoking Opinions and Care: As shown in Table 1, less than half of current/former smokers (43%) reported having their physician discuss tobacco use with them. Less than 40% of MPN current or previous smokers were aware of the risk of MPN development due to smoking, although the majority (83%) were aware of the increased thrombotic risk. Less than one quarter (43/181) of current/former smokers reported having utilized pharmacologic therapy to aid cessation. Conclusions MPN patients with current or previous tobacco use demonstrate significantly higher symptom burden than non-smoking counterparts. In terms of patient care, less than half of patients who are current or previous smokers recall having a physician discuss their smoking habits with them. These results highlight the need for enhanced MPN patient counseling by health care providers, both regarding the risks of smoking and available methods to aid cessation. Disclosures Scherber: Orphan Pharmaceuticals: Honoraria; Incyte: Consultancy. Dueck:Phytogine: Employment; Pfizer: Honoraria; Bayer: Employment. Palmer:Novartis: Research Funding. Mesa:Incyte: Research Funding; Galena: Consultancy; Gilead: Research Funding; Ariad: Consultancy; Celgene: Research Funding; Novartis: Consultancy; CTI: Research Funding; Promedior: Research Funding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".