Tobacco Use in the Myeloproliferative Neoplasms: Patient Behavior, Opinions, and Care
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».