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Enregistrement W2592146876 · doi:10.1182/blood.v128.22.4264.4264

Distribution and Impact of Comorbidities on Survival and Leukemic Transformation in Myeloproliferative Neoplasm (MPN)-Associated Myelofibrosis (MF)

2016· article· en· W2592146876 sur OpenAlexaffabout
Justyna Bartoszko, Tony Panzarella, Caroline McNamara, Anthea Lau, Aaron D. Schimmer, Andre C. Schuh, Hassan Sibai, Karen Yee, Mark D. Minden, Rebecca Devlin, Vikas Gupta

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueMyeloproliferative Neoplasms: Diagnosis and Treatment
Établissements canadiensUniversity Health NetworkUniversity of TorontoPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMyelofibrosisMedicineComorbidityMyeloproliferative neoplasmInternal medicinePolycythemia veraProportional hazards modelPopulationInternational Prognostic Scoring SystemSurvival analysisCancerOncologyMyelodysplastic syndromesBone marrow

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction. Myelofibrosis is a disease characterized by aberrant bone marrow function with eventual fibrosis. Current widely-used disease prognostic indices, such as the Dynamic International Prognostic Scoring System (DIPSS) do not take into account comorbidities, which may have significant effects on patient survival as well as disease course. We sought to describe the comorbidity distribution in this patient population and assess the impact of comorbidities as scored by two different widely used scales in clinical practice, the Adult Comorbidity Evaluation 27 (ACE-27) and the Hematopoietic Cell Transplant Comorbidity Index (HCT-CI), on overall survival and leukemic transformation in myelofibrosis. A score of 3 on ACE-27 or ≥3 on HCT-CI generally indicates a high burden (severe) comorbidities. Methods. We conducted a retrospective study of 309 patients seen at the MPN program at the Princess Margaret Cancer Centre, with a confirmed diagnosis of myelofibrosis [primary myelofibrosis (PMF), post-essential thrombocytopenia (PET-MF) or post-polycythemia vera (PPV-MF)]. Patients were seen from 1999-2014 with a median follow-up time of 2 years. Time to death and leukemic transformation was examined from the date of first presentation to our centre. Our primary aim was to examine the impact of comorbidity scores, as assessed by ACE-27 and the HCT-CI, on overall survival. In a secondary analysis we examined the impact of comorbidity scores on leukemic transformation. Multivariable Cox proportional hazards models were constructed for the primary and secondary outcomes. A series of descriptive analyses were carried out examining the distribution of various comorbidities as captured by the two scales. Results. The most common comorbidities captured by ACE-27 were hypertension (n=92, 22.3%), diabetes mellitus (n=43, 10.4%), venous disease (n=26, 6.3%), solid tumour including melanoma (n=26, 6.3%), and angina/coronary artery disease (n=23, 5.6%). The most common comorbidities captured by HCT-CI were cardiac (n=49, 17.3%), diabetes (n=43, 15.2%), mild hepatic (n=28, 9.9%), cerebrovascular disease (n=25, 8.8%), prior solid tumour (n=24, 8.5%). The distribution of comorbidity scores as compared between scales is shown in Table 1. A total of 78 patients (25.2%) experienced the primary outcome of interest, which was all-cause death. For the primary outcome of overall survival, there were differences across groups of patients with different comorbidity scores using ACE-27 or HCT-CI, with the highest severity groups having worse outcomes (Figure 1). Progressively increasing DIPSS categories (Low, Intermediate-1, Intermediate-2, and High risk) were also associated with worse overall survival. On multivariable survival analysis, an ACE-27 score of 3 when compared to a lower score of 0-1 was associated with an almost two-fold increase in the risk of all-cause death [HR 1.95 (95% CI 1.06-3.58), p=0.03]. On multivariable analysis, an HCT-CI score of 3+ when compared with 0-1 was marginally significantly associated with an increased risk of all-cause death [HR 1.60 (95% CI 0.96-2.68), p=0.07]. Interaction terms were tested between the scores and age at presentation and no effect of age on survival across varying severities of comorbidities was found. In our secondary analysis, there was no impact of the ACE-27 or HCT-CI on leukemic transformation. Conclusions. ACE-27 picked up severe co-morbidities in 13% of patients in our cohort while HCT-CI picked up severe comorbidities in 23%. Although the incidence of severe co-morbidities was lower when assessed by ACE-27, the overall impact on survival of severe comorbidities as assessed by both scores is likely to be similar. The presence of severe comorbidities at the time of diagnosis conferred a significant survival disadvantage in patients with myelofibrosis, but had no impact on progression to leukemic transformation. Table Overall survival by ACE-27 comorbidity category, showing differences between categories of comorbidity severity (p=0.047, log rank test). Table. Overall survival by ACE-27 comorbidity category, showing differences between categories of comorbidity severity (p=0.047, log rank test). Figure Figure. Disclosures Panzarella: Cellgene: Consultancy. Schimmer:Novartis: Honoraria. Schuh:Amgen: Membership on an entity's Board of Directors or advisory committees. Yee:Novartis Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gupta:Novartis: Consultancy, Honoraria, Research Funding; Incyte: Consultancy, 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,268
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

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