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Enregistrement W2912062763 · doi:10.1182/blood-2018-99-109740

3,023 Mayo Clinic Patients with Myeloproliferative Neoplasms: Risk-Stratified Comparison of Survival and Outcomes Data Among Disease Subgroups

2018· article· en· W2912062763 sur OpenAlexaff
Natasha Szuber, Mythri Mudireddy, Maura Nicolosi, Domenico Penna, Rangit Vallapureddy, Terra L. Lasho, Christy M. Finke, Kebede H. Begna, Michelle A. Elliott, C. Christopher Hook, Alexandra P. Wolanskyj, Mrinal M. Patnaik, Curtis A. Hanson, Rhett P. Ketterling, Shireen Sirhan, Animesh Pardanani, Naseema Gangat, Lambert Busque, Ayalew Tefferi

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueMyeloproliferative Neoplasms: Diagnosis and Treatment
Établissements canadiensUniversité de MontréalHôpital Maisonneuve-RosemontJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineMyelofibrosisPolycythemia veraEssential thrombocythemiaInternal medicinePopulationInternational Prognostic Scoring SystemMyelodysplastic syndromesBone marrow

Résumé

récupéré en direct d'OpenAlex

Abstract Background: While several population-based studies have reported on adverse outcomes and life expectancy in myeloproliferative neoplasms (MPN) (JCO. 2015;33:2288; AJH. 1999;61:10; Leukemia. 2013;27:1874; JCO. 2012;30:2995), large-scale studies reporting mature data have been rare and important questions remain unanswered. The current study documents the Mayo Clinic (MC) decades-long experience with over 3,000 consecutive MPN patients including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF) with the majority of patients followed until death. Herein, we provide mature, and importantly, risk-stratified survival data and disease complication estimates. Methods: Study patients were recruited from MC, Rochester, MN, USA between 10/27/1967 and 12/29/2017. All MPN diagnoses and documentation of fibrotic/leukemic transformations were in accordance with the 2016 World Health Organization criteria (Blood. 2016;127:2391). Clinical and laboratory data were abstracted from medical records. Risk-stratification used conventional risk models considering age, leukocytes, and venous thrombosis for PV (Leukemia. 2013;27:1874), international prognostic score for ET (Blood. 2012;120:1197), and dynamic international prognostic scoring system for PMF (Blood. 2010;115:1703). Statistical analyses were based on parameters obtained at the time of referral to MC which, in the majority of cases, coincided with or was within 1 year of diagnosis. All patients were followed from diagnosis until death or date of last follow-up/contact. Recipients of allogeneic stem cell transplants were censored at the time of transplant. Standard statistical methods and time-to-event curves prepared using the Kaplan-Meier method were used to compare MPN subgroups. Survival data for low-risk ET/PV were compared with age- and sex-matched controls from Olmstead County, MN, USA. JMP® Pro 13.0.0 software (SAS Institute, Cary, NC, USA) was used for all analyses. Results: 3,023 consecutive patients (median age 62 years, range 18-96; 51% males) were considered, including 89% diagnosed within the year: 665 PV, 1076 ET and 1282 PMF. Conventional risk stratification in 2925 evaluable patients revealed low, intermediate, and high-risk status in 26%, 29%, and 45% of PV and 30%, 42%, and 28% of ET patients, respectively, while PMF cases were attributed low (14%), intermediate-1 (38%) -2 (40%) and high (8%) risk (Table 1). After a median follow-up of 8.2 years for PV (range 0-39), 9.9 for ET (range 0-47), and 3.2 for PMF (range 0-31), 1631 (54%) deaths, 183 (6%) leukemic transformations, 244 (14%) fibrotic progressions, and 516 (17%) thrombotic events were recorded (Table 1). Median overall survival (OS) was 18 years for ET, 15 for PV and 4.4 for PMF (p<0.05 for all inter-group comparisons) (Figure 1A). Leukemia-free survival was similar for ET and PV (p=0.22) and significantly worse for PMF (p<0.001) (Figure 1B). PV, compared to ET, was associated with higher risk of fibrotic progression (p<0.001) (Figure 1C). Thrombosis risk after diagnosis was highest in PV and lowest in PMF (p=0.002 for PV vs ET; 0.56 for ET vs PMF; and 0.001 for PV vs PMF) (Figure 1D). Following risk stratification, median OS in low-risk ET (28 years) and low-risk PV (27 years) were superimposed (p=0.89) (Figure 2). Similarly, intermediate or high-risk PV and ET patients had comparable OS within each risk strata (p=0.23 and 0.11, respectively). Low-risk PMF survival was analogous to that of intermediate-risk PV (p=0.06). All other risk-stratified categories disclosed significantly different inter- and intra-group survival patterns (p<0.001) (Figure 2). Despite their categorization as favorable-risk disease, both low-risk ET and low-risk PV displayed excess mortality relative to age- and sex-matched controls with median survival of 26.7 and 28.1 years respectively, compared to the expected 37.5 and 39.2 years (p<0.001) (Figure 3). Conclusions: The current study provides large-scale and uniquely mature survival and outcomes data in MPN and highlights MPN subgroup risk categorization as cardinal in appraising disease natural history. Interestingly, OS was only marginally better in ET, compared to PV, while the latter clearly displayed a higher risk of thrombosis and fibrotic progression. Moreover, data disclosed shortened life expectancy relative to matched controls even in MPN with favorable risk attribution. Disclosures Busque: BMS: Consultancy; Novartis: Consultancy; Pfizer: Consultancy; Paladin: Consultancy.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,005
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
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 ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,034
Tête enseignante GPT0,316
Écart entre enseignants0,282 · 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

Citations33
Publié2018
Routes d'admission1
Résumé présentoui

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