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Enregistrement W2559916272 · doi:10.1182/blood.v126.23.2635.2635

An Adjustable Markov Model to Project Life Expectancy (LE) for Early Stage Favorable Risk Hodgkin Lymphoma Patients Treated with Contemporary Therapy

2015· article· en· W2559916272 sur OpenAlexaff
Michael Kelly, Susan K. Parsons, David Hodgson, Joshua T. Cohen, Jennifer M. Yeh, Jeremy S. Abramson, Debra L. Friedman, Tara O. Henderson, Peter Johnson, Stephen G. Pauker, John Raemaekers, Jane N. Winter, Kara M. Kelly, Ralph M. Meyer, Sharon M. Castellino, Andrew M. Evens

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensQueen's UniversityPrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineLife expectancyCohortRandomized controlled trialInternal medicinePediatricsPopulation

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Randomized studies have demonstrated that compared to chemotherapy alone (ChemoTx), combined modality therapy (CMT) improves early event-free survival in HL patients with early stage disease. However, long-term follow up from randomized trials suggests that overall survival (OS) when receiving ChemoTx alone is equivalent or superior to OS compared with CMT. In addition, many studies have described late effects in HL survivors. While the negative impact of late-effects on LE have been estimated for pediatric HL patients (Yeh, Blood, 2012), these estimates have limited generalizability to adult HL patients due to differences in treatment regimens and exposure-related late-effects risks. To address this gap, we sought to develop an adjustable Markov model to predict LE for adult HL patients treated with contemporary therapy. Methods: We created a Markov "state transition model" in which a cohort of patients moves through different health states. The patient cohort (base case, 18 years old) starts with initial diagnosis, and upfront treatment with 12-year OS modeled from the CCG 5942 (Wolden, JCO, 2012; COG, updated data, 2015). Following the first 12 years, the probabilities of dying were modeled by summing background mortality rates and the mortality rate associated with late effects. Background mortality rate estimates came from the 2010 CDC gender-specific LE data. Late effects mortality rates were estimated from excess absolute risk (EAR) estimates due to late effects from the Childhood Cancer Study (CCSS) Cohort 1 subjects across all disease stages who were treated with extended field RT (EFRT), higher alkylating agent therapy, and less anthracycline compared to contemporary cohorts. (Castellino, Blood, 2011) Recognizing that recent comparisons of RT doses and fields from CCSS survivors to those treated with involved field radiotherapy (IFRT) have demonstrated a reduction in RT to healthy tissues of approximately 50% (Koh, Radiation Oncology, 2007), we assumed that this RT reduction would reduce incremental mortality risk attributable to therapy by 50%. Thus, for patients treated with CMT containing IFRT, we reduced the reported EAR estimate for the CCSS-1 HL patients by 50%. Furthermore, for HL patients treated with ChemoTx alone, we assumed incremental mortality risk would be reduced by 75% (i.e., EAR reduced by 75% for this group). Because late effects mortality rates were based on pediatric data, we conducted extensive sensitivity analyses on EAR estimates to portray the scope of uncertainty surrounding LE estimates. Results: We built on previous work on this topic by utilizing 12-year OS from CCG 5942 and by adapting data from the CCSS-1 cohort to reflect the impact of late effects on LE with more modern therapy (e.g. IFRT). 12-year OS for early stage, favorable risk HL patients treated on CCG 5942 was 98.9% and 100% for patients treated with ChemoTx and CMT, respectively. LE for an 18 year old without HL was 60.9 years. Without consideration of the burden of late effects (i.e., EAR=0), a patient with early stage, favorable risk HL had a LE similar to a healthy 18 year old without HL. For HL patients, LE with ChemoTx alone (base case, COPP/ABV) was 58.0 years and the LE for treatment with CMT (i.e., COPP/ABV + IFRT) was 55.7 years. Additionally, reduced LE was also apparent for HL patients who received ChemoTx alone (see Figure). Finally, in order to apply these data to individual HL patients, we created an adjustable model with variables including age, gender, risk group (favorable/unfavorable), and gender- and treatment-specific EAR that may potentially be applied to an individual HL patient. Conclusion: We created an adjustable Markov model that predicts LE for adult HL patients treated with contemporary therapy. This model, including longer term OS data, demonstrated that contemporary therapy reduces the late effects burden. However, for survivors of early stage HL, we found that LE loss due to late effects substantially exceeds LE loss due to HL. To further enhance this model for the potential application in adults with HL, further synthesis of available pediatric and adult data (accounting for contemporary therapy) is needed to account for differences in EAR by age and gender over a life span. Altogether, models that synthesize clinical trial data provide valuable information to providers and may help guide them and HL patients towards individualized therapeutic decisions. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

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)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,028
Score d'incertitude au seuil0,055

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

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 Opus0,048
Tête enseignante GPT0,297
Écart entre enseignants0,249 · 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'étudeSimulation ou modélisation
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
Publié2015
Routes d'admission1
Résumé présentoui

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