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Enregistrement W2983609105 · doi:10.1097/01.cot.0000615272.66203.18

Examining Facility & Provider Influence on Multiple Myeloma Survival

2019· article· en· W2983609105 sur OpenAlexaboutno aff
Michelle Perron

Notice bibliographique

RevueOncology Times · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaMedicineBusinessInternal medicine

Résumé

récupéré en direct d'OpenAlex

healthcare provider: healthcare providerThe global burden of multiple myeloma (MM) has been increasing for the past 3 decades. In the U.S., it now accounts for 10 percent of hematologic cancers. The American Cancer Society estimates that in 2019, approximately 32,000 new cases of MM will be diagnosed in U.S. residents and nearly 13,000 deaths will occur as a result of it. Population-based studies suggest that patients with hematologic cancers such as MM benefit from treatment at high-volume centers or designated NCI Comprehensive Cancer Centers (NCICCC). A 2017 study drilled down on this correlation to examine whether the volume of MM cases influenced patient outcomes. In this study, researchers identified a relationship between patient volume and outcome—specifically, that MM patients treated at low-volume facilities had a 22 percent higher risk of death compared to MM patients treated at facilities that treat a high volume of MM cases (J Clin Oncol 2017;35:598-604). A Study Develops These findings sparked interest in a team of hematologic oncologists at the Lineberger Comprehensive Cancer Center at the University of North Carolina (UNC) at Chapel Hill. Ashley T. Freeman, MD, was a UNC fellow at the time. She is now a medical oncologist at BC Cancer, a comprehensive cancer agency in British Columbia, Canada. “Our team was involved with hematologic malignancies and we had a particular interest in multiple myeloma,” she explained. “UNC also has a unique database that could help us look more closely at possible connections between treatment facilities and outcomes in multiple myeloma.” The Cancer Information & Population Health Resource (CIPHR), housed at UNC Lineberger Comprehensive Cancer Center, is a nationally unique state-based dataset that links the North Carolina Central Cancer Registry (more than 400,000 patients) to beneficiaries of Medicare, Medicaid, and private health insurance plans (more than 6 million patients) throughout North Carolina. Utilizing the abilities of the CIPHR, Freeman and her co-investigators sought to determine the factors that might predict NCICC evaluation and to examine the impact of NCICCC evaluation on overall survival of patients with MM. The team's secondary objective was to determine whether individual provider volume or patient sharing between MM specialists at an NCICCC and oncologists in the community were associated with overall survival rates. Their findings were published in the Journal of the National Comprehensive Cancer Network in September (2019;17(9):1100-1108). “We hoped to tease out whether patients did well at high-volume cancer centers due to the experience of the physicians or the resources of a large center, or whether other factors played a role,” Freeman said. “We knew that treatment at a high-volume center might not be feasible for all patients. We wondered if patient sharing was a way to export that level of care to the community. We sought to answer these questions retrospectively using a claims-based database.” Study Design Using data from the CIPHR, the research team examined a retrospective cohort of patients throughout North Carolina who had been diagnosed with MM. Inclusion criteria were age >18 years and diagnosis of MM between 2006 and 2012 (n = 4,603). The researchers selected this date range because 2006 was the first year Medicare Part D was available, a change that allowed records of oral chemotherapy treatment to be captured, and because 2012 was the most recent complete year of data available at the time the analysis was performed. Exclusions were diagnosis on a death certificate or autopsy (n = 215), the presence of an additional cancer diagnosis (n = 722), lack of confirmatory laboratory studies (n = 256), incomplete information about residence (n = 9), or provider code (n = 49). To ensure that patient comorbidities were captured in order to understand complete health care use, patients were excluded if they did not have continuous insurance enrollment for 6 months prior to and 12 months after diagnosis (n = 2,031). The researchers said this exclusion was important to provide an accurate representation of interactions between patients and their physicians. A final exclusion was for patients who were simultaneously enrolled in Medicare, Medicaid, and a private insurance plan for 18 months (n = 13); this was unlikely and therefore believed to be erroneous. The resulting number of patients who met all inclusion criteria was 1,029. Their mean age was 68 years. Freeman and her coauthors used logistic regression to identify factors associated with an increased likelihood of evaluation at an NCICCC within 12 months of diagnosis. Next, they conducted three separate multivariable analyses: the first to explore the impact of at least one outpatient visit to an NCICCC on overall survival; the second to explore the impact of the primary oncologist's patient volume on overall survival; and the third to explore how patient sharing between MM specialists at an NCICCC and community oncologists affected overall survival. “We examined patient survival according to whether a patient was seen at a comprehensive cancer center and then according to whether a patient was treated by a high- or low-volume provider. We then developed a model to examine patient-sharing relationships between physicians at a comprehensive cancer center and in the community to determine whether these sharing relationships affect survival of patients in the community,” Freeman explained. “We tried to see whether patients treated by doctors who shared care with a multiple myeloma expert did any better.” Study Results On the question of survival rates according to primary treatment setting, the results showed that patients who didn't undergo evaluation at an NCICCC had an increased mortality risk compared to patients who did. On the question of survival rates according to provider experience or focus, patients treated primarily by low-volume community oncologists regardless of patient sharing had a higher mortality risk compared to patients primarily treated by MM specialists at an NCICCC. However, the study found no difference in mortality between patients treated by NCICCC MM specialists and patients treated by the highest-volume community providers. “In a nutshell, patients who were seen at least once at a comprehensive cancer center or who were treated primarily by an NCICCC myeloma specialist had a better survival rate,” Freeman said. “But, when we compared survival for patients treated by NCICCC myeloma specialists and the highest-volume community providers (i.e., the 9th and 10th deciles of volume), we didn't find a difference in survival. That suggests that provider experience rather than simply resource availability at a comprehensive cancer center is important. It is also important to note that there were unmeasured variables in this study, such as treatment specifics, that could have confounded our results. “With regard to patient sharing, we didn't find an impact on survival,” Freeman continued. “That may be because there is no association, or because our definition of patient sharing in this study doesn't properly capture the relationship between physicians.” The fact that the UNC study confirmed earlier findings about the importance of specialist care for MM is likely to spur more research interest—and encourage the oncology community to increase the availability of such resources. “Based on this study and prior studies, there is mounting evidence that patients with multiple myeloma benefit from treatment by experienced providers,” Freeman said. “So whenever possible or feasible to see a multiple myeloma specialist, that should be pursued. Further work is necessary to determine how to improve outcomes for patients when evaluation by an experienced provider is not possible.” Michelle Perron is a contributing writer.

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)0,023
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 enseignants0,082
Score d'incertitude au seuil0,164

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,023
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

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 Opus0,061
Tête enseignante GPT0,347
Écart entre enseignants0,286 · 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
Publié2019
Routes d'admission1
Résumé présentoui

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