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Enregistrement W2161799794 · doi:10.1200/jco.2007.11.9529

Aromatase Inhibitors and Arthralgias: A New Frontier in Symptom Management for Breast Cancer Survivors

2007· letter· en· W2161799794 sur OpenAlexaboutno aff
Harold J. Burstein, Eric P. Winer

Notice bibliographique

RevueJournal of Clinical Oncology · 2007
Typeletter
Langueen
DomaineMedicine
ThématiqueBone health and treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineBreast cancerAromataseInternal medicineTamoxifenCancerAdjuvant therapyOncologyOsteoporosisAromatase inhibitorGynecology

Résumé

récupéré en direct d'OpenAlex

Aromatase inhibitors (AIs) have become widely used as adjuvant therapy for postmenopausal women with hormone receptor– positive breast cancer. Although the optimal schedule and duration remain unclear, current guidelines endorse the inclusion of AI treatment at some point during adjuvant therapy for most postmenopausal women with estrogen receptor–positive breast cancer. Because of the incidence and demographics of breast cancer, hundreds of thousands of breast cancer patients throughout the world will start AI therapy each year. As a class, AIs are generally safe drugs and are reasonably well tolerated by most patients. They have been studied in very large adjuvant trials with careful documentation of both toxicity and patient compliance. The majority of symptoms associated with aromatase inhibition are related to the profound estrogen deprivation that arises as a consequence of AI therapy. These symptoms include hot flashes, night sweats, vaginal dryness, and sexual dysfunction. A particular concern among postmenopausal women has been bone health, including osteopenia, osteoporosis, and osteoporotic fracture, all of which are accelerated by AIs. Serial monitoring of bone mineral density and supplemental calcium, vitamin D, and exercise are recommended to minimize loss of bone density in women receiving AI therapy. Given that AI treatment has become more common, clinical experience has suggested another unique musculoskeletal adverse effect of AI therapy: arthralgias. Arthralgia is defined as pain in one or more joints, and is distinguished from arthritis, which suggests inflammation of the joint caused by structural damage, infection, autoimmunity, or metabolic conditions. Arthralgias may be difficult to gauge in older breast cancer patients with early-stage breast cancer owing to the high prevalence of degenerative joint disease and of nonspecific muscle and joint aches. In patients with stage IV breast cancer, the frequency of bone metastases also confounds easy measurement of drug-associated musculoskeletal symptoms. The pivotal trials of AIs as treatment for metastatic breast cancer documented musculoskeletal complaints in 4% to 13% of women receiving AI therapy. The first report linking AI therapy to arthralgia was a letter to the editor of the Journal of Clinical Oncology (JCO) in 2001, describing a series of patients with metastatic breast cancer treated with AI therapy in the United Kingdom. Among 77 women starting AI therapy for metastatic breast cancer, 16% developed new joint pains, mostly of moderate grade, prompting one in four of these women to stop AI therapy because of symptoms. In each case, cessation of AI therapy resolved the joint pain. The most common sites of joint pain were hands, knees, hips, back, and shoulders. The seminal studies of adjuvant AI therapy such as the Arimidex, Tamoxifen, Alone or in Combination, Breast International Group 1-98, Intergroup Exemestane Study, and National Cancer Institute of Canada trial MA17, captured data on adverse effects of AI and tamoxifen as treatment-related toxicity. Bone and joint symptoms were categorized in a variety of different ways in the toxicity reporting of the major adjuvant AI trials, including classification as arthritis, joint pain, or musculoskeletal disorders, to name several examples. Estimates for the incidence of musculoskeletal problems in these trials range from 5% to 36% among patients receiving AI therapy, and from 4% to 29% among women taking tamoxifen. These broad ranges reflect both the variety of definitions of arthritis, arthralgia, and bone pain, and the potentially confounding effect of widely prevalent nonspecific bone symptoms. Formal surveys of patients continuing protocol treatment in the adjuvant AI trials showed comparable global quality of life among women taking either AI or tamoxifen/placebo therapies, suggesting that whatever musculoskeletal symptoms patients were experiencing, they had on average a modest impact on dayto-day function and activity. Nonetheless, growing clinical experience suggests that AI therapy can be associated with a particular clinical syndrome of arthralgias, and breast cancer specialists are becoming more familiar with these symptoms. Typical sites of discomfort include the hands (fingers and wrists), arms, knees, and feet, pelvic and hip bones, and back. In our experience, patients describe stiffness, achiness, or pain that is usually symmetric, may be associated with mild thickening of the soft tissues (for instance, rings do not fit as before), and is temporally associated with onset of AI therapy. Symptoms may be most noticeable on awakening, and often improve with morning activities. Patients frequently make gestures such as squeezing or flexing their joints to show affected areas, and often mention that they feel they have “aged” abruptly. The true prevalence of such symptoms among breast cancer survivors is not known, and thus the report from Crew et al at JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 25 NUMBER 25 SEPTEMBER 1 2007

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)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0010,002
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,079
Tête enseignante GPT0,455
Écart entre enseignants0,376 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations72
Publié2007
Routes d'admission1
Résumé présentoui

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