MétaCan
Menu
Retour à la cohorte
Enregistrement W4385490247 · doi:10.1093/rheumatology/kead388

Risk/benefit trade-offs in rheumatology: rofecoxib revisited in the era of JAK inhibitors

2023· article· en· W4385490247 sur OpenAlexaff
Glen Hazlewood

Notice bibliographique

RevueLara D. Veeken · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueInflammatory mediators and NSAID effects
Établissements canadiensResearch CanadaAlberta Bone and Joint Health InstituteUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineRofecoxibRheumatologyInternal medicineOncology

Résumé

récupéré en direct d'OpenAlex

In 2004, rofecoxib was withdrawn from the market, after a large trial for polyp prevention (the APPROVE trial) was terminated early due to an increased risk of major adverse cardiovascular events (MACE) in people taking rofecoxib compared with placebo [1]. Shortly after, an ‘expression of concern’ was published by the New England Journal of Medicine [2] over reporting of MACE in the VIGOR trial that compared rofecoxib vs naproxen in people with rheumatoid arthritis [3]. In the VIGOR trial, three myocardial infarctions that occurred in the rofecoxib arm shortly after the study window closed were not included in the original publication. When these additional events were included, the relative risk of myocardial infarction increased from 4.25 (95% CI: 1.39, 17.4) to 5.00 (95% CI: 1.68, 20.1). Additional concerns regarding the presentation of thromboembolic risks were raised [2, 4]. The risks of MACE and concerns over reporting led to thousands of lawsuits. In 2007, a $4.85 billion settlement was reached to end these lawsuits, the largest drug settlement ever at the time. While the decision to withdraw rofecoxib, taken voluntarily by Merck, occurred in the context of pending lawsuits, was it the right decision for patients? Here, comparisons can be made to tofacitinib. Recently, the ORAL Surveillance trial compared tofacitinib to TNF inhibitors (adalimumab or etanercept) in people with rheumatoid arthritis with at least one CV risk factor [5]. ORAL Surveillance failed to find non-inferiority for tofacitinib for both MACE and malignancies, which led to a black box warning for all Janus kinase (JAK) inhibitors, and limitation of its use to people who have not responded or cannot tolerate TNF inhibitors. When viewed as absolute risks, tofacitinib 5 mg BID was associated with 0.2 more MACE events per 100 patient-years than TNF inhibitors, compared with 0.97 more events per 100 patient-years for rofecoxib vs naproxen (Fig. 1). Tofacitinib 5 mg BID was also shown to have a statistically significant increased risk of cancer compared with TNF inhibitors (0.36 more events per 100 patient-years) (Fig. 1). In terms of benefits, both rofecoxib and tofacitinib were similarly effective to their comparator for pain (rofecoxib) and disease control (tofacitinib). Rofecoxib had an added benefit of lowering the risk of major adverse GI events compared with naproxen (2.4 fewer events per 100 patient-years) (Fig. 1). These comparisons, while illustrative, should also consider the difference in trial design between ORAL Surveillance and VIGOR. Oral Surveillance was a non-inferiority safety trial, designed to maximize the detection of MACE and cancers, the co-primary endpoints. It is possible that the observed risks with rofecoxib may have been higher if the trial had a similar design. Comparison of selected risks of tofacitinib vs TNF inhibitors in the ORAL Surveillance trial with rofecoxib vs naproxen in the VIGOR trial. NMSC: non-melanoma skin cancer Following the withdrawal of rofecoxib, people who were taking it to treat their pain had to find alternative approaches. A study from US prescriptions data for people with musculoskeletal disorders found an increase in opioid prescriptions that correlated with a marked drop in non-opioid analgesics in 2005 when rofecoxib was withdrawn from the market [6]. Anecdotally, many people did not achieve the same response with celecoxib or other approaches, and people hoarded supplies of rofecoxib until it ran out. These people had almost certainly heard of the risks of rofecoxib but wanted it anyway. In contrast, JAK inhibitors continue to be a treatment option for people. People may prefer a JAK inhibitor if they have a strong preference for oral therapy, are at low absolute risk of cardiovascular events, or have not responded to other biologic therapy. Before JAK inhibitors, these people may have had to use prednisone and/or NSAIDs, or live with untreated pain, fatigue and other symptoms that come with suboptimally treated RA, which is also associated with an increased risk of MACE. At the time of withdrawal of rofecoxib, an Expert Advisory Committee in Canada recommended that it should be allowed back on the market, citing its efficacy, the low absolute risks of cardiovascular events, and that ‘patients benefit from having a variety of drugs to choose from’ [7]. In contrast, Dr François Bertrand, executive director of medical research for Merck Frosst Canada, commented after the withdrawal of the drug that ‘because of the nature of the events and the availability of other drugs, we decided the right thing was to discontinue [the drug]’ [8]. Although this later statement was perhaps motivated by the pending lawsuits, these conflicting statements highlight the need to have robust and transparent frameworks when considering risk/benefit trade-offs in the drug approval process. When effective treatments have serious but rare risks, who should decide whether a treatment is worthwhile? Patient-centred drug evaluation frameworks would put people living with the condition at the forefront of informing these risk/benefit decisions. Patient preference information refers to data, from people living with the condition, on the relative importance of different outcomes or attributes relevant to the treatment decision. It differs from patient-reported outcomes (PROs). Patient preferences tell us about the trade-offs that people are willing to make; people can trade-off amongst different PROs and other non-patient reported outcomes (e.g. risks of cardiovascular events). Recently, efforts have been made to further the incorporation of patient preference information into regulatory decisions. The Medical Device and Innovation Consortium in the USA, with funding from the Food and Drug Administration (FDA), has released guidance on the use of patient-preference information in the approval process for drugs and other medical devices [9]. The PREFER initiative in Europe, a private–public partnership, released recommendations last year on how, why and when to incorporate patient preferences in medical product decision-making [10]. While this progress is encouraging, the field is still in its early stages, and applied examples are rare. These risk/benefit frameworks acknowledge that the balance of benefits and risks will vary between people, depending on their disease characteristics and personal preferences. The MDIC framework proposes that a drug should be approved if the balance of benefits and risks favours the intervention, even for a subgroup of people [9]. Revisiting rofecoxib, patient-centred drug approval processes would ask whether there are groups of patients who would accept the cardiovascular risks for the benefits it provides. Preference data collected from patients, could help inform these judgements. Clearly though, if we accept the role for JAK inhibitors as providing an effective treatment option for people with rheumatoid arthritis, then we should reconsider the role of rofecoxib for people with uncontrolled pain, where there is a huge unmet need. Informing patients of these risks and benefits is essential and should be managed through best practices in shared decision-making, tailoring treatment discussions to the risk profile and preferences of patients. Rheumatic diseases have enormous impact of people’s lives and treatment choices require balancing risks and benefits. Listening to people with the condition and recognizing the diversity in people’s needs and preferences throughout decision-making processes regarding drug development and approval will help ensure more choices for patients. No new data were analysed in support of this manuscript. No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article. Disclosure statement: G.S.H. declares no financial conflicts of interest. G.S.H. has a non-financial academic relationship with the lead author of the VIGOR trial (co-authored publications, graduate supervision).

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)0,028
score de la tête « metaresearch » (Gemma)0,051
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 enseignants0,028
Score d'incertitude au seuil0,150

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0280,051
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0060,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,007
Communication savante0,0090,014
Science ouverte0,0030,004
Intégrité de la recherche0,0120,027
Charge utile insuffisante (le modèle a refusé de juger)0,0090,002

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 Opus0,011
Tête enseignante GPT0,258
Écart entre enseignants0,247 · 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

Citations0
Publié2023
Routes d'admission1
Résumé présentnon

Explorer davantage

Même revueLara D. VeekenMême sujetInflammatory mediators and NSAID effectsTravaux en français237 207