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Enregistrement W2132107385 · doi:10.1093/europace/eut139

What costs matter? Rethinking social costs of new device technologies

2013· letter· en· W2132107385 sur OpenAlexaff
Alexandra A. Choby, Alexander M. Clark

Notice bibliographique

RevueEP Europace · 2013
Typeletter
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensUniversity of Alberta HospitalUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineProductivitySocial costIndirect costsEconomic costBalance (ability)Product (mathematics)Cost driverEconomic growthAccountingEconomics

Résumé

récupéré en direct d'OpenAlex

This editorial refers to ‘Cost-of-illness study of patients subjected to cardiac rhythm management devices implantation: results from a single tertiary centre’ by J. Fanourgiakis et al., 15: 366–375. Costs worthy of consideration often do not make it to the picture in cost-of-illness studies. Thus, there is cause for concern when Fanourgiakis et al.'s1 timely analysis of costs associated with implantable cardioverter-defibrillator (ICD) uptake in Greece estimate social cost only as impact on health resources and lost productivity. Cost-of-illness studies have been critiqued for misevaluation of cost from the patients' perspective, yet beyond this, they fail to capture non-economic costs to society, which however may be a by-product of the economic system. In the USA, for example, rapid adoption of a new technology also may have costs for the evidence base, an outcome that results from industry-driven device regulation. The US regulatory structure struggles to balance innovation with public safety, a practice that, as the history of ICD adoption in the USA shows, has given rise to controversy within and beyond the medical community. The Food and Drug Administration (FDA) have attempted to address this tension via the FDA Modernization Acts I and II, which seek to balance public safety against costs of designing and marketing devices by ‘pursu[ing] the least burdensome means’2 to certify safety and efficacy. Yet, customer protection is actually undermined by this drive to market new devices quickly,3 because pre-market trials tend to be less rigorous, have smaller samples, and contain lower risk patients that are followed up for shorter periods of time.2,4 In the urgency to market new technologies large robust randomized trials generally follow rather than precede FDA approval because study design details affect how quickly a device comes to the market.2 Consequently, market-driven device regulation undermines evidence-based healthcare because rigorous evidence follows—rather than proceeds—federal safety certification. For example, findings from the recently published EXAMINATION5 study showed drug-eluting stents (DESs) to be equally safe compared with bare-metal stents, yet this knowledge is useful mainly for patients with a DES. Following their rapid uptake, clinical use declined over questions about safety, and DESs are now obsolete thanks to newer technologies. The timing of these findings in relation to clinical use is not accidental, but actually a consequence of regulatory structures. The Center for Devices and Radiological Health (CDRH) regulates the marketing of medical devices. Devices are classified by potential risk to determine the required level of regulatory testing, so that less risky technologies can be marketed more quickly. Devices rated as Class III, i.e. life sustaining or supporting, like the DES, are subject to pre-market analysis (PMA) including a clinical trial, yet for every Class III device that undergoes PMA, ∼60 are marketed under the 510(k) exemption for new devices that are substantially equivalent to a device already on the market.4 Many devices, therefore, bear the FDA label despite limited data on safety and effectiveness.6 Center for Devices and Radiological Health post-marketing surveillance provides a mechanism to detect problems that emerge after approval. Yet, this system relies on clinicians or manufacturers—who may have conflicting obligations to shareholders and patients3—to identify and report problems.3,6,7 The FDA responds to reported adverse events by issuing a safety alert or, more rarely, a market recall.4,7 Yet, poor dissemination means clinicians are often unaware of problems with particular devices.4 Since 1990, there have been 130 device recalls affecting 900 000 major cardiac devices.4 In 10 cases (affecting 25 000 patients), the malfunction was reasonably likely to cause serious health consequences or death.4 Moreover, it is difficult to assign risk values to adverse events because they are underreported and the total number of patients who have a device is unknown.4 Therefore, significant risks often do not emerge until after clinical use—a particularly perilous situation since the US Supreme Court has ruled that (Reigel vs. Medtronic) patients may not litigate manufacturers for FDA-approved devices. Can the market itself be regulatory? Perhaps, if companies that market unsafe devices lose credibility and customers. However, in fact, recalls have little influence on share prices—although this varies somewhat by company characteristics.8 Ironically, facets of existing regulation are responsible: the FDA label, and protection from personal injury and liability suits, bolster shareholder confidence.8 Pro-industry regulations, rapid technology uptake, and underreported adverse events make risks unclear and emergent. Consequently, ‘evidence’ is one step behind clinical practice. Pro-industry regulatory processes and a disempowered FDA tend to produce evidence of excessively limited generalizability to clinical populations and indirectly exposes patients to unnecessary risk of death. Most clinicians expect evidence to be partial and contextually particular, and rely on some combination of evidence and experience yet regulatory structures which result in stronger and more generalizable evidence are needed. A.A.C, corresponding author, contributed to initial conceptualization, literature search, and writing (of first and all drafts) of this comment. A.M.C. contributed to refining the conceptualization and writing (second and following drafts) of this comment. Conflict of interest: none declared.

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)0,027
score de la tête « metaresearch » (Gemma)0,158
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,059
Score d'incertitude au seuil0,144

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0270,158
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,011
Communication savante0,0080,012
Science ouverte0,0050,003
Intégrité de la recherche0,0590,054
Charge utile insuffisante (le modèle a refusé de juger)0,0050,003

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 Opus0,309
Tête enseignante GPT0,401
Écart entre enseignants0,092 · 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

Citations2
Publié2013
Routes d'admission1
Résumé présentoui

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