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Enregistrement W2042345206 · doi:10.1111/j.1365-2125.2006.02806.x

Warfarin: almost 60 years old and still causing problems

2006· article· en· W2042345206 sur OpenAlexaboutno aff
Munir Pirmohamed

Notice bibliographique

RevueBritish Journal of Clinical Pharmacology · 2006
Typearticle
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiquePharmacogenetics and Drug Metabolism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWarfarinMedicinePharmacologyIntensive care medicineInternal medicineAtrial fibrillation

Résumé

récupéré en direct d'OpenAlex

If you have ever doubted that pharmacologically potent compounds can be derived from plants, consider the history of warfarin. In the 1920s cattle in the Northern USA and Canada were afflicted by an outbreak of an unusual disease, characterised by fatal bleeding, either spontaneously or from minor injuries. Mouldy silage made from sweet clover (Melilotus alba and M. officinalis) was implicated, and L M Roderick in North Dakota showed that it contained a haemorrhagic factor that reduced the activity of prothrombin. However, it was not until 1940 that Karl Link and his student Harold Campbell in Wisconsin discovered that the anticoagulant in sweet clover was 3,3′-methylenebis(4-hydroxy coumarin) [1]. Further work by Link led in 1948 to the synthesis of warfarin, which was initially approved as a rodenticide in the USA in 1952, and then for human use in 1954. The name warfarin is derived from WARF (Wisconsin Alumni Research Foundation) and –arin from coumarin. Warfarin is now the most widely used anticoagulant in the world. Given the recent demise of ximelagatran, the first oral thrombin inhibitor, it is likely to maintain its place for many years to come. In the UK it has been estimated that at least 1% of the whole population and 8% of those aged over 80 years are taking warfarin [2, 3]. The increase in its use over the last decade can undoubtedly be traced to overwhelming evidence of its effectiveness in preventing embolic strokes in patients with atrial fibrillation [4]. The main adverse effect associated with warfarin is bleeding. Major and fatal bleeding events occur respectively at rates of 7.2 and 1.3 per 100 patient-years, according to a meta-analysis of 33 studies [5]. Warfarin is also number three on the list of drugs implicated in causing hospital admission through adverse effects [6]. Warfarin’s narrow therapeutic index makes it difficult to maintain patients within a defined anticoagulation range. A recent analysis of 6454 patients with atrial fibrillation taking warfarin showed that for almost 50% of the time, the INR was outside the target range of 2–3 [7]. An INR over 3 increases the risk of bleeding, while an INR less than 2 increases the risk of thrombotic events [8]. The problem is further compounded by the fact that individual dosage requirements vary widely between and within individuals (more about this later). Intuitively, one would expect that the more closely you monitor patients, the more likely you will be to hit the desired target range. Indeed, this seems to be the case [8], but there are no good guidelines on how often patients should be monitored. Herein also lies a problem of resources: the more closely you monitor patients, the more expensive the direct costs to your service [9]. Of course, this does not take into account the savings that may be made through preventing hospital admissions from either under- or over-anticoagulation, but it nevertheless informs monitoring practice. Consequently, the frequency of monitoring varies widely in different places [8]. The usual model of care of patients taking anticoagulants involves attendance at a physician-run hospital-based clinic. However, over the last decade there has been increasing interest in developing other models of care. These have included anticoagulation clinics based in primary care [10] and self-monitoring [11], both of which are as effective as hospital-based monitoring, or more so. In this issue, Chan et al.[12] show that pharmacists were more effective, and less costly, than physicians at achieving target INRs in Chinese patients. This finding is consistent with US and UK comparisons of pharmacist- and physician-managed anticoagulant clinics [13–15]. Nurses are also safe and effective in managing anticoagulant clinics [16], which is reflected by the increased number of anticoagulant specialist nurses in the UK. These findings do not indicate that physicians have inadequate knowledge or expertise (in the trials many were experienced haematologists), but rather reflect the fact that there was often increased frequency of monitoring, contact time, and advice between clinic visits in clinics run by other health-care professionals, a luxury not afforded to physicians. There can be no doubt that managing patients taking warfarin requires a multi-disciplinary and multi-functional approach. Patient education should be an important component, although surprisingly little attention has been paid to this [17]. Warfarin is associated with other adverse effects, including skin necrosis and hair loss. A population-based case-control study in 2000 suggested that warfarin treatment was associated with an increased risk of at-fault car crashes [18]. Since warfarin does not affect psychomotor performance, the finding was thought to be due to the diseases for which warfarin was being used, rather than a direct effect of warfarin itself. However, the association between warfarin and road traffic accidents was not replicated in a recent study published in the Journal[19], and this is again emphasized in this issue [20]. Nevertheless, as Alvarez points out [21], assessment of whether drugs cause road traffic accidents is highly complex, and confounding by indication, concomitant medications, alcohol intake, and driving experience can all influence the findings. It is therefore not surprising that replication of initial findings is often difficult. So where are we heading with warfarin prescribing? Warfarin will continue to be the oral anticoagulant of choice, possibly for the next decade, while we await an oral thrombin inhibitor that is both effective and safe. In the meantime, there is increasing interest in improving warfarin dosage regimens by elucidating the environmental and genetic factors that determine dosage requirements. Individual warfarin dosages are highly variable and range from 0.5 mg/day to over 20 mg/day [3]. Environmental factors that determine dosage requirements include concomitant medications, diet, and alcohol intake. More recently, genetic polymorphisms in the genes encoding CYP2C9, the main enzyme responsible for the metabolism of S-warfarin, the more potent of warfarin’s two stereoisomers, and VKORC1, vitamin K [ep]oxide reductase, the enzyme that warfarin inhibits, have been shown to act as major determinants of warfarin dosage requirements [3]. Combining age and body surface area together with genetic polymorphisms in CYP2C9 and VKORC1 accounts for 55% of the variance in dosage requirements [22]. It has been suggested that this may serve to improve the benefit to harm balance of warfarin therapy, but the clinical value of this approach needs to be proven. Further studies are currently being carried out in the UK and elsewhere to identify other genetic and non-genetic factors that better predict warfarin dosage requirements. These studies, if successful, may herald a new era of personalized medicine, in which the dosage of warfarin, and hence INR control, can be better predicted through the development of algorithms that use environmental and genetic factors as co-variates. The importance of this lies not only in improving the use and safety of warfarin, but because it also serves as a paradigm for introducing pharmacogenetics into other therapeutic areas. Competing interests: Munir Pirmohamed is Principal Investigator for a study, funded by the UK Department of Health, evaluating environmental and genetic factors that determine warfarin dosage requirements.

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)0,004
score de la tête « metaresearch » (Gemma)0,014
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: aucune
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,078

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,014
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0020,003
Communication savante0,0040,008
Science ouverte0,0010,002
Intégrité de la recherche0,0060,010
Charge utile insuffisante (le modèle a refusé de juger)0,0230,014

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 Opus0,146
Tête enseignante GPT0,479
Écart entre enseignants0,332 · 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

Citations332
Publié2006
Routes d'admission1
Résumé présentoui

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