SU112. The Dark Side of Haloperidol Decanoate Shortage in Canada
Notice bibliographique
Résumé
Background: Drug shortages have become an issue of growing interest for health care providers and patients. The number of drug shortages has been rapidly escalating in the last decade all over the world and affecting all types of drugs. In July 2015, American Society of Hospital Pharmacy (ASHP) reports 265 active-drug shortages. Among impacts of such shortages are safety risks, including compromised efficacy, increased side effects burden, more frequent medication errors, and that lead to higher hospital expenses, spanning higher costs for substitute drug, increased costs for health care professionals to deal with switches and rehospitalization. Subsequent shortage of the alternative drug may add to the complexity of the situation over time. In treatment of schizophrenia (Sz), it is only in the recent years that drug shortages became an issue in Canada (Piportil, 2014; Haldol, 2015; Modecate, 2016). In such population, long-acting injectable antipsychotic (LAIAs) are traditionally used in nonadherent, so called “difficult to follow” patients even though, there is a lot of recent literature favoring earlier use of LAIAs in the course of treatment. Haloperidol decanoate (HD) is a first-generation LAIA indicated in the treatment of schizophrenia. In Canada, in 2015, a 6-month shortage of HD led to the obligation of switching patients from this antipsychotic to another drug. Methods: We report a retrospective chart-review mirror study of the 6 months before and after switching HD in 61 patients followed in a third-line psychiatric hospital facility in Quebec city. Results: Patients were mainly suffering from Sz (80%; BP 20%). A significant proportion of patients were also suffering from comorbid personality disorders 46% and/or 57% substance use disorders. Mean age was 50 years old. For 30% of the patients, extrapyramidal syndrome (such as tardive dyskinesia) was described with use of HD before switching. For 60% of patients, HD had been used for more than 10 years. Fifty-eight percentage of the cohort had not been hospitalized during the previous 2 years. A third of the patients were switched to fluphenazine décanoate (FD), while another third were switched to oral haloperidol, the others to various other antipsychotics (only 1 patient to clozapine). Over all, in the 6 month before switching, number of hospitalization days required for this cohort was 800 days compared to 1185 days in the 6 months after, with an individual “mean hospitalization duration of 31 days before compared to 40 days afterwards, corresponding to cost increase of $180 000, only including hospitalizations”. Conclusion: HD shortage had significant impacts, particularly on hospitalizations, in this cohort according to our retrospective chart review. Moreover, in summer 2016, FD itself became back-ordered meaning a new switch for many of those vulnerable patients. These results call for a more vigorous and coordinated reaction from our health care authorities to avoid such situations.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».