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Enregistrement W4382136311 · doi:10.1177/20543581231165712

Electronic Decision Support for Deprescribing in Patients on Hemodialysis: Clinical Research Protocol for a Prospective, Controlled, Quality Improvement Study

2023· article· en· W4382136311 sur OpenAlexafffundabout
Émilie Bortolussi‐Courval, Tiina Podymow, Emilie Trinh, Joseph Moryousef, Ryan Hanula, Jean‐François Huon, Thomas A. Mavrakanas, Rita S. Suri, Todd C. Lee, Emily G. McDonald

Notice bibliographique

RevueCanadian Journal of Kidney Health and Disease · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueElectronic Health Records Systems
Établissements canadiensCentre for Advancing Health OutcomesMcGill University Health Centre
Organismes subventionnairesMcGill University Health CentreMcGill University
Mots-clésMedicineDeprescribingHemodialysisIntensive care medicineProtocol (science)Prospective cohort studyPolypharmacyClinical decision support systemEmergency medicineInternal medicineDecision support systemData miningAlternative medicinePathology

Résumé

récupéré en direct d'OpenAlex

Background: Patients on dialysis are commonly prescribed multiple medications (polypharmacy), many of which are potentially inappropriate medications (PIMs). Potentially inappropriate medications are associated with an increased risk of falls, fractures, and hospitalization. MedSafer is an electronic tool that generates individualized, prioritized reports with deprescribing opportunities by cross-referencing patient health data and medications with guidelines for deprescribing. Objectives: Our primary aim was to increase deprescribing, as compared with usual care (medication reconciliation or MedRec), for outpatients receiving maintenance hemodialysis, through the provision of MedSafer deprescribing opportunity reports to the treating team and patient empowerment deprescribing brochures provided directly to the patients themselves. Design: This controlled, prospective, quality improvement study with a contemporary control builds on existing policy at the outpatient hemodialysis centers where biannual MedRecs are performed by the treating nephrologist and nursing team. Setting: The study takes place on 2 of the 3 outpatient hemodialysis units of the McGill University Health Centre in Montreal, Quebec, Canada. The intervention unit is the Lachine Hospital, and the control unit is the Montreal General Hospital. Patients: A closed cohort of outpatient hemodialysis patients visit one of the hemodialysis centers multiple times per week for their hemodialysis treatment. The initial cohort of the intervention unit includes 85 patients, whereas the control unit has 153 patients. Patients who are transplanted, hospitalized during their scheduled MedRec, or die before or during the MedRec will be excluded from the study. Measurements: We will compare rates of deprescribing between the control and intervention units following a single MedRec. On the intervention unit, MedRecs will be paired with MedSafer reports (the intervention), and on the control unit, MedRecs will take place without MedSafer reports (usual care). On the intervention unit, patients will also receive deprescribing patient empowerment brochures for select medication classes (gabapentinoids, proton-pump inhibitors, sedative hypnotics and opioids for chronic non-cancer pain). Physicians on the intervention unit will be interviewed post-MedRec to determine implementation barriers and facilitators. Methods: The primary outcome will be the proportion of patients with 1 or more PIMs deprescribed on the intervention unit, as compared with the control unit, following a biannual MedRec. This study will build on existing policies aimed at optimizing medication therapy in patients undergoing maintenance hemodialysis. The electronic deprescribing decision support tool, MedSafer, will be tested in a dialysis setting, where nephrologists are regularly in contact with patients. MedRecs are an interdisciplinary clinical activity performed biannually on the hemodialysis units (in the Spring and Fall), and within 1 week following discharge from any hospitalization. This study will take place in the Fall of 2022. Semi-structured interviews will be conducted among physicians on the intervention unit to determine barriers and facilitators to implementation of the MedSafer-supplemented MedRec process and analyzed according to grounded theory in qualitative research. Limitations: Deprescribing can be limited due to nephrologists' time constraints, cognitive impairment of the hemodialyzed patient stemming from their illness and complex medication regimens, and lack of sufficient patient resources to learn about the medications they are taking and their potential harms. Conclusions: Electronic decision support can facilitate deprescribing for the clinical team by providing a nudge reminder, decreasing the time it takes to review and effectuate guideline recommendations, and by lowering the barrier of when and how to taper. Guidelines for deprescribing in the dialysis population have recently been published and incorporated into the MedSafer software. To our knowledge, this will be the first study to examine the efficacy of pairing these guidelines with MedRecs by leveraging electronic decision support in the outpatient dialysis population. Trial registration: This study was registered on Clinicaltrials.gov (NCT05585268) on October 2, 2022, prior to the enrolment of the first participant on October 3, 2022. The registration number is pending at the time of protocol submission.

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)0,052
score de la tête « metaresearch » (Gemma)0,041
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai non randomisé · Signal consensuel: aucune
GenreSignal candidat: Protocole · Signal consensuel: Protocole
Score de désaccord entre enseignants0,052
Score d'incertitude au seuil0,272

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0520,041
Méta-épidémiologie (sens strict)0,0040,002
Méta-épidémiologie (sens large)0,0070,005
Bibliométrie0,0030,006
Études des sciences et des technologies0,0040,003
Communication savante0,0030,003
Science ouverte0,0030,002
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0220,003

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 Opus0,205
Tête enseignante GPT0,574
Écart entre enseignants0,370 · 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'étudeEssai non randomisé
Domainenon disponible
GenreProtocole

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

En bref

Citations7
Publié2023
Routes d'admission3
Résumé présentoui

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