Preventable sources of medication related morbidity and mortality at transitions in care for hospitalized patients
Notice bibliographique
Résumé
With the aging of the population and advances in medical care and public health, a growing proportion of individuals have multiple chronic conditions. Indeed, in Canada, almost half of those aged 65 years and older are living with multiple co-existing chronic conditions. The impact of multimorbidity is multifaceted, leading to lower quality of life, declines in functional status, higher rates of disability and increased mortality. For healthcare systems, multimorbidity leads to greater healthcare utilization, especially of high-cost services such as hospital stays and emergency department visits. One of the most complex aspects of providing optimal care to patients with multimorbidity is their use of many different medications. Approximately 60% of multimorbid patients use five or more medications which in turn increases their risk of drug-drug and drug-disease interactions, and adverse drug events (ADEs). Despite the clinical challenges associated with treating complex patients with many medications, evidence-based guidance for safe and effective prescribing in the context of multimorbidity remains limited. Patients with multiple chronic conditions are likely to require repeated admission to hospital, and once discharged, over one-third will be re-admitted to hospital within 90-days. However, reducing re-admissions in these complex patients has remained challenging since the reason for returning to hospital can include a number of interlinked patient-, provider- and healthcare system-level factors. The impact of patient medications is of significant interest since a large proportion of re-admissions are related to ADEs. When patients are hospitalized, they are often discharged on substantially different medications than those prior to admission. One might expect that discontinuations, additions or modifications to patient drug regimens during hospitalization would reduce the likelihood of adverse health outcomes after discharge. However, the extent to which patients actually adhere to hospital medication changes or the appropriateness of these changes is not known. The impact of these factors on short-term patient health outcomes after discharge is also unknown.The overarching goal of this thesis was to explore potentially preventable sources of medication-related morbidity and mortality in hospitalized patients in the transition between hospital and home. Three studies were completed to accomplish this goal.Objective Study 1: Estimate the incidence and determinants of non-adherence to hospital medication changes in the 30-days after hospitalizationObjective Study 2: Estimate the association between non-adherence to medication changes made at hospital discharge on the risk of re-admissions, emergency department visits and death in the 30-days post dischargeObjective Study 3: Estimate the incidence of potentially inappropriate medications prescribed at hospital discharge and their impact on re-admissions, emergency department visits and death as well as drug-related adverse events in the 30-days after discharge
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».