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Record W3182760497 · doi:10.4212/cjhp.v74i3.3148

Patient Factors Associated with Pharmaceutical Interventions for Inpatients at a Brazilian Teaching Hospital

2021· article· en· W3182760497 on OpenAlexvenueno aff
Debora Bernardes Francisco, Karine Dal-Paz, Thiago Vinícius Nadaleto Didone

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical prescriptionPsychological interventionOdds ratioPharmaceutical careEmergency medicineConfidence intervalLogistic regressionRetrospective cohort studyComorbidityMedical recordInternal medicinePharmacyFamily medicinePharmacologyNursing

Abstract

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Background: Pharmaceutical interventions aim to correct or prevent a drug-related problem (DRP) that might lead to negative clinical consequences and increase health care costs. Objective: To identify variables associated with the provision of pharmaceutical interventions by clinical pharmacists during hospitalization. Methods: In this retrospective cohort study, adult inpatients of the medical ward of the University Hospital of the University of São Paulo in São Paulo, Brazil, were followed from admission to discharge. Logistic regression models were used to evaluate the association between occurrence of at least 1 pharmaceutical intervention and the following baseline characteristics: sex, age, Charlson comorbidity index, renal failure, electrolyte imbalance, hemoglobin, platelet count, and use of a nasoenteric tube, as well as the number, second-level Anatomical Therapeutic Chemical (ATC) code, and administration route of prescribed medications. Results: A total of 148 patients were included in the study, of whom 75 (50.7%) were men. The mean age was 62.8 (95% confidence interval [CI] 59.9–65.8) years, and the mean length of the hospital stay was 10.7 (95% CI 8.4–13.1) days. Analgesics (ATC code N02), the most common type of medication, were prescribed to 144 (97.3%) of the patients. Pharmaceutical interventions were performed for only 49 (33.1%) of the patients. One out of every 4 of these interventions was intended to obtain information not provided in the prescription, to allow the prescription to be completed and dispensing to proceed. According to the multivariate analysis, the odds ratio (OR) of occurrence of at least 1 pharmaceutical intervention increased for patients with electrolyte imbalance (OR 2.68, 95% CI 1.09–6.63; p = 0.033), patients using 5 to 8 medications (OR 8.73, 95% CI 1.07–71.36; p = 0.043), patients using 9 or more medications (OR 10.39, 95% CI 1.28–84.05; p = 0.028), and patients using at least 1 systemic antibacterial (ATC code J01; OR 2.76, 95% CI 1.30–5.84; p = 0.008). Conclusions: The findings of this study could allow the identification, at the time of admission and possibly before the occurrence of a DRP, of patients at higher risk of requiring a pharmaceutical intervention later during their hospital stay. To optimize patient care, clinical pharmacists should closely follow inpatients with electrolyte imbalance, polypharmacy, and/or use of systemic antibacterials. RÉSUMÉ Contexte : Les interventions pharmaceutiques visent à corriger ou à prévenir un problème lié aux drogues (PLD), qui pourrait entraîner des conséquences cliniques négatives et accroître les coûts des soins de santé. Objectif : Déterminer les variables associées aux interventions pharmaceutiques des pharmaciens cliniques lors d’une hospitalisation. Méthodes : Dans cette étude de cohorte rétrospective, les patients adultes hospitalisés au Service de médecine de l’Hôpital universitaire de São Paulo au Brésil ont été suivis dès leur admission et jusqu’à leur sortie. Des modèles de régression logistique ont été utilisés pour évaluer l’association entre au moins une intervention pharmaceutique et les caractéristiques de base suivantes : sexe, âge, indice de comorbidité de Charlson, insuffisance rénale, déséquilibre électrolytique, hémoglobine, numération plaquettaire et utilisation d’un tube nasoentérique, et l’ensemble du groupe a subi une évaluation selon le nombre de médicaments prescrits au deuxième niveau des classifications du Système de classification anatomique thérapeutique chimique (ATC) et leur voie d’administration. Résultats : Cent-quarante-huit (148) patients ont été inclus dans cette étude; 75 d’entre eux (50,7 %) étaient des hommes. L’âge moyen était de 62,8 ans (95 % intervalle de confiance [IC] 59,9 - 65,8), et la durée moyenne du séjour à l’hôpital était de 10,7 jours (95 % IC 8,4 – 13,1). Des analgésiques (code ATC N02), type de médicament le plus répandu, ont été prescrits à 144 patients (97,3 %). Seuls 49 patients (33,1 %) ont fait l’objet d’une intervention pharmaceutique. Une de ces interventions sur quatre avait pour but d’obtenir des informations absentes dans la prescription mais indispensables à l’obtention de la validation de la prescription et de l’autorisation de distribution des médicaments. Selon l’analyse multivariée, le rapport de cotes (RC) de la nécessité d’au moins une intervention pharmaceutique augmentait pour les patients ayant un déséquilibre électrolytique (RC 2,68, 95 % IC 1,09 - 6,63; p = 0,033), les patients prenant entre cinq et huit médicaments (RC 8,73, 95 % IC 1,07 - 71,36; p = 0,043), les patients prenant au moins neuf médicaments (RC 10,39, 95 % IC 1,28 - 84,05; p = 0,028) et ceux utilisant au moins un antibactérien systémique (code ATC J01; RC 2,76, 95 % IC 1,30–5,84; p = 0,008). Conclusions : Les résultats de cette étude pourraient permettre d’identifier, à l’admission à l’hôpital et probablement avant l’apparition d’un PLD, les patients présentant des risques plus élevés, qui pourraient nécessiter une intervention pharmaceutique plus tard lors de leur séjour. Pour optimiser les soins aux patients, les pharmaciens cliniques doivent suivre étroitement les patients hospitalisés ayant un déséquilibre électrolytique, ceux qui nécessitent une polypharmacie et ceux qui utilisent des antibactériens systémiques.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.129
GPT teacher head0.405
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2021
Admission routes1
Has abstractyes

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