Evidence map›Paper›PMID 35041044›Full record

ArticleEuropean journal of clinical pharmacology2022

Evaluation of two European risk models for predicting medication harm in an Australian patient cohort.

Nazanin Falconer, Michael Barras, Ahmad Abdel-Hafiz, Sam Radburn, Neil Cottrell

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Article in European journal of clinical pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nazanin FalconerSchool of Pharmacy, Pharmacy Australia Centre of Excellence, The University of Queensland, 20 Cornwall Street, BrisbaneWoolloongabba, Brisbane, QLD, 4102, Australia. n.ghahremanfalconer@uq.edu.au.ORCID http://orcid.org/0000-0003-4682-7890
Michael BarrasSchool of Pharmacy, Pharmacy Australia Centre of Excellence, The University of Queensland, 20 Cornwall Street, BrisbaneWoolloongabba, Brisbane, QLD, 4102, Australia.
Ahmad Abdel-HafizPrincess Alexandra Hospital, Metro South Health, Brisbane, QLD, 4102, Australia.
Sam RadburnPrincess Alexandra Hospital, Metro South Health, Brisbane, QLD, 4102, Australia.
Neil CottrellSchool of Pharmacy, Pharmacy Australia Centre of Excellence, The University of Queensland, 20 Cornwall Street, BrisbaneWoolloongabba, Brisbane, QLD, 4102, Australia.

Funding

The University of Queensland PhD Scholarship (RTP)
6 · The paper itself

Abstract

purposeTo externally evaluate the performance of two European risk prediction models, for identifying patients at high-risk of medication harm, in an Australian hospital setting.

methodsThis was a secondary analysis of a pre-existing cohort study described in a recently published study by Falconer et al. (Br J Clin Pharmacol 87(3):1512-1524, 2021) describing the development of a predictive risk model for inpatient medication harm. We retrospectively extracted relevant variables using the electronic health records of general medical and geriatric patients admitted to a quaternary hospital, in Brisbane, over 6 months from July to December 2017. This dataset was used to externally evaluate the two European models, The Brighton Adverse Drug Reaction Risk (BADRI) model by Tangiisuran et al. and a risk model developed by Trivalle et al. The variables were entered into both models and the patients' risk of medication harm was calculated, and compared with actual patient outcomes. Predictive performance was evaluated by measuring area under the receiver operative characteristic (AuROC) curves.

resultsThe Australian patient cohort included 1982 patients (median age 74 years), of which 136 (7%) patients experienced ≥ 1 medication harm event(s). External evaluation of the two European models identified that both the BADRI and the Trivalle models had reduced predictive performance in an Australian patient cohort, compared with their original studies (AuROC of 0.63 [95% CI: 0.58-0.68] and 0.60 [95% CI: 0.55-0.65], respectively).

conclusionNeither model demonstrated sufficient discrimination to warrant further evaluation in our local setting. This is likely a result of variations between the development and the validation cohorts, and the change in healthcare systems over time, and highlights the need for an up-to-date and context-specific risk prediction model.

Indexed as

HospitalizationModels, StatisticalAgedArea Under CurveAustraliaCohort StudiesDrug-Related Side Effects and Adverse ReactionsHumansRetrospective StudiesRisk FactorsAdverse drug eventsAdverse drug reactionsClinical pharmacologyClinical pharmacyMedication harmPredictive risk modelPredictive risk scoreRisk prediction

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.