Literature DB >> 35041044

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

Nazanin Falconer1,2,3, Michael Barras4,5, Ahmad Abdel-Hafiz5, Sam Radburn5, Neil Cottrell4,6.   

Abstract

PURPOSE: To externally evaluate the performance of two European risk prediction models, for identifying patients at high-risk of medication harm, in an Australian hospital setting.
METHODS: This 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.
RESULTS: The 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).
CONCLUSION: Neither 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.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Adverse drug events; Adverse drug reactions; Clinical pharmacology; Clinical pharmacy; Medication harm; Predictive risk model; Predictive risk score; Risk prediction

Mesh:

Year:  2022        PMID: 35041044     DOI: 10.1007/s00228-021-03271-1

Source DB:  PubMed          Journal:  Eur J Clin Pharmacol        ISSN: 0031-6970            Impact factor:   2.953


  1 in total

1.  A Review of Challenges and Opportunities in Machine Learning for Health.

Authors:  Marzyeh Ghassemi; Tristan Naumann; Peter Schulam; Andrew L Beam; Irene Y Chen; Rajesh Ranganath
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2020-05-30
  1 in total

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