Risk prediction models on adverse drug reactions: A review

Kurniawati, F. and Kristin, E. and Febriana, S.A. and Pinzon, R.T. (2023) Risk prediction models on adverse drug reactions: A review. Pharmacy Education, 23 (4). pp. 11-15. ISSN 15602214

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Abstract

Background: The risk prediction model has become increasingly popular in recent years in helping clinical decision-making. Existing models cannot be directly applied in Indonesia. Objective: To review the existing prediction models and their limitations. Method: A search related to the prediction of ADRs risk was conducted using several journal databases: PubMed, Scopus and Google Scholar. Articles were screened to match specified criteria and further studied. Result: Nine articles met the criteria and were then analysed. Studies were carried out in various countries. The study population include; the elderly (>65 years, three studies), age (�15 years, three studies), patients with Chronic Kidney Disease (CKD) (�18 years, one study) and two studies in cancer patients. The outcomes were; ADR (five studies), ADE (two studies), DRPs (one study), and cardiovascular effects (one study). The methods for determining the predictors of ADRs all used multivariable logistic regression. Conclusion: Each country has different treatment patterns, prescribing practices, traditions and drug distribution, so it is necessary to develop a prediction model for ADRs that is country-specific. © 2023 FIP.

Item Type: Article
Additional Information: cited By 0
Uncontrolled Keywords: adverse event; aged; antibody dependent enhancement; cardiovascular effect; chronic kidney failure; clinical decision making; drug distribution; female; human; male; prediction; prescribing practice; Review; risk factor; search engine; systematic review
Subjects: R Medicine > RM Therapeutics. Pharmacology
Divisions: Faculty of Medicine, Public Health and Nursing > Non Surgical Divisions
Depositing User: Ani PURWANDARI
Date Deposited: 22 Aug 2024 08:41
Last Modified: 22 Aug 2024 08:41
URI: https://ir.lib.ugm.ac.id/id/eprint/2930

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