eprintid: 9257
rev_number: 9
eprint_status: archive
userid: 2098
dir: disk0/00/00/92/57
datestamp: 2024-09-17 02:55:08
lastmod: 2024-09-17 02:55:08
status_changed: 2024-09-17 02:55:08
type: article
metadata_visibility: show
contact_email: repository@staff.ukdw.ac.id
creators_name: , Rizaldy Taslim Pinzon
creators_id: 0517057601
title: RISK PREDICTION MODELS ON ADVERSE DRUG REACTIONS: A REVIEW
ispublished: pub
subjects: R1
divisions: fak_kedokteran
full_text_status: public
keywords: Adverse drug reaction; Model; Prediction; Risk
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 ADR 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.
date: 2023-10-10
publication: Journal of Pharmacy Education
volume: 23
number: 4
publisher: International Pharmaceutical Federation
pagerange: 11-15
id_number: doi:10.46542/pe.2023.234.1115
refereed: TRUE
issn: 1477-2701
official_url: https://doi.org/10.46542/pe.2023.234.1115
funders: fivy_k@ugm.ac.id
citation:   Rizaldy Taslim Pinzon  (2023) RISK PREDICTION MODELS ON ADVERSE DRUG REACTIONS: A REVIEW.  Journal of Pharmacy Education, 23 (4).  pp. 11-15.  ISSN 1477-2701     
document_url: https://katalog.ukdw.ac.id/9257/1/Risk%20Prediction%20Models.pdf