2018
DOI: 10.1111/bcp.13514
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Systematic review of predictive risk models for adverse drug events in hospitalized patients

Abstract: Keywords adverse drug events, adverse drug reactions, clinical pharmacology, clinical pharmacy, drug related problems, medication errors, predictive risk model, risk score AIMAn emerging approach to reducing hospital adverse drug events is the use of predictive risk scores. The aim of this systematic review was to critically appraise models developed for predicting adverse drug event risk in inpatients. METHODSEmbase, PubMed, CINAHL and Scopus databases were used to identify studies of predictive risk models f… Show more

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Cited by 61 publications
(91 citation statements)
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References 72 publications
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“…Screening for RI at hospital admission is especially important considering that up to 72% of non-hospitalized CKD patients are not aware of their kidney insufficiency [2] and RI is an established risk factor for DRP [32]. Regarding the adjustment of drug therapy to renal function, it is vital to understand which estimation of GFR or renal clearance to use.…”
Section: Discussionmentioning
confidence: 99%
“…Screening for RI at hospital admission is especially important considering that up to 72% of non-hospitalized CKD patients are not aware of their kidney insufficiency [2] and RI is an established risk factor for DRP [32]. Regarding the adjustment of drug therapy to renal function, it is vital to understand which estimation of GFR or renal clearance to use.…”
Section: Discussionmentioning
confidence: 99%
“…The CFIR acknowledges the importance of accounting for patient characteristics and needs, prioritisation and the availability of resources [31]. Validated tools to identify patients at high risk for medicationrelated problems could be used to make more efficient use of available resources [34]. These tools may require selfassessment by the patient [35] or algorithm-based screening of the electronic medical record [36,37].…”
Section: Discussionmentioning
confidence: 99%
“…At this initial stage no upper limit was placed on the number of candidate variables 26 38. All candidate variables were selected based on clinical relevance and not using univariable analysis to avoid the possibility of variable inclusion by statistical chance 39 40. Once the number of MRH events in the study was known, the number of variables was reduced for model development recognising 10 events per variable by Peduzzi et al as a rough indicator 38.…”
Section: Methodsmentioning
confidence: 99%