Clinical prediction rule to identify high‐risk inpatients for adverse drug events: the JADE Study

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 for hospitalized adult inpatients. Studies had to have used multivariable logistic regression for model development, resulting in a score or rule with two or more variables, to predict the likelihood of inpatient adverse drug events. The Checklist for the critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) was used to critically appraise eligible studies. RESULTSEleven studies met the inclusion criteria and were included in the review. Ten described the development of a new model, whilst one study revalidated and updated an existing score. Studies used different definitions for outcome but were synonymous with or closely related to adverse drug events. Four studies undertook external validation, five internally validated and two studies did not validate their model. No studies evaluated impact of risk scores on patient outcomes. CONCLUSIONAdverse drug event risk prediction is a complex endeavour but could help to improve patient safety and hospital resource management. Studies in this review had some limitations in their methods for model development, reporting and validation. Two studies, the BADRI and Trivalle's risk scores, used better model development and validation methods and reported reasonable performance, and so could be considered for further research.British Journal of Clinical Pharmacology Br J Clin Pharmacol (2018) 84 846-864 846

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“…‘Number of medications’ featured in nine risk scores, except in models by Sakuma et al . and McElnay et al . .…”

Section: Resultsmentioning

confidence: 90%

“…The study by Sakuma et al . was the only one to measure all types of ADEs, including medication errors that resulted in harm . Trivalle et al .…”

Section: Resultsmentioning

confidence: 99%

Systematic review of predictive risk models for adverse drug events in hospitalized patients

Falconer

Barras

Cottrell

2018

Brit J Clinical Pharma

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“…To our knowledge, no algorithms exist that evaluate the risk of MEs by the use of risk scores. Other studies developed risk scores by the use of multiple regressions . Hohl et al .…”

Section: Discussionmentioning

confidence: 99%

Detection of Patients at High Risk of Medication Errors: Development and Validation of an Algorithm

Sædder

Lisby

Nielsen

et al. 2015

Basic Clin Pharma Tox

119

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Medication errors (MEs) are preventable and can result in patient harm and increased expenses in the healthcare system in terms of hospitalization, prolonged hospitalizations and even death. We aimed to develop a screening tool to detect acutely admitted patients at low or high risk of MEs comprised by items found by literature search and the use of theoretical weighting. Predictive variables used for the development of the risk score were found by the literature search. Three retrospective patient populations and one prospective pilot population were used for modelling. The final risk score was evaluated for precision by the use of sensitivity, specificity and area under the ROC (receiver operating characteristic) curves. The variables used in the final risk score were reduced renal function, the total number of drugs and the risk of individual drugs to cause harm and drug-drug interactions. We found a risk score in the prospective population with an area under the ROC curve of 0.76. The final risk score was found to be quite robust as it showed an area under the ROC curve of 0.87 in a recent patient population, 0.74 in a population of internal medicine and 0.66 in an orthopaedic population. We developed a simple and robust score, MERIS, with the ability to detect patients and divide them according to low and high risk of MEs in a general population admitted at acute admissions unit. The accuracy of the risk score was at least as good as other models reported using multiple regression analysis.

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“…These include the Drug Burden Index16 and the Anticholinergic Risk Scale,17 both of which strongly correlate anticholinergic drug effects with reduced physical and cognitive decline 18 19. However, their complexity has limited widespread use in clinical practice.Risk scores20–23 or clinical prediction rules24 which estimate the risk of adverse drug events (ADEs) in individual patients based on multivariable regression models which include age, number of medications, renal function and comorbidities.Deprescribing guidelines directed at particular medications (or drug classes) which identify clinical scenarios where a particular drug is likely to be inappropriate and how to safely wean or discontinue it. Such guidelines are proliferating,25–27 although their effects on clinical decision-making are yet to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

“…Risk scores20–23 or clinical prediction rules24 which estimate the risk of adverse drug events (ADEs) in individual patients based on multivariable regression models which include age, number of medications, renal function and comorbidities.…”

Section: Introductionmentioning

confidence: 99%

Review of structured guides for deprescribing

2016

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Avoiding inappropriate polypharmacy has become increasingly recognised as a safety imperative for older patient care. Deprescribing is an active process of reviewing all medications being used by individual patients that prompts clinicians to consider which medications have unfavourable risk–benefit trade-offs in the context of illness severity, advanced age, multi-morbidity, physical and emotional capacity, life expectancy, care goals and personal preferences. Structured guides to deprescribing include algorithms, flow charts or tables which are patient-directed and aim to guide the clinician through sequential steps in deciding which medications should be targeted for discontinuation. In this narrative review, we describe seven structured deprescribing guides whose stated purpose included the reduction of polypharmacy, their use was not restricted to a single drug or drug class and they had undergone some form of efficacy testing. There was considerable heterogeneity in guide design and content, with some guides constituting little more than a set of principles while others entail detailed processes and sub-steps which addressed multiple determinants of drug appropriateness. Evidence of effectiveness for each guide was limited in that none have been evaluated in randomised controlled trials, that pilot or feasibility studies have involved relatively small patient samples, that intra-rater and inter-rater reliabilities have not been determined and that most have been studied in hospital settings. Which is most useful to clinicians is unknown in the absence of head-to-head comparisons. While most guides have face validity, more research is needed for determining effectiveness and ease of use in routine clinical practice, especially in primary care settings.

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Clinical prediction rule to identify high‐risk inpatients for adverse drug events: the JADE Study

Abstract: This prediction rule had the modest predictive ability and could help physicians and other healthcare professionals to make an estimation of patients at high risk for ADEs.

Cited by 26 publications

References 30 publications

Systematic review of predictive risk models for adverse drug events in hospitalized patients

Systematic review of predictive risk models for adverse drug events in hospitalized patients

Detection of Patients at High Risk of Medication Errors: Development and Validation of an Algorithm

Review of structured guides for deprescribing

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