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Background: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable., Aim: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients., Methods: A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA)., Results: A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4., Conclusion: The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To determine the burden, on the ED, of harm from unintentional adverse drug events (ADEs) in the community., Methods: A retrospective, observational study of 936 randomly selected presentations to a level 6 ED at a principal referral hospital in Brisbane, Australia, in November 2017. Clinical records were screened by a pharmacist, who identified suspected ADEs. All suspected ADEs and a random selection of presentations without ADEs were reviewed by an expert panel, which classified, by consensus: occurrence and type of ADE, contribution of ADE to presentation, severity of harm and preventability of presentation. Medication-related ED presentations (ADE-Ps) and potential ADEs were, respectively, defined as presentations directly attributable to an ADE, and medication events that occurred but did not cause the ED presentation. Descriptive data analysis was performed., Results: The median (interquartile range) age of patients was 40 (27-58) years, with 49.7% (95% confidence interval [CI] 46.5-52.9) being male. The prevalences of ADE-Ps and potential ADEs were 9.2% (95% CI 7.5-11.3) and 5.0% (95% CI 3.8-6.6), respectively. The severity of harm was classified as 'death or likely permanent harm' in 4.7% (95% CI 0.2-9.1) of ADE-Ps, 'temporary harm' (89.5%, 95% CI 83.1-96.0) and 'minimal or no harm' (5.8%, 95% CI 0.9-10.8). Most (79.1%, 95% CI 70.5-87.7) ADE-Ps were preventable., Conclusions: There is a high burden on emergency care because of unintended medication harm in the community. Interventions to reduce such harm are likely to require a co-ordinated primary, acute and public healthcare response. The high proportion of presentations with potential ADEs indicates opportunity for harm mitigation in the ED., (© 2022 Australasian College for Emergency Medicine.)

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.)

Background: Medication harm is experienced by up to 30% of hospitalised patients, of which 7% experience severe harm. Pharmacist review can mitigate this harm. However, in increasingly busy hospitals, with high patient throughput, and scarce resources, there is a need to prioritise patients. Current methods are cumbersome, include many risk factors and are not evaluated in the clinical setting., Objectives: To determine key criteria used by hospital pharmacists and investigate perspectives related to patient prioritisation for potential medication harm in the hospital setting., Methods: This study used two methods; focus groups and a cross-sectional survey of Australian hospital pharmacists. Focus groups were used to identify criteria and perspectives related to prioritisation and were analysed thematically. Criteria from focus groups, and a systematic review, were used to design the survey. The survey was distributed via the Society of Hospital Pharmacists of Australia. The top 10 prioritisation criteria, and associated sub-criteria selected by over 50% of respondents were ranked. Combination of criteria used most frequently on a day-to-day basis were identified., Results: Twenty clinical pharmacists participated in four, one-hour, audio recorded focus groups. Using inductive thematic analysis of transcripts three themes were identified; 1) prioritisation criteria, 2) barriers to, and 3) facilitators of patient prioritisation, with five sub-themes and 26 codes. Pharmacists identified a number of barriers such as a lack of relevant handover information. Organisational demands, such as patient discharge and medications supply also influenced priority and could act as barriers to a pharmacist enacting their prioritisation plan. A total of 231 pharmacists completed the survey. High priority criteria included, renal impairment, use of high-risk medications and therapeutic drug monitoring., Conclusion: Pharmacists described prioritisation as a multifactorial process with a focus on high-risk medications and renal impairment. These findings will inform the development of a predictive risk score for patient prioritisation., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Aim: An 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., Methods: Embase, 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., Results: Eleven 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., Conclusion: Adverse 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., (© 2018 The British Pharmacological Society.)