Medication-related harm in older adults following hospital discharge
Development and validation of a prediction tool
Nikesh Parekh (Brighton and Sussex Medical School)
Khalid Ali (Brighton and Sussex Medical School)
John Graham Davies (King’s College London)
Jennifer M. Stevenson (King’s College London)
Winston Banya (Royal Brompton and Harefield NHS Foundation Trust)
Stephen Nyangoma (Royal Brompton and Harefield NHS Foundation Trust)
Rebekah Schiff (Guy's and St Thomas’ NHS Foundation Trust)
Tischa Van Der Cammen (TU Delft - Industrial Design Engineering)
Jatinder Harchowal (The Royal Marsden NHS Foundation Trust)
Chakravarthi Rajkumar (Brighton and Sussex Medical School)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Objectives To develop and validate a tool to predict the risk of an older adult experiencing medication-related harm (MRH) requiring healthcare use following hospital discharge. Design, setting, participants Multicentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015. Primary outcome measure Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non-adherence, harm from medication error). Three data sources provided MRH and healthcare use information: Hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated. Results 1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases (â 'possible' and 'aprobable') were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced 'definite' MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: Age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool's discrimination C-statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches. Conclusions The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool's evaluation in other settings.
help_outline