Medication-related harm in older adults following hospital discharge: development and validation of a prediction tool

Abstract

ObjectivesTo 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, participantsMulticentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015.Primary outcome measureParticipants 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.Results1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases (‘possible’ and ‘probable’) 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.ConclusionsThe 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.

Development and validation of the Adverse Inpatient Medication Event model (AIME)

“…Decision curves were constructed for the AIME model, and a simpler ‘Polypharmacy’ model. The Polypharmacy model, based on a common approach of ‘number of medications’ for patient prioritisation, 34 was informed by the average number of medications administered to study participants within the first 24 hours of hospitalisation. The standardised net benefit (equation 1) of applying the models was calculated at different threshold probabilities to create the decision curves.…”

Prevalence and Nature of Medication Errors and Medication-Related Harm Following Discharge from Hospital to Community Settings: A Systematic Review

“…These medication groups may become a focus of attention by researchers and healthcare staff as potential targets for remedial action that could improve patient outcomes [126]. Our review can be used to inform the development and update a medication-related harm prediction tools that focus on post-discharge risk [127], as well as to update and reinforce prescribing and monitoring quality indicators in primary care settings [128][129][130]. Elsewhere these findings could also inform ongoing use of the national health services (National Health Service) New Medicines Service in community pharmacies in the UK [131], which involves counselling the patient starting new medications for chronic diseases including diabetes mellitus and hypertension and for those starting new anticoagulant medications.…”

Medication Regimen Complexity Index Prediction of Adverse Drug Reaction–Related Hospital Admissions

“…4,5 This apparent discrepancy may be partly explained by the difference in outcome used. 4,5 The definition of MRH includes harm related to medication nonadherence as well as ADRs, and so it is possible that MRCI and medication count are more relevant risk factors for harm related to nonadherence than they are to ADRs. This suggests a need to tailor eligibility/inclusion criteria for medication management services more tightly to the desired outcome.…”

“…This finding is interesting in that the PADR-EC tool scoring did not incorporate the medication count, whereas the PRIME tool, using MRH as the dependent variable, did. 4,5 This apparent discrepancy may be partly explained by the difference in outcome used. 4,5 The definition of MRH includes harm related to medication nonadherence as well as ADRs, and so it is possible that MRCI and medication count are more relevant risk factors for harm related to nonadherence than they are to ADRs.…”

“…2,3 Risk prediction tools such as Prediction of hospitalization due to Adverse Drug Reactions in Elderly Community dwelling patients (PADR-EC) and the Prospective study to develop a model to stratify the RIsk of Medication-related harm in hospitalised Elderly patients (PRIME) have been developed to assist in the detection of older patients at risk of MRH. 4,5 Whereas the number of medications is a factor in PRIME (which focused on MRH), the number of medications was not included in the final model for the PADR-EC score (focused on ADRs). However, the number of antihypertensive medications was a strong predictor of ADRs in the PADR-EC study, suggesting that specific medications, as opposed to the total number of medications, may be a stronger predictor of significant MRH leading to hospitalization.…”