Identifying medication harm in hospitalised patients: a bimodal, targeted approach

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model

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.

Scoping Review of Studies Evaluating Frailty and Its Association with Medication Harm

INTRODUCTION

Frailty is associated with an increased risk of death and morbid events. Frail individuals are known to have multiple comorbidities which are often associated with polypharmacy. Whilst a relationship between polypharmacy and frailty has been demonstrated, it is not clear if there is an independent relationship between frailty and medication harm.

AIMS

This scoping review aimed to identify and critically appraise studies evaluating medication harm in patients with frailty.

METHODS

PubMed, EMBASE, CINAHL and Cochrane databases were searched from inception until 1 February 2021 using key search terms that are synonymous with frailty (such as frail and frail elderly) and medication harm (such as adverse drug events and adverse drug reactions). To be included, studies must have identified medication harm as a primary or secondary outcome measure, and used a frailty assessment tool to determine frailty, or clearly defined how frailty was assessed. Data were narratively synthesised and presented in tables. The checklist from the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies from the National Heart, Lung, and Blood Institute was used to assess the quality and risk of bias of studies that met the inclusion criteria.

RESULTS

Of 2685 retrieved abstracts, 24 underwent full-text review and nine studies met the inclusion criteria. Three studies were retrospective cohort studies, and six were prospective observational studies. Six studies comprised two distinct groups of frail and non-frail individuals, and the remaining three studies evaluated medication harm in an entirely frail population. Seven studies used validated frailty tools such as the Clinical Frailty Scale, Fried Frailty Index, and Fried Frailty Phenotype. Two studies measured frailty using self-defined criteria. Overall, frail individuals were at risk of medication harm with rates ranging between 18.7 and 77% across the nine studies. However, whether frailty is an independent predictor of medication harm remains uncertain, as this was only evaluated in one study. The risk of bias assessment identified limitations in methods and reporting with all nine studies.

CONCLUSION

This scoping review identified nine studies evaluating medication harm in frail patients. However, all were limited by the methodological quality and inadequate reporting of study factors. There are few high-quality studies that described a relationship between medication harm and frailty. More robust studies are required that examine the independent relationship between frailty and medication harm, after adjusting for all possible confounders and in particular polypharmacy.