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Background: Frailty and medicine-related problems are common among the elderly and have been associated with an increased risk of adverse outcomes. However, there has been limited research on the association between frailty and medicines use. Aim: To determine the distribution of frailty among older people who used medicines and to examine the difference in frailty scores over time with continuation or discontinuation of specific medicines. Methods: The study population was from the Australian Longitudinal Study of Ageing (ALSA). The data used in the analysis were collected in 1992 (wave 1) and in 1995 (wave 3). Frailty scores were assessed at both waves using a modified version of the Frailty Index. Non-parametric tests were used to examine the difference in the distribution of frailty scores in relation to medicines used at baseline. Medicine use was categorised into continued or stopped by comparing use at wave 3 to use at baseline. A t-test was used to compare the mean changes in frailty scores between the two groups, with a p-value of

Background: The Beers criteria are used to identify potentially inappropriate medications (PIMs) in the elderly. Few studies have examined the Beers criteria in elderly hospitalised patients in Australia. Aim: To identify the prevalence of PIM use in elderly patients admitted to an Australian hospital using the 2003 Beers criteria. Method: A prospective cross-sectional study of 100 elderly inpatients was undertaken using the Beers criteria to identify and compare PIM on admission and discharge. Logistic regression analysis examined the risk factors for PIM use. Results: 42% of patients had at least one PIM on admission compared to 39% at discharge. The most common PIMs were amitriptyline, senna, amiodarone and benzodiazepines (nitrazepam and diazepam). Excluding senna, the prevalence decreased to 36% at admission and 24% at discharge. At admission (including and excluding senna), PIM was not significantly associated with age, gender, living arrangements, number of comorbidities and number of medications. At discharge, PIM (including senna) was significantly associated with age (OR 1.1; 95%CI 1.02–1.3; p = 0.02), female gender (OR 3.6; 95%CI 1.3–10; p = 0.01) and number of medications (OR 1.2; 95%CI 1.0–1.3; p = 0.02). When senna was excluded PIM at discharge was only significantly higher in female patients (OR 5.4; 95%CI 1.7–16.9; p = 0.004). Conclusion: The Beers criteria detected a high prevalence of PIM use in elderly hospitalised patients. Refereed/Peer-reviewed

Background: There are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined. Objective: To examine the predictive performance of four internationally validated frailty measures in an older Australian population. Methods: A retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailty phenotype (FP), simplified frailty phenotype (SFP), frailty index (FI) and prognostic frailty score (PFS). Odds ratios (OR) were calculated to measure the association between frailty and outcomes at Wave 3 including mortality, hospitalisation, nursing home admission, fall and a combination of all outcomes. Predictive performance was measured by assessing sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratio (LR). Area under the curve (AUC) of dichotomised and the multilevel or continuous model of the measures was examined. Results: Prevalence of frailty varied from 2% up to 49% between the measures. Frailty was significantly associated with an increased risk of any outcome, OR (95% confidence interval) for FP: 1.9 (1.4-2.8), SFP: 3.6 (1.5-8.8), FI: 3.4 (2.7-4.3) and PFS: 2.3 (1.8-2.8). PFS had high sensitivity across all outcomes (sensitivity: 55.2-77.1%). The PPV for any outcome was highest for SFP and FI (70.8 and 69.7%, respectively). Only FI had acceptable accuracy in predicting outcomes, AUC: 0.59-0.70. Conclusions: Being identified as frail by any of the four measures was associated with an increased risk of outcomes; however, their predictive accuracy varied. Refereed/Peer-reviewed