Analysis of anticholinergic and sedative medicine effects on physical function, cognitive function, appetite and frailty: a cross-sectional study in Australia.

OBJECTIVE: To test the association between use of medicines with anticholinergic or sedative properties and physical function, cognitive function, appetite and frailty. DESIGN, SETTING AND PARTICIPANTS: This cross-sectional study analysed baseline data collected as part of the Australian Longitudinal Study of Ageing, a population-based cohort of 2087 participants aged 65 years or over living in South Australia. MAIN OUTCOME MEASURES: Physical function was measured at baseline using measures including hand grip strength, walking speed, chair stands, activities of daily living and instrumental activities of daily living (IADL). Cognitive function was measured using Mini-Mental State Examination. Appetite was measured using Center for Epidemiologic Studies Depression question 2. Frailty was measured using frailty index. The association between use of anticholinergics or sedatives and physical or cognitive function, appetite, or frailty was assessed using analysis of covariance and ordinal or binary logistic regression. RESULTS: Almost half of the population were using anticholinergics or sedatives (n=954, 45.7%). Use of anticholinergics was significantly associated with poorer grip strength, slower walking speed, poorer IADL and poorer appetite. Use of sedatives was significantly associated with poorer grip strength, slower walking speed and poorer IADL. We found no significant association between medicine use and cognitive function. Users of anticholinergics or sedatives were significantly more likely to be frail compared with non-users. CONCLUSION: Use of medicines with anticholinergic or sedative properties is significantly associated with poorer physical function, poorer appetite and increased frailty. Early identification of signs and symptoms of deterioration associated with medicine use is particularly important in older people so that worsening frailty and subsequent adverse events are prevented.

Predictive performance of four frailty measures in an older Australian population.

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.