The prevalence of potentially inappropriate prescribing in acutely unwell older people

Transcription

1 The prevalence of potentially inappropriate prescribing in acutely unwell older people Raliat Onatade, Parul Modha, Jasmine Fernando, Emily Knight, Ewa Maryniak

2 Background Potentially Inappropriate Prescribing (PIP): The use of medicines for which the risk of adverse drug events outweighs the therapeutic benefit, when safer, equally effective alternative therapies are available Underprescribing is a form of PIP Potentially Inappropriate Medications (PIMs) are associated with higher mortality and morbidity rates and increased healthcare costs

3 The Problem International studies indicate that inappropriate prescribing is highly prevalent in older people Rates of between 35% and 77% have been found The extent of inappropriate prescribing in older people in UK hospital settings is unknown O Connor M, Gallagher P et al. Inappropriate prescribing: criteria, detection and prevention. Drugs Aging (2012); 29

4 Study Aims To determine the prevalence and types of PIMs in older people at admission to, and on discharge from, an acute UK teaching hospital To compare the prescribing of PIMs between patients in specialist older people s and nonspecialist clinical areas

5 The Tool The Screening Tool of Older Persons Potentially Inappropriate Prescriptions (STOPP) classifies common drug issues found to contribute to potentially inappropriate prescribing (PIP) to highlight PIMs in the elderly 65 rules relating to the most common and the most potentially dangerous instances of inappropriate prescribing in older people Developed in Ireland in 2008 Gallagher P, Ryan C, Byrne S et al. International Journal of Clinical Pharmacology and Therapeutics (2008); 46

6 Subjects and Setting Patients 65 years or over at discharge from a 950-bed acute teaching hospital Trust Specialist - All patients discharged from the Healthcare of the Ageing Unit (HAU) between 30 May and 31 July 2011 Non-Specialist - Randomly selected patients discharged from the acute non-hau wards between 27 June and 31 July 2011 Exclusions: Patients who died during admission or whose clinical information was not available electronically

7 Method Data collectors were trained in the use of STOPP Admission and discharge medication lists were retrospectively reviewed for the presence of STOPP drugs Analysis: PASW (SPSS) v20 was used for statistical analysis

8 Population Characteristics Patient numbers HAU = 195, Non-HAU = 336 Sex = Female HAU = 55% (108/195), Non-HAU = 44% (148/336) Median age at admission HAU = 85.5 years, Non-HAU = 76 years Median length of stay HAU = 19 days (3 239), Non-HAU = 4 days (1 78) a priori sample size calculation indicated that 400 patients would give a 95% CI of +/- 3

9 Results - Medication Total meds at admission Total meds on discharge Median no. of meds at admission Median no. of meds on discharge Patients on >10 meds on admission Patients on >10 meds at discharge HAU (n = 195) Non-HAU (n = 336) 1711* 2631** 1887* 3102 ** 9 (0 20) 10 (0 21) 7 (0 24) 9 (0-24) 34% 22% 41% 33% *, ** significant increase, p <0.005, paired samples t-test

10 Results - PIMs PIM prevalence at admission (95% CI) PIM prevalence at discharge (95% CI) Number of PIMs at admission (mean, SD) Number of PIMs on discharge (mean, SD) HAU (n = 195) Non-HAU (n = 336) 26.7% 27.1% ( ) ( ) 23% 25.3% ( ) ( ) (0.38, 0.73) * (0.27, 0.45) (0.26, 0.53) * (0.25, 0.44) *significant decrease, p <0.005, Wilcoxon Signed Ranks Test Overall PIM prevalence for total population of 531 patients was 26.9% (95% CI = 23.1% 30.7%) at admission and 24.3% (20.7% %) on discharge

11 Results PIM types At admission to HAU: Common PIMs were those associated with tricyclic antidepressants, opiates and other drugs in patients at risk of falls, inappropriate alpha-blockers and duplicate drugs On discharge, opiates and first generation antihistamines were increased Non-HAU: Most common: long-term high dose PPIs (34% and 33% of admission and discharge PIMs). Prescriptions for aspirin at doses greater than 150mg increased during admission

12 Post hoc analysis I Patients admitted to the HAU and taking more than 10 medications had more than double the likelihood of having a PIM compared to those prescribed 10 or fewer (Odds Ratio = 2.3, 95% CI = )

13 Mean no of PIMs Mean no. of PIMs Polypharmacy and PIMs Admission * 1 TO 5 6 TO TO TO 20 No of medications at admission *There is a positive relationship between the no. of PIMs and the number of medications prescribed at admission (p<0.05, one way ANOVA) 0.5 Discharge to 5 6 to to to 20 No of medications on discharge

14 Post hoc analysis II PIM index = No. of PIMs / Total number of medications Complements assessment of prevalence Allows comparison of prescribing appropriateness between studies A smaller PIM index for a population = more appropriate prescribing in that population, taking into account total amount of prescribed medication HAU patients - PIM index at admission = 0.043, discharge = Non-HAU patients - PIM index at admission = 0.046, discharge = Gallagher and Mahony, PIM index = 0.076, PIM prevalence = 35% Hamilton et al, PIM index = 0.135, PIM prevalence = 56.2% Gallagher P, O Mahony D. Age and Ageing (2008); 37 Hamilton H, Gallagher P, et al. Archives of Internal Medicine (2011);13

15 Discussion PIM prevalence was lower than published rates from outside the UK Due to differences in prescribing culture/healthcare systems/clinical pharmacy input? Admission to a specialist HAU = statistically significant reduction in potentially inappropriate medication Polypharmacy again shown to be associated with inappropriate prescribing STOPP is a suitable tool for use in everyday practice, but needs updating

16 Conclusions and Future Work This study provides a previously unknown baseline rate for PIM prevalence in acute UK hospital settings Pharmacists looking after older patients outside HAUs should be extra-vigilant about potentially inappropriate medications Similar studies are needed in other settings to confirm findings

17 Thank you for listening Thanks to Greg Scutt and Dr. Vivian Auyeung for additional statistical support