Download The prevalence of potentially inappropriate prescribing in

The prevalence of
potentially
inappropriate
prescribing in
acutely unwell older
people
Raliat Onatade, Parul Modha, Jasmine
Fernando, Emily Knight, Ewa Maryniak
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
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
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
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
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
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
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
Results - Medication
HAU
(n = 195)
Non-HAU
(n = 336)
Total meds at admission
1711*
2631**
Total meds on discharge
1887*
3102 **
9
(0 – 20)
7
(0 – 24)
10
(0 – 21)
9
(0 - 24)
34%
22%
41%
33%
Median no. of meds at
admission
Median no. of meds on
discharge
Patients on >10 meds on
admission
Patients on >10 meds at
discharge
*, ** significant increase, p <0.005, paired samples t-test
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%
(20.5 – 32.9)
23%
(16.7 – 28.5)
74
(0.38, 0.73)*
51
(0.26, 0.53)*
27.1%
(23.4 - 30.8)
25.3%
(21.7 - 28.9)
120
(0.27, 0.45)
107
(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% - 28.0%) on discharge
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
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 = 1.2-4.4)
Mean no. of PIMs
Polypharmacy and PIMs
Admission
*
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1 TO 5
6 TO 10
11 TO 15
16 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)
Mean no of PIMs
Discharge
0.5
0.4
0.3
0.2
0.1
0
1 to 5
6 to 10
11 to 15
No of medications on discharge
16 to 20
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 = 0.027
• Non-HAU patients - PIM index at admission = 0.046, discharge =
0.034
• Gallagher and Mahony, 2008 - PIM index = 0.076, PIM prevalence =
35%
• Hamilton et al , 2011 - 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
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
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
Thank you for listening
[email protected]
Thanks to Greg Scutt and Dr. Vivian Auyeung for
additional statistical support