International Journal of Clinical Pharmacy

, Volume 36, Issue 3, pp 570–580

Potentially inappropriate medication related to weakness in older acute medical patients

Authors

    • Optimed, Clinical Research Centre, Hvidovre HospitalUniversity of Copenhagen
  • Ove Andersen
    • Optimed, Clinical Research Centre, Hvidovre HospitalUniversity of Copenhagen
  • Marianne Hallin
    • Optimed, Clinical Research Centre, Hvidovre HospitalUniversity of Copenhagen
  • Janne Petersen
    • Optimed, Clinical Research Centre, Hvidovre HospitalUniversity of Copenhagen
Research Article

DOI: 10.1007/s11096-014-9940-y

Cite this article as:
Jensen, L.D., Andersen, O., Hallin, M. et al. Int J Clin Pharm (2014) 36: 570. doi:10.1007/s11096-014-9940-y
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Abstract

Background The use of potentially inappropriate medications (PIMs) is common in the older population. Inappropriate medications as well as polypharmacy expose older people to a greater risk of adverse drug reactions and may result in hospitalizations. Objective To evaluate the prevalence of PIMs among acutely hospitalized patients aged ≥65 years in an acute medical unit, and to investigate the relationship between use of PIMs and weakness. Setting This longitudinal observational study was undertaken in the Acute Medical Unit, Hvidovre Hospital, University of Copenhagen, Denmark. Method Patients aged ≥65 years admitted to the acute medical unit during the period October to December 2011 were included. Patients were interviewed at admission and at a follow-up visit 30 days after discharge. Data included information about medications, social status, functional status, cognitive status, handgrip strength, health-related quality of life, visual acuity, days of hospitalization, and comorbidities, and was prospectively collected. Polypharmacy was defined as regular use of 5 or more drugs. The Charlson Comorbidity Index was used to categorize comorbidities. Main outcome measure The prevalence of PIMs and the association with PIMs and functional status handgrip strength, HRQOL, comorbidities, social demographic data and vision. Results Seventy-one patients (55 % men) with a median age of 78.7 years participated. The median number of medications was eight per person. Eighty percent were exposed to polypharmacy. PIMs were used by 85 % of patients, and PIMs were associated with low functional status (p = 0.032), low handgrip strength (p = 0.006), and reduced health-related quality of life (p = 0.005), but not comorbidities (p = 0.63), age (p = 0.60), sex (p = 0.53), education (p = 0.94), cognition (p = 0.10), pain (p = 0.46), or visual acuity (p = 0.55). Conclusions Use of PIMs was very common among older people admitted to an acute medical unit. The use of PIMs is associated with low functional status, low handgrip strength, and reduced health-related quality of life.

Keywords

ComorbidityDenmarkFunctionalHand strengthOlder adultsPotential inappropriate medications (PIMs)PolypharmacyQuality of life

Impact of these findings on practice

  • Eighty-five percent of acute medical patients are exposed to potentially inappropriate medications (PIMs).

  • The use of PIM is associated with low functional status, low handgrip strength, and reduced health-related quality of life.

  • The use of PIM is not associated with morbidity as assessed by Charlson Comorbidity Index.

  • A large scale study is needed to validate the association of PIMs with weakness in older patients.

Introduction

The average life expectancy is increasing worldwide, as is the prevalence of chronic diseases and comorbidities [1, 2]. The aging process is associated with physiological changes, including decreased hepatic clearance, reduced glomerular filtration capacity, and loss of total muscular mass [3, 4]. These changes can impact pharmacodynamics and pharmacokinetics, which can increase the risk of adverse drug reactions (ADRs) [3, 4].

Potentially inappropriate medications (PIMs) are defined as drugs for which the risks of use outweigh the benefits [5, 6]. Prescriptions for PIMs can potentially expose older people to a greater risk of ADRs [7, 8]. The most widely used method for defining PIMs is the Beers List [911]. Nationally modified indicators have been developed in many countries because of different drug therapy traditions, national guidelines, and market conditions. The Institute for Rational Pharmacotherapy in Denmark [12] has developed a list of PIMs called the Red–Yellow–Green List based on a review of the medications of 59 elderly nursing home residents who used antipsychotics, Beers Criteria [5, 8, 13], indicators from the Swedish National Board of Health and Welfare [14], STOPP Criteria [15], and indicators from the Norwegian General Practice List [16]. The Red–Yellow–Green List is divided into three categories: red indicates drugs that should not be used in older people, yellow indicates drugs that should be followed-up, and green denotes a lack of evidence for long-term use or adverse drug events [12, 17] (Table 1).
Table 1

Red-Yellow-Green List

Drug name

ATC code

Digestive system

Metoclopramide (yellow)

A03FA01

Bisacodyl (red)

A06AB02

Sodium picosulfate (red)

A06AB08

Hyoscine butylbromide (red)

A03BB01

Propantheline bromide (red)

A03AB05

Anticoagulants

Acetylsalicylic acid and dipyridamole (red)

B01AC30

Acetylsalicylic acid 150 mg (red)

B01AC06

Acetylsalicylic acid 75 mg (yellow)

B01AC06

Clopidogrel plus acetylsalicylic acid (green)

B01AC04 + B01AC06

Cardiovascular

Furosemide (yellow)

C03CA01

Digoxin (red)

C01AA05

Urological drugs

Tolterodine (red)

G04BD07

Fesoterodine (red)

G04BD11

Solifenacine (red)

G04BD08

Analgesics

3-dimethylamino-1,1-diphenylbutene-(1)hydrochloride and ketobemidone chloride (yellow)

N02AG02

Oxycondone/Morphine (yellow)

N02AA05/N02AA01

NSAIDs (red)

M01A

Quinine (red)

P01BC01

Sleep/anxiety

Promethazine (yellow)

R06AD02

Nitrazepam (red)

N05CD02

Zopiclone (yellow)

N05CF01

Zolpidem (yellow)

N05CF02

Diazepam (red)

N05BA01

Oxazepam (yellow)

N05BA04

Antipsychotics

Aripiprazole(red)

N05AX12

Zuclopenthixol (red)

N05AF05

Flupentixol (red)

N05AF01

Levomepromazine (red)

N05AA02

Risperidone (yellow)

N05AX08

Haloperidol (red)

N05AD01

Quetiapine (red)

N05AH04

Chlorprothixene (red)

N05AF03

Olanzapine (red)

N05AH03

Biperidene (red)

N04AA02

Orphenadrine (red)

N04AB02

Antidepressants

Amitriptyline (red)

N06AA09

Clomipramine (red)

N06AA04

Imipramine (red)

N06AA02

Nortriptyline (red)

N06AA10

Dementia drugs

Donepezil (green)

N06DA02

Rivastigmine (green)

N06DA03

Galantamine (green)

N06DA04

Memantine (green)

N06DX01

Red drugs that should not be used in older people

Yellow drugs that should be followed up

Green denotes a lack of evidence for long-term use or adverse drug events

Studies have demonstrated that PIMs are often used among older people in nursing homes. The prevalence of PIMs varies, but Lau et al. [18] reported that 50 % of residents in nursing homes use at least one PIM. In primary care in European countries, PIM rates range from 5.8 to 38.5 % [1925], but the prevalence of PIMs in older patients admitted to the hospital range from 20 to 77 % depending on the criteria [6, 2628]. To the best of our knowledge, only a few studies have examined the potential influence of the use of PIMs on functional status, handgrip strength, and health-related quality of life in older people [27, 2931].

The aim of the present study was to evaluate the prevalence of PIMs among hospitalized patients aged ≥65 years in an acute medical unit and to investigate the relationship between PIM use and weakness as measured by cognitive status, visual acuity, functional status, handgrip strength, and health-related quality of life. A secondary aim was to investigate the association between polypharmacy and quality of life.

Ethical approval

Approval from the Scientific Ethical Committee was not needed according to Danish law. The registration of patient information was reported to the Danish Data Protection Agency.

Methods

Selection of participants

This study was conducted as a longitudinal study of patients admitted to the acute medical unit at Hvidovre Hospital at the University of Copenhagen from October to December 2011. On weekdays, the patients were randomly selected from a list of all acute admissions to the acute medical unit within the past 24 h. Patients aged ≥65 years were eligible for inclusion. The exclusion criteria were deafness, blindness, unconsciousness, confusion, terminal illness, cancer, or an inability to speak or understand Danish.

Data collection

All patients were informed orally and in writing about the study before deciding whether or not to participate. A structured interview was conducted by a pharmacist (LDJ) or nurse (MH). The patients were interviewed in a hospital room within 24 h of providing consent. The same pharmacist or nurse examined the patients in their homes 30 days after discharge. During the baseline interview conducted at the hospital, background data were collected along with information on cognitive status, vision, drug administration, assistance with drug handling, home care, pain, functional independence, health-related quality of life, fatigue, and ADRs in a yes/no question (including format), a physical test for handgrip strength, as well as the tests described below. Glomerular filtration rate [175× (creatinine in μmol/l/88.4)−1.154 × (age in years)−0.203 × (0.742 if female)] [32] and plasma creatinine and β-haemoglobin concentrations were analysed routinely upon admission to Hvidovre Hospital.

Cognitive status was assessed with the Mini-Mental State Examination (MMSE) at admission. Patients with an MMSE score <24 were considered to be cognitively impaired [33, 34]. The visual acuity test was used to determine the smallest letter a patient could read on a standardized card held 30 cm away. If the patients generally used reading glasses or regular glasses they wore them for the test. Visual impairment was defined as a visual acuity of less than 6/20 based on the World Health Organization’s classification [35, 36].

Functional independence was measured using the New Mobility Score to determine the patients’ indoor and outdoor mobility. The patients were scored from 0 (no walking ability at all) to 9 (fully independent). Because we were interested in identifying patients who were functionally dependent, we dictomized the New Mobility Score according to Parker et al. [37] and a score <5 was indicative of functional dependence.

Handgrip strength was measured using a handheld dynamometer and measured in the dominant hand three times. The maximum of the three handgrip strength trials was used. A handgrip strength <30 kg for men and <20 kg for women was considered indicative of a high risk of sarcopenia [38].

Health-related quality of life was measured using the Euroqol-5d (EQ-5d). The EQ-5d is based on five items (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression), each with three response levels (no problem, some problems, extreme/severe problems) [39]. Comorbidity was defined as two or more diseases in one patient. The Charlson Comorbidity Index is a method for classifying morbidity, which is associated with a higher risk of death caused by comorbid disease [40]. The Charlson Comorbidity Index consists of 17 indicators, each representing a disease group. To reach a sufficient number of patients in each category, all Charlson Comorbidity Index scores >2 were categorized as being equal to 2.

Medication

At the follow-up visit, patients were asked to show their medication containers for review. Medications prescribed on a regular basis, pro re nata drugs, and over-the-counter drugs, except natural medicines, food supplements, and vitamins, were registered. Each patient’s general practitioner (GP) was contacted in writing at the time of follow-up and asked to provide a list of currently prescribed medications for the patient. All redeemed prescriptions were recorded in the Personal Electronic Medication Profile, which was used together with the GP’s medication list and the follow-up visit to determine as comprehensive a medication status as possible [41, 42]. All information was included, even medications that were only reported or found in one information source. The anatomical therapeutic chemical classification system (ATC) was used to identify medications [43]. The ATC codes are divided into five levels, but only levels 1, 2, and 3 were used when studying drug classes in this study.

PIMs were identified by the Red–Yellow–Green List [12]. For each patient we calculated indicators to determine whether the patient received any PIMs, any red PIMs, any yellow PIMs, or any green PIMs and the total number of PIMs the patients received. Polypharmacy was defined as the use of five or more drugs; these drugs could be from the Red–Yellow–Green List [4446]. Dispensed drugs were medications refilled into, for example, a dosage box or small beaker.

Statistical analysis

The characteristics of the cohort and the use of drugs were reported using descriptive statistics; we used number and percentages for discrete variable and medians with minimum and maximum for continuous variables. To study the potential influence of covariates on PIMs, we preformed three logistic regression models for each of the possible covariates, with PIMs, red PIMs, and polypharmacy as response variables. The results are presented as odds ratios (ORs) with a corresponding 95 % confidence interval and P value. To study the effect of PIMs, red PIMs, and polypharmacy on the New Mobility Score, we fitted one linear regression analysis for each of the indicators of PIMs, red PIMs, and polypharmacy. First, we fitted unadjusted analyses, then adjusted for age and sex, followed by adjustments for comorbidities using the Charlson Comorbidity Index. Similar analyses were performed for handgrip strength, health-related quality of life, and fatigue. However, for fatigue, instead of using a linear regression analysis, we used a logistic regression analysis. In the case of a significant association, we performed analyses for each of the PIMs on the list to determine whether any specific drug caused the significant association.

The level of significance was set at 0.05. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC).

Results

Attendance and participants

During the inclusion period, 492 patients fulfilled the inclusion criteria. A random sample of 95 patients was invited to participate. Sixteen patients did not want to participate, and the remaining 79 patients (16 % of the patients that fulfilled the inclusion criteria) provided informed consent. Of these patients, one died and seven were discharged before the interview was performed. Therefore, 71 patients were interviewed during hospitalization (Fig. 1). Sixty-two (87 %) of the patients participated in the follow-up visit, as four patients died before the follow-up visit and five declined to participate. All but one GP forwarded medication lists for the patients involved in the study (i.e., 70 patients´ medication lists were received).
https://static-content.springer.com/image/art%3A10.1007%2Fs11096-014-9940-y/MediaObjects/11096_2014_9940_Fig1_HTML.gif
Fig. 1

Flowchart of inclusion and follow-up

The characteristics of the 71 patients are provided in Table 2. Nearly 30 % of the patients were functionally dependent, and 50 % of the men and 81 % of the women had a risk of sarcopenia. Comorbidity was present in 66 % of the patients. Thirty-eight percent of the patients were readmitted, and 8 % of the patients died within 90 days of discharge.
Table 2

Background information and characteristics of the 71 older acutely admitted patients

Characteristic

Value

Sociodemographic characteristics

Median age, years (min; max)

78.7 (65.3; 100.9)

Sex, male, n (%)

39 (54.9)

Education, n (%)

7 years or less of schooling

21 (30.0)

8–11 years of schooling

11 (15.5)

Skilled

34 (47.9)

College education

4 (5.6)

Marital status, n (%)

Married

29 (40.8)

Widowed

30 (42.3)

Divorced

10 (14.1)

Single

2 (2.8)

Living alone, n (%)

40 (56.3)

Residential, n (%)

Living at home

66 (93.0)

Living in nursing homes

5 (7.0)

Home care, n (%)

Cleaning

32 (45.1)

Help with drug handling

23 (32.4)

Functional characteristics

Physical function, n (%)

New mobility score ≤5a

21 (29.6)

Cognitively impaired, n (%)

MMSE ≤24b

23 (32.4)

Handgripc, n (%) (N = 64)

Men <30 kg

19 (50.0)

Women <20 kg

21 (80.8)

Visual acuity, n (%) (N = 66)

Visually impaired

20 (30.3)

Disease characteristics

Median admission duration, days (min; max)

4.0 (1; 55)

Diagnoses per patient, median (min; max)

2.0 (1; 9)

Patients with comorbidities, n (%)

47 (66.2)

Charlson Comorbidity Index, n (%)

0

37 (52.1)

1

25 (35.2)

2+

9 (12.7)

Blood work, n (%)

Low glomerular filtration rated

30 (42.3)

High creatininee

20 (28.2)

High alanine aminotransferasef

2 (2.8)

Low β-haemoglobing

31 (43.7)

High β-haemoglobinh

1 (1.4)

MMSE Mini-Mental State Examination

aScore from 0 (no walking ability at all) to 9 (fully independent). A New Mobility Score ≤5 is considered functional dependency

bCognitive status. A score ≤24 is considered cognitively impaired

c64 patients were included in the analysis. A handgrip <30 kg for men and handgrip <20 kg for women was considered sarcopenia

dGlomerular filtration rate <60 ml/min was considered low

eCreatinine >90 µmol/l for women and Creatinine >105 µmol/for men were considered high

fAlanine aminotransferase >45 U/l for women and Alanine aminotransferase >70 U/l for men was considered high

gβ-haemoglobin <7.3 mmol/l for women and β-haemoglobin <8.3 mmol/l for men was considered low

hβ-haemoglobin >9.5 mmol/l for women and β-haemoglobin <10.5 mmol/l for men was considered high

Potentially inappropriate medication use

In total, the 71 patients received 608 drugs (Table 3). Seventy-nine percent of the drugs were present in at least two sources (Personal Electronic Medication Profile, GP’s medication list, and/or the medication information from the follow-up visit). Of the 128 (21 %) drugs found in one source only, 26 (20 %) were for patients who only had two possible sources because they did not have follow-up visits, and 78 (60 %) drugs were found during visits to the patients’ homes.
Table 3

Use of medications and the classification of PIMs for 71 patients

Medications

Value

Drug per patient, median (min; max)

8.0 (0; 19)

Polypharmacy, n (%)

57 (80.3)

PIMs, n (%)

60 (84.5)

Dispensed drugsa, n (%)

44 (63.8)

Red PIMs, n (%)

27 (38.0)

Yellow PIMs, n (%)

51 (71.8)

Green PIMs, n (%)

0 (0.0)

PIM potentially inappropriate medication

a69 patients were included in the analysis

Only 15 % of the participants did not receive any PIMs; of these, one patient did not take any drugs at all. Forty-one percent of the patients were exposed to only one PIM, 13 % of the patients to two PIMs, and 31 % of the patients to three or more PIMs.

The patients’ medications are presented by drug class in Fig. 2. The most common drug classes were antithrombotic agents (74 % of patients), lipid modifying agents (54 %), and diuretics (51 %). The most common drug among the patients was acetylsalicylic acid (62 %).
https://static-content.springer.com/image/art%3A10.1007%2Fs11096-014-9940-y/MediaObjects/11096_2014_9940_Fig2_HTML.gif
Fig. 2

Number of patients receiving drugs from different ATC code classes [43]. Only the ATC classes containing 10 or more patients were included

Table 4 shows the association between potential confounders for PIMs and polypharmacy. PIMs, red PIMs, and polypharmacy had no significant associations with age, sex, sociodemographic characteristics, cognitive impairment, visual acuity, pain, or the Charlson Comorbidity Index.
Table 4

Associations between possible confounders of PIMs and polypharmacy, and PIMs, red PIMs, and polypharmacy

 

N

PIMs (yes vs. no)

p value

Red PIMs (yes vs. no)

p value

Polypharmacy >5 drugs versus ≤5 drugs

p value

 

OR (95 % C I)

 

OR (95 % C I)

 

OR (95 % C I)

 

Age

≥80 versus <80 years

71

1.43 (0.38; 5.41)

0.60

1.34 (0.51; 3.52)

0.55

3.54 (0.89; 14.05)

0.07

Sex

Male versus female

71

0.65 (0.17; 2.47)

0.53

0.39 (0.15; 1.05)

0.06

0.89 (0.28; 2.91)

0.85

Education

10 years of school versus skilled or more

71

1.05 (0.29; 3.82)

0.94

1.11 (0.43; 2.91)

0.83

0.84 (0.26; 2.70)

0.77

Living alone

Yes versus no

71

1.68 (0.46; 6.12)

0.43

2.00 (0.74; 5.41)

0.17

1.38 (0.43; 4.44)

0.59

Residential

Nursing home versus home living

71

a

 

a

 

0.38 (0.06; 2.44)

0.31

Assistance with cleaning

Yes versus no

71

2.50 (0.60; 10.32)

0.21

4.29 (1.54; 11.90)

0.01

1.62 (0.48; 5.44)

0.43

Assistance with drug handling

Yes by others versus no (doing it yourself)

71

5.79 (0.69; 48.31)

0.10

3.16 (1.13; 8.87)

0.03

a

 

Dispensed drugs

Yes versus no

69

4.67 (1.05; 20.74)

0.04

5.23 (1.54; 17.75)

0.01

8.40 (2.00; 35.22)

0.004

Cognitively impaired

Yes versus no

71

5.79 (0.69; 48.31)

0.10

2.40 (0.87; 6.66)

0.09

0.83 (0.24; 2.84)

0.77

Visual acuity

≥0.3 versus <0.3

66

1.19 (0.28; 5.06)

0.81

1.38 (0.47; 4.08)

0.56

0.84 (0.22; 3.20)

0.51

Pain

Yes versus no

70

1.68 (0.43; 6.54)

0.46

1.96 (0.61; 6.27)

0.26

2.48 (0.73; 8.46)

0.15

Charlson Comorbidity Index

  

0.63

 

0.73

 

0.05

1 versus 0

71

2.02 (0.48; 8.52)

 

0.69 (0.24; 2.01)

 

11.52 (1.39; 95.51)

 

2 versus 0

71

a

 

1.17 (0.27; 5.10)

 

3.84 (0.43; 34.31)

 

Results are expressed as odds ratios from a logistic regression model

aNot included in the model, as parameter estimation could not be identified

Table 5 presents the potential outcomes of PIMs and polypharmacy with the New Mobility Score, handgrip strength, and health-related quality of life. Low New Mobility Scores were significantly associated with PIMs, red PIMs, and polypharmacy. By studying each of the PIMs, we found that sodium picosulphate (p = 0.002), furosemide (p = 0.004), and nitrazepam (p = 0.04) were associated with low New Mobility Scores. Low handgrip strength was significantly associated with PIMs and red PIMs. By studying each of the PIMs, we found that furosemide (p = 0.01) was associated with low handgrip strength. Reduced health-related quality of life was significantly associated with red PIMs, and the drugs associated with reduced health-related quality of life were metoclopramide (p = 0.002), sodium picosulphate (p = 0.0001), furosemide (p = 0.02), and quinine (p = 0.05). The ADR fatigue was significantly associated with PIMs. Fatigue was the only ADR included in the analysis because very few patients reported other ADRs. After adjusting the models for age and sex, we found no larger changes, similar when adjusting for the Charlson Comorbidity Index.
Table 5

The association of potentially inappropriate medications (PIMs), red PIMs, and polypharmacy with New Mobility Score, handgrip, health-related quality of life, and fatigue

 

New mobility scorea (n = 71)

p value

Handgrip strengtha (n = 64)

p value

Health-related quality of lifea (n = 71)

p value

Fatigueb (n = 71)

p value

 

β (95 % CI)

β (95 % CI)

β (95 % CI)

OR (95 % CI)

PIMs

Unadjusted

−1.80 (−3.42; −0.17)

0.030

−9.82 (−16.72; −2.92)

0.006

−0.12 (−0.30; 0.06)

0.188

0.17 (0.04; 0.73)

0.017

Adjusted 1c

−1.50 (−2.93; −0.07)

0.040

−8.69 (−13.57; −3.81)

0.001

−0.10 (−0.28; 0.07)

0.249

0.18 (0.04; 0.75)

0.018

Adjusted 2d

−1.59 (−3.47; 0.77)

0.061

−9.35 (−16.47; −2.23)

0.011

−0.09 (−0.27; 0.10)

0.351

0.18 (0.04; 0.80)

0.023

Red PIMs

Unadjusted

−2.32 (−3.44; −1.20)

<0.001

−8.16 (−13.67; −2.64)

0.006

−0.19 (−0.32; −0.06)

0.005

0.55 (0.20; 1.53)

0.256

Adjusted 1c

−1.92 (−2.94; −0.89)

0.004

−5.77 (−9.87; −1.67)

0.005

−0.17 (−0.30; −0.04)

0.013

0.58 (0.20; 1.64)

0.301

Adjusted 2d

−2.34 (−3.46; −1.23)

<0.001

−8.48 (−14.03; −2.94)

0.003

−0.19 (−0.31; −0.06)

0.004

0.51 (0.18; 1.46)

0.209

Polypharmacy

Unadjusted

−1.49 (−2.97; −0.01)

0.049

−5.80 (−12.52; 0.92)

0.090

−0.14 (−0.31; 0.02)

0.082

0.38 (0.11; 1.24)

0.107

Adjusted 1c

−1.06 (−2.40; 0.29)

0.123

−4.13 (−9.16; 0.90)

0.105

−0.13 (−0.30; 0.03)

0.103

0.37 (0.11; 1.27)

0.113

Adjusted 2d

−1.34(−2.92; 0.24)

0.096

−5.20 (−12.57; 2.17)

0.164

−0.13 (−0.30; 0.04)

0.126

0.46 (0.13; 1.64)

0.232

aLinear regression, b logistic regression, c adjusted for sex and age, d adjusted for Charlson Comorbidity Index

Discussion

In the present study, 85 % of the 71 acute medical patients were exposed to PIMs, and polypharmacy was present in 80 % of the patients. We have not found any other study using the Red–Yellow–Green List in an acute medical unit. However, a prevalence study from Europe using the Beers criteria reported prevalence ranging from 23 % in Prague to 43 % in Geneva, and from 35 % to 77 % using the STOPP criteria [47, 48]. This lower prevalence of PIMs is consistent with a low median number (6 vs. 8) of drugs prescribed per patient as indicated in the study by Gallagher et al. [26]. Furthermore, the high prevalence of PIMs defined by the Red–Yellow–Green List could be explained by the fact that both the STOP criteria and the Beers list has dose restrictions, whereas the Red–Yellow–Green List only involves indicators of patients receiving the drug independently of dose. For example, acetylsalicylic acid has a prevalence of 62 % in our study, but this drug indicator was lower than 14 % for both the Beers list and the STOPP criteria, probably because of dose restrictions [47]. The prevalence of polypharmacy was consistent with other studies. Yong et al. [44] found that polypharmacy was represented in 81 % of the admitted medical patients in their study. However, Viktil et al. [49] found that polypharmacy was represented in 47 % of the admitted medical patients in their study, but this could be explained by a lower median age of 75 years.

We found no other studies on the association between low function and PIMs using the Red–Yellow–Green List in the literature. However, PIMs defined by the Beers criteria is associated with handgrip strength [29]. Gnjidic et al. [50] found a borderline significant association between the Beers criteria and handgrip strength, and this weak association disappeared when they adjusted for age, sex, education, comorbidities, cognitive function, depression, and sleep disturbance. However, we think that three of the variables adjusted for cognitive function, depression, and sleep disturbance could be seen as mediators. Low handgrip strength has also been associated with a higher probability of premature death in the elderly [51]. Poor functional capacity may be due to underlying disease and aging. However, we found that adjusting for the Charlson Comorbidity Index and age did not change the association substantially.

New Mobility Score showed a correlation with three specific PIMs, sodium picosulphate, furosemide, and nitrazepam, whereas handgrip strength only showed a correlation with furosemide. This observation is consistent with the increased use of furosemide in cases of heart failure, which is one of the prominent diseases associated with limited life expectancy [52]. Another explanation for the link between these drugs and New Mobility Score may be dizziness, which is a registered ADR of nitrazepam, as are muscle weakness and coordination and balance disorders. Benzodiazepine has been associated with impaired function in older people [53]. Two registered ADRs of furosemide are hypomagnesaemia and hypokalaemia, which can also cause symptoms like muscle weakness. In addition, laxatives such as sodium picosulphate can cause dehydration and may cause muscle weakness. Furthermore, laxatives are used to treat constipation caused by opioids.

Reduced health-related quality of life was significantly associated with four red PIMs: metoclopramide, sodium picosulphate, furosemide, and quinine. Depression was a registered ADR of metoclopramide. Other factors could explain the association between reduced health-related quality of life and red PIMs, such as loss of functional capacity, age-related disability, illness, and the fact that one needs help and cannot perform daily activities. We also found that adjusting for age and sex made the association non-significant. Reduced health-related quality of life can also have a negative influence on the economic burden of the entire health care system, resulting in a greater need for care [54, 55]. Thus, PIMs can be costly for the health care system [56].

Furosemide was the only drug significantly associated with low New Mobility Score, low handgrip strength, and reduced health-related quality of life. Thirty-four percent of the patients received furosemide. Whether the effects were due to drugs or caused by the underlying condition is debatable.

The study had limited power due to the small sample size, which may cause a lack of precision in the estimates of the studied associations. Moreover, we had 17 drop-outs after consent, but only nine drop-outs at the follow-up visit, four due to death. Furthermore, we performed a post hoc analysis of each of the PIMs to evaluate the associations, despite a risk of multiple testing. Natural medicines, food supplements, and vitamins were not included in the study as these are not present in the Red–Yellow–Green List and not registered in the Personal Electronic Medication Profile. This approach could result in an underestimation of polypharmacy. We only looked at pre-defined inappropriate medications in this study, but other drugs not appearing on the list of inappropriate drugs may also have a negative impact on functional ability, reduce health-related quality of life, and cause ADRs. Previous studies have found that specific drug classes are associated with worse functional status [53].

Being able to assemble a complete medication list from multiple sources of medication information was a strength of this study. Despite access to more medical information, whether patients actually take their medication is debatable [41, 42, 57, 58]. Spouses can use each other’s drugs, which can bias drug use estimation. We accumulated different drugs from three sources, which could lead to an overestimation of drug use, but 79 % of the drugs were present in at least two sources. Of the drugs only registered in one source, 20 % were from patients without a follow-up visit and 60 % were found at the follow-up visit, which we expect to be the most valid source. Information regarding over-the-counter drugs may have been lost for the patients who did not have a follow-up visit due to the use of only two sources [58], as the Personal Electronic Medication Profile does not register over-the-counter drugs. We were only able to collect a comprehensive and validated medication list at follow-up, though we used handgrip strength from admission. This is a limitation because the patients could have been prescribed new medicine during the time period following admission. Handgrip strength was previously shown to be stable 30 days after discharge in acutely admitted older medical patients [59].

Further work should focus on the influence of medications, especially PIMs, on functional level and physical exercise. By including more variables, such as physical activity and quality of life, treatment can be optimized, improving patient safety in this population. Moreover, this study indicated that an acute medical unit is a good setting in which to screen for PIMs. Notably, the original Beers List and the STOPP criteria on which the Red–Yellow–Green List is built were developed using the Delphi Method [5, 15]. The Delphi Method is a consensus method with relatively low evidence. As with the updated Beers list [60], future studies should focus on developing a new PIMs list with a higher level of evidence, and on long-term drug use and predictors of the re-admission of older people.

Conclusion

The results of the present study show that the use of inappropriate drugs is common among acutely admitted older people. The use of PIMs is associated with low functional independence, low handgrip strength, and reduced health-related quality of life, but it is not associated with morbidity as assessed by the Charlson Comorbidity Index. The results also indicate that PIMs are more strongly associated with outcomes, such as the New Mobility Score, handgrip strength, and health-related quality of life, than polypharmacy.

Funding

None.

Conflicts of interest

None.

Copyright information

© Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie 2014