Polypharmacy, length of hospital stay, and in-hospital mortality among elderly patients in internal medicine wards. The REPOSI study

Abstract

We evaluated the prevalence and factors associated with polypharmacy and investigated the role of polypharmacy as a predictor of length of hospital stay and in-hospital mortality.
Thirty-eight internal medicine wards in Italy participated in the Registro Politerapie SIMI (REPOSI) study during 2008. One thousand three hundred and thirty-two in-patients aged ≥65 years were enrolled. Polypharmacy was defined as the concomitant use of five or more medications. Linear regression analyses were used to evaluate predictors of length of hospital stay and logistic regression models for predictors of in-hospital mortality. Age, sex, Charlson comorbidity index, polypharmacy, and number of in-hospital clinical adverse events (AEs) were used as possible confounders.
The prevalence of polypharmacy was 51.9% at hospital admission and 67.0% at discharge. Age, number of drugs at admission, hypertension, ischemic heart disease, heart failure, and chronic obstructive pulmonary disease were independently associated with polypharmacy at discharge. In multivariate analysis, the occurrence of at least one AE while in hospital was the only predictor of prolonged hospitalization (each new AE prolonged hospital stay by 3.57 days, p < 0.0001). Age [odds ratio (OR) 1.04; 95% confidence interval (CI) 1.01-1.08; p = 0.02), comorbidities (OR 1.18; 95% CI 1.12-1.24; p < 0.0001), and AEs (OR 6.80; 95% CI 3.58-12.9; p < 0.0001) were significantly associated with in-hospital mortality.
Although most elderly in-patients receive polypharmacy, in this study, it was not associated with any hospital outcome. However, AEs were strongly correlated with a longer hospital stay and higher mortality risk.

Full text

PHARMACOEPIDEMIOLOGY AND PRESCRIPTION
Polypharmacy, length of hospital stay, and in-hospital
mortality among elderly patients in internal medicine wards.
The REPOSI study
Alessandro Nobili &Giuseppe Licata &
Francesco Salerno &Luca Pasina &Mauro Tettamanti &
Carlotta Franchi &Luigi De Vittorio &
Alessandra Marengoni &Salvatore Corrao &
Alfonso Iorio &Maura Marcucci &
Pier Mannuccio Mannucci &
On behalf of SIMI Investigators
Received: 1 September 2010 / Accepted: 12 December 2010 /Published online: 11 January 2011
#Springer-Verlag 2011
Abstract
Purposes We evaluated the prevalence and factors associ-
ated with polypharmacy and investigated the role of
polypharmacy as a predictor of length of hospital stay and
in-hospital mortality.
Methods Thirty-eight internal medicine wards in Italy partic-
ipated in the Registro Politerapie SIMI (REPOSI) study during
2008. One thousand three hundred and thirty-two in-patients
aged ≥65 years were enrolled. Polypharmacy was defined as
the concomitant use of five or more medications. Linear
regression analyses were used to evaluate predictors of length
of hospital stay and logistic regression models for predictors
of in-hospital mortality. Age, sex, Charlson comorbidity
index, polypharmacy, and number of in-hospital clinical
adverse events (AEs) were used as possible confounders.
Results The prevalence of polypharmacy was 51.9% at hospital
admission and 67.0% at discharge. Age, number of drugs at
admission, hypertension, ischemic heart disease, heart failure,
and chronic obstructive pulmonary disease were independently
associated with polypharmacy at discharge. In multivariate
analysis, the occurrence of at least one AE while in hospital was
the only predictor of prolonged hospitalization (each new AE
prolonged hospital stay by 3.57 days, p<0.0001). Age [odds
ratio (OR) 1.04; 95% confidence interval (CI) 1.01–1.08;
SIMI, Italian Society of Internal Medicine. Participating hospitals and
coauthors are listed in the Acknowledgements.
A. Nobili (*):L. Pasina :C. Franchi :L. De Vittorio
Laboratory for Quality Assessment of Geriatric Therapies and
Services, “Mario Negri”Institute for Pharmacological Research,
via Giuseppe La Masa, 19,
20156 Milan, Italy
e-mail: nobili@marionegri.it
M. Tettamanti
Laboratory of Geriatric Neuropsychiatry,
“Mario Negri”Institute for Pharmacological Research,
via Giuseppe La Masa, 19,
20156 Milan, Italy
G. Licata :S. Corrao
Dipartimento Biomedico di Medicina Interna e Specialistica,
University of Palermo,
Palermo, Italy
F. Salerno
Medicina Interna, IRCCS Policlinico San Donato,
University of Milano,
Milan, Italy
A. Marengoni
Department of Medical and Surgery Sciences,
University of Brescia Geriatric Ward, Spedali Civili,
Brescia, Italy
A. Iorio :M. Marcucci
Department of Internal Medicine, University of Perugia,
Perugia, Italy
P. M. Mannucci
Scientific Direction, IRCCS Cà Granda Foundation Maggiore
Policlinico Hospital,
Milan, Italy
Eur J Clin Pharmacol (2011) 67:507–519
DOI 10.1007/s00228-010-0977-0

Conclusions Although most elderly in-patients receive poly-
pharmacy, in this study, it was not associated with any hospital
outcome. However, AEs were strongly correlated with a
longer hospital stay and higher mortality risk.
Keywords Elderly .Polypharmacy .Hospital stay .
In-hospital mortality
Introduction
Polypharmacy is a problem for rational drug prescribing in
elderly patients [1–4], the largest consumers of medicines often
taking from two to five drugs on a regular basis. A survey of
community-dwelling elderly showed that >90% of people
≥65 years used at least one drug weekly and more than 40%
took five or more [5]. Among hospitalized elderly, the
prevalence of polypharmacy ranged from 20% to 60%,
reflecting different criteria used to select patients and collect
medication data [6–8]. Although polypharmacy has no
generally accepted definition, most often it is defined by
cutoffs in terms of the number of medications taken ranging
from two to ten [9–12]. However, regardless of the definition,
its prevalence has been reported to increase with age and is
associated with an increased risk of inappropriate drug
prescription, underuse of effective treatment, medication errors,
poor adherence to pharmacological therapies, drug/drug and
drug/disease interactions, and adverse effects [3,13–18]. The
incidence of adverse drug reactions (ADRs) increases expo-
nentially rather than linearly with the number of drugs taken,
and advanced age and polypharmacy are associated with a
substantial increase in ADR risk [4,10,15,19–21].
Polypharmacy is often a consequence of multiple
chronic conditions, which lead physicians to prescribe
more than one drug, thus increasing the risk of disability,
hospitalization, and mortality [22–27]. Although the
available guidelines have improved and rationalized drug
prescription in many disease-oriented fields, they are still
weak for elderly people exposed to polypharmacy due to
multiple chronic conditions [28,29]. One chronic condi-
tion can potentially worsen another, and drugs can interact
negatively with others, increasing the risk of ADRs and
reducing the expected benefit. Older patients and patients
with multiple chronic disorders are almost always exclud-
ed from trials to verify drug effectiveness because of the
fear they may be unable to complete the studies due to
poor compliance, frequent side effects, or death. Subse-
quently, many drugs are prescribed to these patients even
though the drug benefit–risk profile is not known [27,28].
Many risk factors for polypharmacy have been identified,
including demographic aspects such as age, race, education,
sex, health, number of chronic diseases, living arrangements,
and number and characteristics of healthcare providers [1,2,
14–16]. Elderly patients with multiple chronic conditions are
common in general medicine specialties such as internal and
geriatric medicine. These patients are usually frail, are highly
sensitive to pharmacotherapy due to changes in their
pharmacokinetic and pharmacodynamic parameters, and are
often admitted with acute diseases, which may increase their
susceptibility to polypharmacy [10,21].
Hospitalization is a major risk for older persons,
particularly the very old. In many cases, hospitalization
is associated with an irreversible decline in functional
status, cognitive performance, and quality of life [14,20,
24,30]. Although polypharmacy and the risk of inappro-
priate use of medications in community-living and
institutionalized elderly patients have been amply de-
scribed, few studies have analyzed the prevalence,
predictors, and in-hospital outcomes of polypharmacy in
the elderly. The aims of this study were to evaluate in a
sample of hospitalized elderly people the prevalence of
polypharmacy (defined as five or more drugs) at admis-
sion and at discharge from internal and geriatric medicine
wards, to assess the prevalence of the most frequently
prescribed medications, to analyze the predictors of
polypharmacy at hospital discharge, and to investigate
the role of polypharmacy as a predictor of longer hospital
stay and increased in-hospital mortality.
Patients and methods
Methods
This prospective cohort study ran from January 2008 to
December 2008 in 38 hospitals in different Italian regions,
all of which participated in the Registro Politerapie SIMI
(REPOSI) study, a collaborative effort between the Italian
Society of Internal Medicine (SIMI) and the Mario Negri
Institute for Pharmacological Research. The REPOSI study
was designed with the purpose of setting up a network of
internal medicine wards to investigate the prevalence and
correlates of polymorbidity and polypharmacy in hospital-
ized elderly patients. Participation was voluntary, but we
were careful to ensure participating centers were represen-
tative in terms of countrywide distribution and size and had
unselected admissions from their own territory or the
emergency room. The REPOSI study was specifically
designed to describe the prevalence of multiple concurrent
diseases and treatments in hospitalized elderly patients, to
correlate the patient’s clinical characteristics with the type
and number of diseases and treatments, and to evaluate the
main clinical outcomes at discharge.
508 Eur J Clin Pharmacol (2011) 67:507–519
p=0.02), comorbidities (OR 1.18; 95% CI 1.12–1.24; p<
0.0001), and AEs (OR 6.80; 95% CI 3.58–12.9; p<0.0001)
were significantly associated with in-hospital mortality.

Study population
Patients were eligible for REPOSI if: (1) they were admitted
to one of the 38 participating internal medicine wards during
the 4 index weeks chosen for recruitment (one in February,
one in June, one in September, and one in December 2008);
(2) their age was ≥65 years; 3) they gave informed consent.
Each ward had to enrol at least the first ten consecutive
eligible patients during each index week. During each index
week, all wards had to complete the register of all patients
admitted to the ward and indicate those who were consecu-
tively enrolled in the study. For patients who were excluded,
the reason had to be given. On the basis of these data, during
the 4 weeks, the recruitment rate for each ward was nearly
40% of patients admitted. Sixty-eight percent of them were
excluded because of age <65 years. Other reasons for
exclusion were refusal to participate or to sign informed
consent (23%), seriousness of patient’s clinical condition or
admission in terminal state (6%), and other reasons (3%). No
difference for age or sex (the only available data) emerged for
these patients in comparison with the enrolled sample. Of
1,411 patients enrolled, 79 (5.6%) were excluded because
of missing or incomplete data (25 had missing data on
hospital outcome and 54 on most sociodemographic and
clinical characteristics due to errors or omissions in data
input and recording), and 1,332 fulfilled the requirements
for the analysis. The 54 patients excluded because of
missing data showed no significant differences in out-
comes in comparison with the analyzed cohort.
Data collection
All data obtained from the patient’s medical records were
entered into a standardized Web-based “essential”Case
Report Form (CRF) by the attending physicians. The
following data were recorded for each patient: basic
sociodemographic details, clinical parameters, diagnoses
and treatments at hospital admission and discharge, clinical
events in hospital, and outcome. Before starting the enrol-
ment, all investigators received instructions on how to
standardize the procedure for patient inclusion and how to
enter and code data in the electronic CRF. All data were
collected and checked by a central monitor institution
(Mario Negri Institute for Pharmacological Research,
Milan) in full compliance with Italian law on personal data
protection. Under the applicable legal principles on patient
registries, the study did not require the approval of an
ethical committee.
Diagnoses and comorbidities
At admission, the main reason for that admission and any
comorbidities were recorded. At discharge, all diagnoses
listed in the medical record were listed, confirmed by
clinical examination, anamnesis, laboratory, and instrumen-
tal data collected by the attending physicians, and encoded
according to the International Classification of Diseases,
9th Revision, Clinical Modification (ICD-9-CM), Sixth
Edition (World Health Organization, 1987) [31]. Chronic
diseases identified by Veehof et al. [12] analyzed as
potential predictors of polypharmacy were hypertension,
ischemic heart disease, atrial fibrillation, heart failure,
diabetes mellitus, chronic obstructive pulmonary disease
(COPD) or bronchial asthma, osteoporosis or osteoarthritis,
gastrointestinal disease (peptic or duodenal ulcer, nonspe-
cific bowel inflammation), and psychiatric disorders (de-
mentia, depression). Depression was eventually excluded
because it was present in only four patients. Comorbidity
was evaluated by the Charlson comorbidity index [32], a
method used in longitudinal studies for classifying comor-
bid conditions that might affect the risk of mortality. This
weighted index takes into account the number and
seriousness of comorbid diseases. Specific diseases are
graded in three levels of severity: low (≤2), moderate (3–4),
and severe (≥5) according to the level of individual organ
function and prognostic importance.
Drugs and polypharmacy
All drugs being taken at hospital admission and all
medications recommended at discharge were recorded and
encoded according to the Anatomical Therapeutic Chemical
classification system (ATC) (WHO 1990) [33]. Drug
classes were determined in relation to the ATC third-level
(pharmacological subgroup) classification. The numbers of
drugs taken at admission and discharge were compared for
each patient. Although there is still no consensus or
commonly used cutoff for polypharmacy, as previous
studies have mostly used four or five drugs as cutoff
points, and most drugs prescribed to elderly patients are for
chronic therapies, we defined polypharmacy as exposure of
a patient to five or more different medications [2–4,9]. The
prevalence of polypharmacy was analyzed in 5-year age
brackets: 65–69, 70–74, 75–79, 80–84, 85–89,≥90 years.
Polypharmacy was evaluated at hospital admission (all
1,332 patients) and at discharge (the 1,155 sent home). Data
on drug prescription were not available for 111 patients
who were not discharged to go home and for 66 who died
in hospital. However, no statistically significant difference
was observed at admission between the sociodemographic
and clinical characteristics of 111, 66, and 1,155 patients.
Predictors, clinical events, and adverse outcome
Patients’characteristics, diagnoses, comorbidity, and AEs
were considered potential predictors of polypharmacy. AEs
Eur J Clin Pharmacol (2011) 67:507–519 509

were defined as any change in the individual’s health—with
specific symptoms and signs of recent onset—occurring
after hospital admission [34,35]. Length of hospital stay
and in-hospital mortality were used as outcome measures
when evaluating the effect of polypharmacy. Of the 111
patients who were not discharged to go home and were
subsequently excluded from the analysis, 61 were trans-
ferred to other hospital wards because of acute medical or
surgical disease during hospitalization, 44 were transferred
to rehabilitation units or long-term-care facilities, and six
were terminally ill at admission and thus transferred to end-
of-life-care units.
Statistical analysis
Categorical variables were expressed as frequency and
percentages and continuous variables as means [± standard
deviation (SD)]. The differences in distribution of categor-
ical variables between patients with and without polyphar-
macy were computed using the chi-squared test, whereas
the t-test was used for continuous variables. Determinants
of polypharmacy treatment at discharge were studied in 551
patients without polypharmacy at admission. We applied
logistic regression models to identify patient-related char-
acteristics associated with polypharmacy. Each model was
adjusted for sex, age, number of drugs at admission, and
occurrence of at least an AE in hospital. Diagnoses were
separately tested (hypertension, ischemic heart diseases,
heart failure, atrial fibrillation, diabetes mellitus, COPD,
dementia, cerebrovascular diseases, liver diseases, gastro-
intestinal disorders, osteoporosis/osteoarthritis, chronic re-
nal failure, anemia, and malignancy). For each variable, we
calculated the odds ratio (OR) and 95% confidence interval
(95% CI). Outcome measures (predictors of length of
hospital stay and in-hospital mortality) were analyzed using
linear and logistic regression analysis, respectively. Regres-
sion coefficients, OR and their 95% CI were calculated at
univariate analysis and after adjustment for sex, age,
comorbidities at admission, number of drugs at admission,
and occurrence of at least one AE in hospital. In all
multivariate models, standard errors (SE) were corrected to
allow for the nonindependence of patients within the same
ward. All statistical calculations were done with the
software JMP v 8.0.2 (SAS Institute Inc., Cary, NC,
USA) and STATA v11.1 (Stata Corp LP, College Station,
TX, USA).
Results
Of the 1,332 patients enrolled, 772 (54.2%) were women,
the average age was 79.4 years (SD ± 7.5), and 616 (46%)
were ≥80 years or more. At admission, almost 90% of
patients came from home, where 42% lived with their
spouse, 24% alone, 19% with children, and the remainder
from nursing home. The mean number of diagnoses was 5.2
(SD±2.3). The Charlson comorbidity index mean score was
0.5 (SD±0.8), and 545 (40%) patients had a Charlson index
rated as moderate or severe. The most frequent diagnoses at
hospital admission were hypertension (57.8%), diabetes
mellitus (24.0%), coronary heart disease (CHD, 23.0%),
atrial fibrillation (AF, 20.6%), COPD (20.0%), and cardio-
vascular disease (CVD) (19.5%).
Prevalence of polypharmacy
At admission, patients had an average of 5.2 (SD±2.3)
diagnoses, were in treatment with an average of 4.9 (SD±2.9)
drugs, and 51.9% were taking five or more different drugs
(polypharmacy). Figure 1shows the prevalence of poly-
pharmacy at admission in relation to age. The prevalence
rate was highest at ages 70-74 and 80-84 years, with nearly
60% of patients in polypharmacy. At discharge, the
prevalence of patients in polypharmacy increased from
51.9% to 67.0% (+15.1%); patients were discharged with
an average of 6.0 (SD±2.9) drugs per person and an average
of 5.9 (SD+ 2.5) diagnoses.
Among the 1,155 patients discharged, 341 (29.5%) had
no polypharmacy at either admission or discharge, 210
(18.2%) shifted to polypharmacy at discharge, 40 (3.5%)
were on polypharmacy at admission but not at discharge,
and 564 (48.8%) were on polypharmacy at both admission
and discharge. Figure 2compares patient distribution in
relation to the number of drugs at admission and at
discharge and shows a clear increase in the number of
patients receiving five or more different medications at
discharge.
Table 1lists the ten most frequently prescribed drug
classes at admission and discharge. Except for blood-
glucose-lowering drugs (A10B), the prevalence of treated
patients was higher at discharge.
Predictors of polypharmacy
Tab le 2shows the results of univariate analysis of
polypharmacy predictors in relation to sociodemographic
and clinical characteristics for the 1,332 patients at
admission and the 1,155 discharged. In both samples,
age ≥85 years, education, number of diagnoses, comor-
bidity, number of drugs, number of AEs during hospital
stay, diagnosis of hypertension, ischemic heart disease,
atrial fibrillation, heart failure, diabetes mellitus, COPD,
chronic renal failure, and osteoporosis/osteoarthritis were
associated with the use of polypharmacy. At admission, a
diagnosis of gastrointestinal disorders was also positively
related to polypharmacy.
510 Eur J Clin Pharmacol (2011) 67:507–519

Table 3shows analyses of the 551 patients without
polypharmacy at admission, comparing sociodemographic
and clinical characteristics of the 341 who received fewer
than five drugs at discharge and the 210 discharged with
polypharmacy. Upon univariate analysis, the number of
diagnoses, having at least one AE, length of hospital stay,
and diagnosis of hypertension, ischemic heart disease, atrial
fibrillation, heart failure, COPD, osteoporosis/osteoarthritis,
or chronic renal failure were predictors of polypharmacy at
discharge.
At multivariate analysis in the full sample of 1,155
patients for whom prescription data were available both at
admission and discharge, age, number of drugs at admis-
sion, hypertension, ischemic heart disease, heart failure, and
COPD were the most important predictors of polypharmacy
(Table 4).
In a post hoc analysis of the same 1,155 patients, the
predictors of changes in the numbers of drug from
admission to discharge were evaluated. For 148 patients
(13.0%), the number of drugs at discharge were fewer than
at admission (delta <0), whereas for 295 (25.4%), there was
no difference (delta=0) and for 712 (61.6%) there was an
increase (delta >0). Each disease was separately tested in
multivariate analyses to assess whether it was responsible
for the change in the number of drugs at discharge.
Hypertension increased the number of drugs at discharge
Fig. 1 Prevalence of polyphar-
macy at admission in different
age groups, in 1,332 patients
Fig. 2 Prevalence of drug
use at admission and at
discharge in 1,155 patients
Eur J Clin Pharmacol (2011) 67:507–519 511

Length of hospital stay and in-hospital mortality
In the entire sample of 1,332 elderly patients, 1,155 (86.7%)
were discharged to go home, 111 (8.3%) were transferred to
another ward, and 66 (5.0%) died in hospital. The average
hospital stays were, respectively, 13.1 (SD±11.6), 13.1 (SD±
11.3), and 10.7 (SD ± 8.0) days. Table 5shows the outcomes
at discharge in relation to polypharmacy at admission and
the univariate and multivariate analyses of predictors of
length of hospital stay and in-hospital mortality. Both
analyses found that at least one AE while in hospital was
positively related to the time in hospital, prolonging it by
3.57 days (95% CI 2.32–4.83; p<0.0001).
Sensitivity analysis
As the chosen cutoff of five or more drugs to define
polypharmacy is not unanimously accepted, and to estimate
the sensitivity of the results to this cutoff, we re-ran the
multivariate analysis for four and six or more drugs.
Estimates for predictors of length of hospital stay and
mortality were almost identical to those reported in Table 5,
with statistical significance confirmed for the same varia-
bles. Among different diseases, whereas hypertension and
heart failure were still statistically significant, others were
not always present, probably because of random fluctua-
tions or complex interactions between the single variables
in the full model.
Discussion
This study shows how common polypharmacy is in elderly
patients admitted to an internal medicine or geriatric ward.
More than 50% of patients aged ≥65 in a network of 38 Italian
internal medicine hospital wards and who voluntarily partic-
ipated in the REPOSI study were taking five or more different
drugs (polypharmacy), mostly as chronic therapies. Poly-
pharmacy was more common in people between 70 and
84 years of age and was related to comorbidities. The decline
in polypharmacy in people >85 years of age might be due to
poor drug tolerance with age or with doctors’fears of serious
side effects being more common in very elderly patients, as a
reduced morbidity with age is unlikely. Hospitalization did
not lead to a reduction in the number of drugs. Only 13% of
patients had fewer drugs prescribed at discharge than at
admission, and >60% had an increase. Moreover, among
patients admitted with polypharmacy, only 3.5% were dis-
charged with fewer than five different medicines. This finding
suggests that most disorders affecting elderly people admitted
to hospital are chronic and need stable therapy. The increase in
the number of prescriptions at discharge suggests that
hospitalization leads to new diagnoses that require further
drugs or that old therapies need to be replaced by new, more
complex, therapies. The increases involved all classes of
At admission (1,332
patients)
At discharge (1,155
patients)
n(%) n(%)
B01A, antithrombotic agents 692 (52.0) 690 (59.7)
A02B, drugs for peptic ulcer and GERD 546 (41.0) 647 (56.0)
C03C, high ceiling diuretics 465 (34.9) 454 (39.3)
C09A, ACE inhibitors 359 (27.0) 361 (31.3)
C07A, beta-blocking agents 334 (25.1) 329 (28.5)
C10A, lipid modifying agents 230 (17.3) 210 (18.2)
A10B, blood glucose lowering drugs, excl. insulins 205 (15.4) 168 (14.6)
C01A, cardiac glycosides 119 (8.9) 105 (9.1)
C01D, vasodilators used in cardiac diseases 224 (16.8) 223 (19.3)
A10A, insulins and analogues 111 (8.3) 129 (11.2)
Tab l e 1 Ten most frequently
prescribed drug classes: third
level of the Anatomical
Therapeutic Chemical
classification system
GERD gastroesophageal reflux
disease, ACE angiotensin-
converting enzyme
512 Eur J Clin Pharmacol (2011) 67:507–519
by a mean of 0.33 (95% CI 0.09–0.56; p=0.008)
independently of sex, age, number of drugs at admission,
ward, and occurrence of at least one AE in hospital.
Likewise, ischemic heart disease, heart failure, and COPD
increased the number of drugs at discharge by a mean of
0.28 (95% CI 0.04–0.53; p=0.02), 0.61 (95% CI 0.22–
1.00; p=0.003), and 0.61 (95% CI 0.28–0.95; p= 0.001).
Dementia reduced the number of drugs at discharge by a
mean of 0.76 (95% CI 0.30–1.22; p=0.0002).
Predictors of in-hospital mortality were age (OR= 1.04; 95%
CI 1.01–1.09; p=0.02), and comorbidity (OR= 1.18; 95% CI
1.12–1.24; p<0.0001). An AE during hospital stay increased
the risk of in-hospital mortality by nearly sevenfold, indepen-
dently of sex, age, comorbidity, or polypharmacy (OR=6.80;
95% CI 3.58–12.9; p<0.0001). Polypharmacy was not a
predictor for either length of hospital stay or in-hospital
mortality. Adding the ward to the covariates of logistic
regression analysis had no effect on the results.

drugs, thus excluding the possibility that the additional drugs
were needed because of inadequate treatment at home.
Prevalence of polypharmacy
Few studies have analyzed the prevalence of polypharmacy
in a general hospital setting. They used different thresholds
for polypharmacy, and prevalence at admission ranged from
20% to 60% [6,7] and was higher at hospital discharge. In
the Wawruch study [7], which analyzed 600 patients
aged ≥65 years in an internal medicine ward, the prevalence
of polypharmacy (six or more drugs) at admission was
60%, increasing to 62% at discharge. Another study [8]
found that among 543 patients aged ≥75 admitted to
Table 2 Univariate analysis of predictors of polypharmacy (five or more drugs) at admission and discharge
Variables Admission (1,332) Discharge (1,155)
No polypharmacy Polypharmacy Pvalue No polypharmacy Polypharmacy Pvalue
(641) (691) (381) (774)
Sociodemographic variables
Females, n(%) 359 (56.0) 363 (52.5) 0.20 206 (54.1) 414 (53.5) 0.85
Age (years), mean (±SD) 79.6 (8.0) 79.2 (7.2) 0.35 79.8 (8.2) 78.9 (7.2) 0.08
Age≥75 years, n(%) 446 (69.6) 482 (69.8) 0.94 268 (70.3) 533 (68.9) 0.61
Age≥85 years, n(%) 178 (27.8) 146 (21.1) 0.004 109 (28.6) 160 (20.7) 0.003
Education (years), mean (±SD) 6.1 (3.5) 6.54 (3.8) 0.03 5.9 (3. 7) 6.6 (3.8) 0.006
Widow/er, n(%) 265 (41.3) 282 (40.8) 0.89 161 (42.2) 310 (40.1) 0.54
Living alone, n(%) 152 (23.7) 163 (23.6) 0.95 102 (26.8) 176 (22.7) 0.15
Clinical variables
Number of diagnoses, mean (±SD) 3.3 (2.0) 5.1 (2.3) <0.0001 5.0 (2.1) 6.8 (2.4) <0.0001
Five or more diagnoses, n(%) 273 (42.6) 527 (76.3) <0.0001 126 (33.1) 556 (71.8) <0.0001
Charlson index, mean score (±SD) 2.0 (2.6) 3.0 (2.5) <0.0001 2.2 (2.3) 3.3 (2.9) <0.0001
Charlson index score≤2, n(%) 455 (71.0) 322 (48.1) <0.0001 255 (67.3) 330 (42.6) <0.0001
Charlson index score =3-4, n(%) 125 (19.5) 220 (31.8) 85 (22.4) 271 (35.1)
Charlson index score≥5, n(%) 61 (9.5) 139 (20.1) 39 (10.3) 173 (22.3)
Number of drugs, mean (±SD) 2.6 (1.2) 7.1 (2.1) <0.0001 4.2 (1.9) 7.7 (2.6) <0.0001
Five or more drugs, n(%) –691 (51.9) –– 774 (67.0) –
AEs, mean (±SD) 0.6 (1.1) 0.7 (1.2) 0.083 0.5 (1.0) 0.6 (1.1) 0.02
One AE, n(%) 137 (21.4) 160 (23.2) 0.06 80 (21.0) 160 (20.7) 0.01
More than one AEs, No. (%) 78 (12.2) 110 (15.9) 32 (8.4) 112 (14.5)
Hospital stay - days, mean (±SD) 11.0 (9.3) 11.0 (7.9) 0.97 10.2 (8.6) 10.9 (7.7) 0.15
Comorbid diseases, No. (%)
Hypertension 334 (52.1) 441 (63.8) <0.0001 192 (50.4) 509 (65.8) <.0001
Ischemic heart disease 72 (11.2) 242 (35.0) <0.0001 37 (9.7) 259 (33.5) <.0001
Atrial fibrillation 92 (14.4) 165 (23.9) 0.0001 54 (14.2) 198 (25.6) <.0001
Heart failure 63 (9.8) 129 (18.7) <0.0001 21 (5.5) 194 (25.1) <.0001
Diabetes mellitus 86 (13.4) 244 (35.3) <0.0001 55 (14.4) 257 (33.2) <.0001
COPD/bronchial asthma 98 (15.3) 174 (25.2) <0.0001 53 (13.9) 198 (25.6) <.0001
Dementia 45 (7.0) 59 (8.5) 0.30 41 (10.8) 65 (8.4) 0.18
Cerebrovascular disease 133 (20.8) 172 (24.9) 0.07 92 (24.2) 216 (27.9) 0.19
Depression 3 (0.5) 7 (1.0) 0.25 1 (0.3) 3 (0.4) 0.74
Liver disease 50 (7.8) 72 (10.4) 0.10 43 (11.3) 93 (12.0) 0.74
GI disorders 84 (13.1) 132 (19.1) 0.003 60 (15.8) 151 (19.5) 0.13
Osteoporosis/osteoarthritis 96 (15.0) 109 (15.8) 0.69 43 (11.3) 132 (17.1) 0.01
Chronic renal failure 47 (7.3) 153 (22.1) <0.0001 27 (7.1) 163 (21.1) <0.0001
Anemia 88 (13.7) 102 (14.8) 0.59 67 (17.6) 165 (21.3) 0.15
Malignancy 114 (17.8) 132 (19.1) 0.54 100 (26.3) 184 (23.8) 0.33
AE, Adverse clinical event; COPD, Chronic obstructive pulmonary disease; GI, gastrointestinal
Eur J Clin Pharmacol (2011) 67:507–519 513

selected internal medicine wards, 58% were taking more
than six different medications. In another study, 57% of
2,465 elderly patients admitted to geriatric and internal
medicine wards were using more drugs at discharge than in
the month before admission [6]. Our analysis showed that
nearly 40% of 1,155 elderly patients discharged to home
who at admission were taking fewer than five different
medicines had shifted to polypharmacy at discharge. On
average, they were taking five drugs at admission and six at
discharge, thus fewer than in other studies, which reported
an average of six and seven drugs at admission and
discharge, respectively [6,8].
Antithrombotic agents were the most prescribed drugs
both at admission and at discharge in our study, followed
by drugs for peptic ulcer and gastroesophageal reflux
disease (GERD), high-ceiling diuretics, and ACE inhibitors.
Drugs for peptic ulcer and GERD showed the largest
increase at discharge, shifting from 41% at admission to
56%. This might indicate an area of inappropriate prescrib-
ing that was specifically assessed in an ad hoc analysis [36]
in which >60% of patients receiving these drugs had no
specific indication for their use. Predictors of polypharmacy
in hospital in-patients have been examined only in a few
studies [6,7], which indicated different sociodemographic
Variables No polypharmacy Polypharmacy Pvalue
341 (61.9%) 210 (38.1%)
Sociodemographic variables
Females, n(%) 182 (53.4) 120 (57.1) 0.39
Age (years), mean (±SD) 79.6 (8.2) 79.1 (7.5) 0.49
Age≥75 years, n(%) 237 (69.5) 144 (68.6) 0.82
Age≥85 years, n(%) 95 (27.9) 51 (24.3) 0.36
Education (years), mean (±SD) 5.9 (3.6) 6.5 (3.6) 0.09
Widow/er, n(%) 140 (41.1) 79 (38.0) 0.48
Living alone, n(%) 87 (25.7) 43 (20.7) 0.18
Clinical variables
Number of diagnoses, mean (±SD) 3.8 (1.8) 5.0 (1.9) <0.0001
Five or more diagnoses, n(%) 101 (29.6) 121 (57.6) <0.0001
Charlson index, mean score (±SD) 1.7 (2.2) 2.1 (2.6) 0.04
Charlson index score≤2, n(%) 256 (75.1) 144 (68.6) 0.24
Charlson index score=3-4, n(%) 59 (17.3) 47 (22.4)
Charlson index score≥5, n(%) 26 (7.6) 19 (9.1)
Number of drugs, mean (±SD) 2.2 (1.2) 3.2 (1.0) <0.0001
AEs, mean (±SD) 0.4 (0.9) 0.6 (1.2) 0.04
One AE, n(%) 71 (20.8) 35 (16.7) 0.01
More than one AE, n(%) 25 (7.3) 32 (15.2)
Hospital stay (days), mean (±SD) 9.9 (8.7) 11.5 (8.1) 0.03
Comorbid diseases, n(%)
Hypertension 156 (45.8) 139 (66.2) <0.0001
Ischemic heart disease 26 (7.6) 33 (15.7) 0.003
Atrial fibrillation 35 (10.3) 38 (18.1) 0.009
Heart failure 13 (3.8) 38 (18.1) <0.0001
Diabetes mellitus 40 (11.7) 32 (15.2) 0.24
COPD/bronchial asthma 37 (10.9) 40 (19.1) 0.007
Dementia 25 (7.3) 10 (4.8) 0.23
Cerebrovascular disease 67 (20.0) 47 (22.4) 0.44
Depression 1 (0.3) 2 (1.0) 0.31
Liver disease 33 (9.7) 15 (7.1) 0.31
GI disorders 46 (13.5) 25 (11.9) 0.59
Osteoporosis/osteoarthritis 38 (11.1) 41 (19.5) 0.006
Chronic renal failure 19 (5.6) 16 (7.6) 0.034
Anemia 46 (13.5) 25 (11.9) 0.59
Malignancy 63 (18.5) 29 (13.8) 0.15
Table 3 Univariate analysis
of predictors of polypharmacy
(five or more drugs) at discharge
for 551 patient admitted without
polypharmacy
AE adverse clinical event,
COPD chronic obstructive
pulmonary disease, GI
gastrointestinal
514 Eur J Clin Pharmacol (2011) 67:507–519

Variables Odds ratio
a
(95% CI) Pvalue
Females 1.00 (0.75-1.32) 0.98
Age (years), mean 0.96 (0.94-0.98) <0.0001
Number of drugs at admission, mean 2.32 (1.96-2.74) <0.0001
Adverse clinical events (one or more) 1.08 (0.72-1.63) 0.70
Hypertension 1.84 (1.34-2.53) <0.0001
Ischemic heart disease 1.72 (1.18-2.50) 0.005
Heart failure 3.77 (1.77-8.01) 0.001
Atrial fibrillation 1.09 (0.66-1.82) 0.61
Diabetes mellitus 0.88 (0.54-1.43) 0.70
COPD 1.93 (1.33-2.79) 0.001
Dementia 0.69 (0.29-1.66) 0.41
Cerebrovascular disease 0.93 (0.62-1.39) 0.73
Liver disease 0.90 (0.49-1.63) 0.72
GI disorders 0.99 (0.59-1.66) 0.98
Osteoporosis/osteoarthritis 1.44 (0.84-2.46) 0.18
Chronic renal failure 0.98 (0.51-1.89) 0.96
Anemia 0.88 (0.56-1.40) 0.59
Malignancy 0.72 (0.47-1.10) 0.13
Table 4 Multivariate analysis
of predictors of polypharmacy at
discharge among 1,155 patients
CI confidence interval, COPD
chronic obstructive pulmonary
disease, GI gastrointestinal
a
Adjusted for sex (female vs. male),
age, number of drugs
at admission, occurrence of at least
one adverse clinical event in hospi-
tal, single diagnosis, and
nonindependence of patients within
the same ward
Eur J Clin Pharmacol (2011) 67:507–519 515
Table 5 Outcome measures and predictors of outcome at discharge
No polypharmacy Polypharmacy Pvalue
(641 patients) (691 patients)
n(%) n(%)
Outcome measures
Hospital stay (days), mean (±SD) 11.0 (9.3) 11.0 (7.9) 0.97
Discharged. n(%) 551 (86.0) 604 (87.4) 0.27
Transferred. n(%) 61 (9.5) 50 (7.2)
Died. n(%) 29 (4.5) 37 (5.4)
Univariate and multivariate analysis
Length of hospital stay Regression coefficient (95% CI) Regression coefficient
a
(95% CI)
Females –0.14 (–1.06; 0.79) –0.19 (–1.34; 0.96)
Age (years) (mean) 0.00 (–0.06; 0.06) –0.02 (–0.11; 0.06)
Charlson index, at admission (mean score) 0.16 (–0.03; 0.35) 0.09 (–0.21; 0.39)
Polypharmacy (five or more drugs) 0.28 (–0.64; 1.21) –0.07 (–1.30; 1.15)
Adverse clinical events (one or more) 3.56 (2.60; 4.52)* 3.57 (2.32; 4.83)*
Mortality Odds ratio (95% CI) Odds ratio
a
(95% CI)
Females 0.97 (0.59-1.60) 0.94 (0.54-1.67)
Age (years), mean 1.05 (1.02-1.09)** 1.04 (1.01-1.08)***
Charlson index, at admission (mean score) 1.20 (1.12-1.28)* 1.18 (1.12-1.24)*
Polypharmacy (five or more drugs) 1.16 (0.71-1.92) 1.00 (0.59-1.69)
Adverse clinical events (one or more) 8.19 (4.41-15.2)* 6.80 (3.58-12.9)*
CI, confidence interval
a
Adjusted for sex (female vs. male), age, comorbidity (Charlson comorbidity index score), number of drugs at admission (five or more), occurrence of at
least one AE in hospital, and for the nonindependence of patients within the same ward
*p<0.0001, **p= 0.002, ***p= 0.02

and clinical factors such as age, sex, living alone, multiple
pathologies, and some specific chronic diseases (e.g., hyper-
tension, diabetes mellitus, heart failure, CVD, and COPD).
We, too, found that age, number of drugs at admission,
hypertension, ischemic heart disease, heart failure, and COPD
were predictors of polypharmacy at discharge. As in other
studies [6–8], the results of our study indicate that several
cardiovascular chronic diseases at admission should be
considered a predictor of polypharmacy at discharge because
the worsening of these chronic disorders might justify the
prescription of new drugs over and above previous home
therapy. Furthermore, clinicians might add new drug
therapies at discharge to manage the conditions that led to
hospital admission or to “prevent”some disease or drug-
related risk, such as gastroprotective agents for patients
taking aspirin or nonsteroidal anti-inflammatory drugs or
polypharmacy itself. Another reason for polypharmacy is
often the need to treat some chronic condition, such as
diabetes, hypertension, heart failure, and atrial fibrillation,
according to the guidelines for each disease. In many cases,
guidelines for each specific disease support the need to
prescribe more than one drug, but only in a few cases do
they take into account the patient’s comorbidities and the
number and type of drugs taken at the time of the study [10,
21,27–29,37].
Although the absolute number of drugs cannot be
considered a direct indicator of prescribing appropriateness
[38], there is growing evidence that polypharmacy is
associated with increases in many adverse outcomes,
including adverse drug reactions, drug–drug or drug–disease
interactions, falls, hospital admission, and mortality [3,4,16,
20,29]. Moreover, although analysis of the appropriateness
of drug prescribing was not the aim of our study, we stress
the importance of reviewing drug regimens for older persons
in hospital taking multiple medications, as discussed in the
Assessing Care of Vulnerable Elders (ACOVE) quality
indicators for appropriate drug [39]. However, considering
the small proportion of enrolled patients whose medications
at discharge were fewer than at admission, our study seems
to suggest that hospitalization fails as an important step for
reviewing patient’s drug regimens and that clinicians still
prefer a disease-oriented approach [28,29].
Length of hospital stay and in-hospital mortality
One study found that the number of drugs used at hospital
admission was related to the number of drug-related
problems while in hospital. The risk of a drug-related
problems increased with each additional drug supplied in
hospital. This linear relationship was present over the entire
range of drugs, without any specific number [9]. To our
knowledge, only one report analyzed the relationship
between polypharmacy and length of hospital stay and in-
hospital mortality [8]. This study, too, found that poly-
pharmacy was not related to the length of hospital stay or
in-hospital mortality. We also found, as expected, that the
occurrence of AEs in hospital was the most significant
predictor for these outcomes, prolonging hospital stay by
nearly 4 days and raising the risk of in-hospital death
sevenfold. Only a case of suspected adverse drug reaction
was reported by clinicians. This low rate is probably due to
the lack of an explicit request to signal ADRs and to the
well-known underreporting by physicians of suspected
ADRs. Although common consequences of polypharmacy
include ADRs that can negatively influence outcomes, an
account of the restricted window of observation during
hospital stay or the “diluted effect”of the number of
prescribed drugs was probably the reason we found no
effects of polypharmacy on the major clinical outcomes.
Furthermore, polypharmacy may be unavoidable and
appropriate in some patients, especially when it is carefully
prescribed and monitored.
Strengths and limitations
The major strengths of the REPOSI study are, first, its
multicenter design, which involved 38 internal medicine
and geriatric wards throughout Italy, resulting in a sample
representation of the hospitalized elderly population,
Second, patients were enrolled in four different weekly
periods (one per season) to balance the effect of seasons on
acute diseases leading to hospital admission. However,
there were some limitations to our study. First, problems
can arise when using hospital data for research, because
hospital records are not designed for research purposes but
for patient care, and their diagnostic quality may vary
depending upon each hospital, physician, and clinical unit,
as data on disability and cognitive status are not routinely
collected from these sources. Moreover, admissions are
often selective on the basis of local characteristics,
associated medical conditions, and admissions policies,
which can vary from hospital to hospital. Second, the data
set was not planned to include multidimensional geriatric
assessment because that is not a general practice in internal
medicine wards. Thus, we have no information on patients’
functional profiles. Third, the study allowed no general
conclusion regarding the number of drugs taken before
admission or on the appropriateness of the drugs already
prescribed. Also, over-the-counter drugs and herbal medi-
cines taken before admission were not included.
Conclusions
Although published studies on polypharmacy did not all
use the same cutoff point, undoubtedly, elderly in-patients
516 Eur J Clin Pharmacol (2011) 67:507–519

are exposed to a large number of drugs, often due to
chronic conditions, and hospitalization often leads to a
significant increase in the number of medications. When
assessing the risk of polypharmacy, clinicians should
carefully consider the patient’s age, the number and type
of drugs at admission, and the presence of any chronic
disease such as hypertension, ischemic heart disease, heart
failure, or COPD. At discharge, the presence of polyphar-
macy and an increase in the number of drugs should alert
clinicians to carefully review each patient’s drug portfolio
with the aim of withdrawing useless or inappropriate
medications. Last but not least, the occurrence of an AE
in hospital should raise the level of clinical monitoring for
the patient, because AEs are strongly related to the risk of
prolonging hospital stay or in-hospital mortality.
Acknowledgments We thank Professor Farncesco Violi, President of
the Italian Society of Internal Medicine, for his help and encourage-
ment. We are grateful to Judith Baggott for editorial assistance.
Financial disclosure Carlotta Franchi holds a fellowship granted by
Rotary Clubs Milano Naviglio Grande San Carlo, Milano Scala and
Inner Wheel Milano San Carlo.
Conflict of interest All the authors declare that no conflict of interest
exist. All the authors state that they have a full control of data and that
they agree to allow the journal to review their data if requested.
Funding sources Nothing.
Appendix
REPOSI collaborators and participating units
The following hospital and investigators contributed to this
study: Pier Mannuccio Mannucci, Alberto Tedeschi, Raffaella
Rossio (Medicina Interna 2, Fondazione IRCCS Ospedale
Maggiore, Milano); Guido Moreo, Barbara Ferrari (Medicina
Interna 3, Fondazione IRCCS Ospedale Maggiore, Milano);
Cesare Masala, Antonio Mammarella, Valeria Raparelli
(Medicina Interna, Università La Sapienza, Roma); Nicola
Carulli, Stefania Rondinella, Iolanda Giannico (Medicina
Metabolica, Università di Modena e Reggio Emilia); Leonardo
Rasciti, Silvia Gualandi (Medicina Interna, Policlinico S.
Orsola Malpighi, Bologna); Valter Monzani, Valeria Savojardo
(Medicina d’Urgenza, IRCCS Fondazione Ospedale Maggiore,
Milano); Maria Domenica Cappellini, Giovanna Fabio, Flavio
Cantoni (Medicina Interna 1A, Fondazione IRCCS Ospedale
Maggiore, Milano); Franco Dallegri, Luciano Ottonello,
Alessandra Quercioli, Alessandra Barreca (Medicina Interna
1, Università di Genova); Riccardo Utili, Emanuele Durante-
Mangoni, Daniela Pinto (Medicina Interna, Seconda Università
di Napoli); Roberto Manfredini, Elena Incasa, Emanuela
Rizzioli (Medicina Interna, Azienda USL, Ferrara); Massimo
Vanoli, Gianluca Casella (Medicina Interna, Ospedale di Lecco,
Merate); Giuseppe Musca, Olga Cuccurullo (Medicina Interna,
P.O. Cetraro, ASP Cosenza); Laura Gasbarrone, Giuseppe
Famularo, Maria Rosaria Sajeva (Medicina Interna, Ospedale
San Camillo Forlanini, Roma); Antonio Picardi, Dritan Hila
(Medicina Clinica-Epatologia, Università Campus Bio-
Medico, Roma); Renzo Rozzini, Alessandro Giordano
(Fondazione Poliambulanza, Brescia); Andrea Sacco,
Antonio Bonelli, Gaetano Dentamaro (Medicina, Ospedale
Madonna delle Grazie, Matera); Francesco Salerno, Valentina
Monti, Massimo Cazzaniga (Medicina Interna, IRCCS
Policlinico San Donato, Università di Milano); Ingrid Nielsen,
Piergiorgio Gaudenzi, Lisa Giusto (Medicina ad Alta Rotazione,
Azienda Ospedaliera Universitaria, Ferrara); Enrico Agabiti
Rosei, Damiano Rizzoni, Luana Castoldi (Clinica Medica,
Università di Brescia); Daniela Mari, Giuliana Micale (Medicina
Generale ad indirizzo Geriatrico, IRCCS Istituto Auxologico
Italiano, Milano); Emanuele Altomare, Gaetano Serviddio,
Santina Salvatore (Medicina Interna, Università di Foggia);
Carlo Longhini, Cristian Molino (Clinica Medica, Azienda Mista
Ospedaliera Universitaria Sant’Anna, Ferrara); Giuseppe
Delitalia, Silvia Deidda, Luciana Maria Cuccuru (Clinica
Medica, Azienda Mista Ospedaliera Universitaria, Sassari);
Giampiero Benetti, Michela Quagliolo, Giuseppe Riccardo
Centenaro (Medicina 1, Ospedale di Melegnano, Vizzolo
Predabissi, Milano); Alberto Auteri, Anna Laura Pasqui, Luca
Puccetti (Medicina Interna, Azienda Ospedaliera Universitaria
Le Scotte, Siena); Carlo Balduini, Giampiera Bertolino,
Piergiorgio Cavallo (Dipartimento di Medicina Interna,
Fondazione IRCCS Policlinico San Matteo, Università
degli Studi di Pavia); Esio Ronchi, Daniele Bertolini,
Nicola Lucio Liberato (Medicina Interna, Ospedale Carlo
Mira, Casorate Primo, Pavia); Antonio Perciccante,
Alessia Coralli (Medicina, Ospedale San Giovanni-
Decollato-Andisilla, Civita Castellana); Luigi Anastasio,
Leonardo Bertucci (Medicina Generale, Ospedale Civile
Serra San Bruno); Giancarlo Agnelli, Ana Macura,
Alfonso Iorio, Maura Marcucci (Medicina Interna e
Cardiovascolare, Ospedale Santa Maria della Misericordia,
Università di Perugia); Cosimo Morabito, Roberto Fava
(Medicina, Ospedale Scillesi d’America, Scilla); Giuseppe
Licata, Antonino Tuttolomondo, Riccardo Di Sciacca
(Medicina Interna e Cardioangiologia, Università degli
StudidiPalermo);LuisaMacchini, Anna Realdi (Clinica
Medica 4, Università di Padova); Luigi Cricco, Alessandra
Fiorentini, Cristina Tofi (Geriatria, Ospedale di Montefiascone);
Carlo Cagnoni, Antonio Manucra (UO Medicina e Primo
Soccorso, Ospedale di Bobbio, Azienda USL di Piacenza);
Giuseppe Romanelli, Alessandra Marengoni, Francesca
Bonometti (UO Geriatria, Spedali Civili di Brescia);
Michele Cortellaro, Maria Rachele Meroni, Marina Magenta
Eur J Clin Pharmacol (2011) 67:507–519 517

(Medicina 3, Ospedale Luigi Sacco, Università di Milano);
Carlo Vergani, Dionigi Paolo Rossi (Geriatria, Fondazione
IRCCS Ospedale Maggiore e Università di Milano).
Clincal data monitoring and revision: Valentina Spirito,
Damia Noce, Jacopo Bonazzi, Rossana Lombardo, Luigi
De Vittorio (Istituto di Ricerche Farmacologiche “Mario
Negri”, Milano).
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