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Use of Medications With Anticholinergic Effect Predicts Clinical Severity of Delirium Symptoms in Older Medical Inpatients
Ling Han, MD, MSc;
Jane McCusker, MD, DrPH;
Martin Cole, MD, FRCPC;
Michal Abrahamowicz, PhD;
François Primeau, MD, FRCPC;
Michel Élie, MD, FRCPC
Arch Intern Med. 2001;161:1099-1105.
ABSTRACT
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Background Use of anticholinergic (ACH) medications is a biologically plausible
and potentially modifiable risk factor of delirium, but research findings
are conflicting regarding its association with delirium.
Objectives To evaluate the longitudinal association between use of ACH medications
and severity of delirium symptoms and to determine whether this association
is modified by the presence of dementia.
Patients and Methods A total of 278 medical inpatients 65 years and older with diagnosed
incident or prevalent delirium were followed up with repeated assessments
using the Delirium Index for up to 3 weeks. Exposure to ACH and other medications
was measured daily. The association between change in medication exposure
in the 24 hours preceding a Delirium Index assessment was assessed using a
mixed linear regression model.
Results During follow-up (mean ± SD, 12.3 ± 7.0 days), 47 medications
with potential ACH effect were used in the population (mean, 1.4 medications
per patient per day). Increase in delirium severity was significantly associated
with several measures of ACH medication exposure on the previous day, adjusting
for dementia, baseline delirium severity, length of follow-up, and number
of non-ACH medications taken. Dementia did not modify the association between
ACH medication use and delirium severity.
Conclusion Exposure to ACH medications is independently and specifically associated
with a subsequent increase in delirium symptom severity in elderly medical
inpatients with diagnosed delirium.
INTRODUCTION
DELIRIUM may be the most common acute cognitive dysfunction in hospitalized
elderly patients, occurring in 11% to 26% of elderly medical or geriatric
inpatients.1-3
Delirium has been associated with prolonged hospital stay, increased functional
decline, morbidity, mortality, and nursing home placement.3-4
However, delirium is often underrecognized clinically,5-6
and, to date, evidence of intervention benefits is limited.7-8
Thus, identifying risk factors for delirium, especially modifiable risk factors,
is of great importance for effective prevention of this condition.
In recent decades, an increasing number of studies have examined risk
factors that might predispose, precipitate, or perpetuate development and
progression of delirium.9-18
Despite considerable methodological differences, most studies have found that
medication use in general, and anticholinergic (ACH) medication use in particular,
is a common precipitating risk factor.9-15,19-21
The ACH medication–delirium association may be potentially important
given its high biological plausibility, as suggested by a central cholinergic
deficit mechanism for delirium19, 22-24
and clinical correlation between serum ACH activity and delirium.18, 25-28
However, research findings to date are still controversial. Some studies
have found a significant association between use of ACH medications and delirium,23, 27-30
whereas others have not.12-14
Several reasons may underlie this discrepancy. First, studies used different
measures of ACH medication exposure, including serum ACH level,26-29
aggregate risk scores of ACH potency,27-28,30-33
or number and dose of ACH medications using different classifications.12-14,17-18
Second, the effect of ACH medications on delirium may be confounded by other
risk factors, such as dementia, age, or comorbid conditions. Third, patients
with dementia showed cognitive decline with doses of ACH medications at which
nondemented controls did not,16 suggesting
that dementia modifies the ACH-delirium relation. Finally, most published
studies considered medication use to be a precipitating factor only. Exposure
was typically measured before onset of delirium, either at a single point
or accumulated over time to the onset of delirium.11-14,17-18
Whether and to what degree ACH medications play a role in predicting the severity
of delirium symptoms after its onset has not yet been investigated, to our
knowledge. Because the types, doses, and timing of prescribed medications
can change frequently, especially in hospitalized patients, and presentation
and severity of delirium symptoms typically fluctuate over time, studies that
ignore the dynamic features of medication exposure and delirium symptoms might
be biased by a false temporal sequence or confounding by indication. Therefore,
we conducted this study to investigate the effect of ACH medication exposure
on the subsequent severity of delirium symptoms in a cohort of hospitalized
elderly patients with diagnosed delirium. Our 2 a priori hypotheses were that
(1) current exposure to ACH medications is independently associated with increased
severity of delirium symptoms and (2) the effect of ACH medication exposure
on delirium severity may depend on dementia status, with demented patients
being more sensitive to ACH medications than those without dementia.
PATIENTS AND METHODS
PATIENTS AND PROCEDURES
Study patients were inpatients diagnosed as having delirium who were
enrolled in a prospective, randomized controlled trial of a delirium geriatric
service or in an observational cohort study of outcomes of delirium (prognosis
study) at a 400-bed, university-affiliated primary acute care hospital. Consecutive
patients 65 years and older admitted from the emergency department to the
medical or geriatric services between March 1, 1996, and January 31, 1999,
were screened by a study nurse for study eligibility within 24 hours of admission.
Patients were excluded if they were (1) admitted to the hospital on a Friday
or Saturday, (2) diagnosed as having stroke or terminal illness, (3) under
intensive care or cardiac monitoring for more than 48 hours, or (4) unable
to speak or understand English or French. Eligible patients were screened
by a study nurse using the Short Portable Mental Status Questionnaire34-35 and review of nursing notes for symptoms
of delirium. Those with a questionnaire score of 3 or more or symptoms of
delirium were assessed using the Confusion Assessment Method (CAM),36 a structured interview of delirium symptoms according
to Diagnostic and Statistical Manual of Mental Disorders,
Revised Third Edition, criteria.37 Prevalent
cases were defined as those that met CAM criteria for definite or probable
delirium at hospital admission. Patients without delirium at hospital admission
were rescreened daily for the following week; those scoring at least 1 point
higher on the Short Portable Mental Status Questionnaire on any subsequent
assessment than on the initial assessment, or reported in the nursing notes
to have symptoms of delirium, were assessed using the CAM. Patients who met
the CAM criteria more than 24 hours after hospital admission were diagnosed
as having incident delirium. Both prevalent and incident delirium cases were
asked to participate in the study. Assent was obtained from the patient and
informed consent from a significant other. Both studies were approved by the
hospital's research ethics committee.
OUTCOME MEASURE
During hospitalization, all cohort members were followed up using the
Delirium Index (DI) by a research assistant masked to patients' study group,
medication use, and other patient data in medical records. Patients were assessed
at least every 3 days during the first week and weekly thereafter for 8 weeks
or until death or discharge from the hospital. For this study, we analyzed
DI data collected during the first 21 days because DI assessments were sparse
after day 21, when most patients were discharged or dead.
The DI was developed by our group, based on the CAM, to rate the severity
of 7 delirium symptoms: altered attention; disorganized thinking; disorientation;
and disturbances in consciousness, memory, perception, and motor activity.
Each symptom is scored as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe),
with total scores ranging from 0 (no symptoms) to 21 (severe), based only
on observation of individual patients. Interrater reliability between psychiatrists
(M.C. and F.P.) and research assistants (intraclass concordance coefficient
= 0.88) and concurrent validity with the Trepacz Delirium Rating Scale38 (r = 0.84) are satisfactory.
MEDICATION EXPOSURES
Data on medications were extracted from patient hospital charts by a
nurse using a standard form. For medications the patient was receiving at
the time of enrollment, the information abstracted included route (oral, intramuscular,
intravenous, etc); dose; and frequency of administration, collapsed into either
use as needed or regular use (once, twice, or multiple administrations per
day). Also abstracted were the date and types of dose and frequency changes
during hospitalization by recording whether a medication was newly added or
removed and whether the dosage was increased or decreased each day. This information
was used to calculate 5 measures of daily medication exposure:
- Summers' Drug Risk Number (DRN): We used the Class
of Drug developed by Summers30 for 62 medications,
a 3-level (III being the highest) ordinal scale denoting the synergistic and
central nervous system ACH potency of a medication,25, 28, 31-33
as used previously.31 Medications that were
not included in Summers' Class of Drug list were given a score of 0.
- Clinician-rated ACH score: Because Summers' classification,
published in 1978, did not include many newer medications, we developed an
alternative measure of ACH medication exposure. First, we established a list
of 340 medications that included those used in our population and those reported
to have ACH effect in the literature.17-18,30, 35, 39-43
Second, 3 geriatric psychiatrists (M.C., F.P., and M.E.) independently rated
the ACH effect of each medication from 0 (none) to 3 (high) based on their
clinical experience and knowledge of the properties of the medications. Then
we assessed the interrater reliability of the 3 clinicians' ratings for all
340 medications and the concordance of the mean and median values of the 3
clinicians' ratings with Summers' Class of Drug30
and 3 sources of laboratory data,39-41
respectively. We selected the median value of the clinicians' ratings based
on high correlations between the 3 clinicians' ratings for the 340 medications
and strong agreement of the median ratings with Summers' Class of Drug (r = 0.71, n = 62) and with the ACH effect ratings from
laboratory data (r = 0.56-0.65; n = 14-32).
- Number of ACH medications was a count of all the
medications with a clinician-rated ACH score greater than 0.
- Number of non-ACH medications was a count of all
the medications with a clinician-rated ACH score of 0.
- Total number of medications was a count of all
the medications, ACH and non-ACH, and is a commonly used measure of medication
exposure.9, 11, 13
Because certain antipsychotic medications may be prescribed to control
certain symptoms of delirium, the clinician-rated ACH score and number of
ACH medications were also recomputed excluding antipsychotic medications (7
agents).
These measures of medication exposure were used as time-dependent variables,
ie, measured each day during follow-up. Because drug doses were not available,
except at enrollment, for computation of the Summers' DRN and clinician-rated
ACH score we assigned a priori selected weights to each type of change for
each medication used with respect to its previous dose and frequency. At baseline,
each regularly prescribed medication, regardless of actual frequency
and dose, was given a weight of 1.0, whereas that prescribed on an as-needed
basis was given a weight of 0.5. For each medication used during days without
dose or frequency changes, the corresponding DRN and ACH scores were assumed
to be equal to those at baseline or on the last day of dose or frequency change
(ie, weight = 1), whichever was more recent. For any day when the dose or
frequency of a medication changed, the 2 medication exposures were approximated
by multiplying the exposures on the previous day by a factor of 1.5 for an
increase or 0.67 for a decrease. (A list of the clinician-rated ACH scores
for the 234 medications evaluated in this study and a case scenario to demonstrate
the weighting strategy are available from the authors on request.)
Because toxic delirium typically starts within hours of drug or other
chemical substance intake,19, 24, 37
we defined the day before DI assessment as the exposure time window. For instance,
if a patient was assessed with the DI on day 3, his or her DI score was associated
with the medication exposure measured for day 2, a strategy similar to that
used by Marcantonio et al.12
CONFOUNDING OR MODIFYING VARIABLES
Potentially important confounding or effect-modifying variables included
a time-dependent variable, length of follow-up since enrollment, and the following
fixed baseline variables. Dementia was assessed using the Informant Questionnaire
on Cognitive Decline in the Elderly (IQCODE), a 16-item, clinically validated
instrument based on interviewing a family member that uses a cutoff score
greater than 3.5 to define dementia.44-45
Because of some missing family interviews, we retained a category for missing
IQCODE. The Mini-Mental State Examination45-46
was used descriptively at enrollment but was not used to define dementia due
to potential confounding by delirium symptoms. Comorbidity was assessed with
the Charlson Comorbidity Index using data abstracted from the hospital chart
by a nurse abstracter masked to study group.47
Laboratory variables, including serum albumin (abnormal, <33 g/L) and serum
urea nitrogen–creatinine ratio,10 were
also abstracted from patient hospital charts. Patients with missing data were
assumed to have normal values. Visual and hearing impairment were assessed
clinically at enrollment for presence or absence. History of alcohol and/or
other drug abuse (present or absent) was obtained from an informant. Sociodemographic
information included sex, age, marital status, and living arrangement before
hospital admission (home vs other). Other confounders included prevalent vs
incident delirium and study group, denoted by 2 dummy variables: intervention
vs control and trial vs prognosis.
STATISTICAL ANALYSES
Preliminary analyses included descriptive statistics of medication exposure
and potential confounding or modifying variables. In patients with multiple
DI measurements, the within-patient mean of each time-dependent variable was
calculated based on all days preceding DI assessments.
An unbalanced repeated-measures analysis of variance model using the
SAS 6.12 MIXED procedure48 was used to account
for (1) repeated measurements of the DI (dependent variable) and exposure
for the same individual and (2) unbalanced design, ie, the fact that the number
of available DI scores or their timing varied from patient to patient. This
procedure allows for a mixture of between-patient (fixed at baseline) and
within-patient (time-dependent) covariates. We assumed that the covariance
structure of errors, autoregressive order 1, will account for the dependence
of subsequent observations on the same patient and used the Akaike Information
Criterion for comparison with alternative structures.49-51
All models were estimated using restricted maximum likelihood estimation.48
Effects of the 3 ACH variables—Summers' DRN, clinician-rated ACH
score, and the number of ACH medications—were each assessed in a separate
model, adjusting for the number of non-ACH medications or the total number
of medications, follow-up duration, dementia, age, Charlson Comorbidity Index
score, visual or hearing impairment, serum albumin level, living arrangement
before hospital admission, type of delirium, and study group. In addition,
all models included the baseline DI score, allowing us to evaluate the exposure
effect in patients with the same initial delirium severity and thereby reduced
potential confounding by indication, which could occur if symptomatically
severe patients are prescribed more ACH medications.
Model estimation was conducted using a 3-step strategy. First, we fitted
a model with 14 a priori selected covariates. Second, we assessed the following
additional covariates one at a time to test their potential confounding effects:
(1) sex, (2) serum urea nitrogen–creatinine ratio, and (3) history of
alcohol or other drug abuse. Finally, we tested the interactions of ACH medication
exposure with dementia in the models selected via the previously stated procedure.
In sensitivity analysis, the final models incorporated modified measures of
ACH medications, ie, antipsychotic agents were excluded from ACH category.
A significance level of P<.05 was used for hypothesis
testing.
RESULTS
CHARACTERISTICS OF THE STUDY POPULATION
Of 293 patients with delirium enrolled, we excluded 15 with only a baseline
DI assessment, leaving 278 patients (191 from the trial and 87 from the prognosis
study). Table 1 presents the characteristics
of this delirium cohort. Table 2
summarizes different aspects of variation over time in time-dependent variables
representing medication exposures and delirium severity. During the 21 days
of follow-up, the mean ± SD number of DI assessments was 5.7 ±
2.8. The mean ± SD length of follow-up between the first and last DI
assessment was 12.3 ± 7.0 days. The 278 patients used a total of 234
medications at least once, 47 (20.1%) of which were classified as ACH medications
(clinician-rated ACH score >0). Table 3 presents the prevalence of the most frequently used ACH medications,
ie, those used by at least 3% of the study population at any time during follow-up.
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Table 1. Characteristics of 278 Patients With Delirium at Baseline
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Table 2. Characteristics of 278 Patients With Delirium: Time-Dependent
Variables*
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Table 3. Most Frequently Used ACH Medications in Patients With Delirium*
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REPEATED-MEASURES ANALYSES OF VARIANCE
There were no systematic differences in Akaike Information Criterion
values between the 3 covariance structures considered (data not shown). Therefore,
we decided to select autoregressive order 1 structure, based on its conceptual
simplicity and stability of results. Table
4 summarizes the results of 4 regression models, each using a different
combination of medication exposure variables. The regression coefficients,
their 95% confidence intervals, and the corresponding P values are shown for medication exposure and other main covariates.
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Table 4. Repeated-Measures Analyses of the Effect of ACH Medications
on Severity of Delirium in 278 Patients: Mixed Linear Regression Models*
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In the initial models including the 14 preselected covariates, the clinician-rated
ACH score was a statistically significant correlate of delirium severity on
the next day, when adjusted for the number of non-ACH medications (P<.01) (Table 4, model
1). The effect remained statistically significant even when adjusting for
the total number of medications (P<.02) (Table 4, model 2). However, the effect
of Summers' DRN was not significant when adjusted for total number of medications
(P = .35) (model 4) or number of non-ACH medications
(P = .08) (data not shown). When testing the effect
of the number of ACH medications, we adjusted for non-ACH medications but
not for total number of medications because the latter included ACH medications.
The results were consistent with model 1 in terms of the significance of the
estimated regression coefficients for the ACH medication exposure (Table 4, model 3). The effect of increasing
the number of non-ACH medications was also statistically significant, but
the effect of ACH medications was almost 5 times stronger (0.52 vs 0.11) (Table 4, model 3).
We then included in models 1 through 3 sex, serum urea nitrogen–creatinine
ratio, and alcohol and/or other drug abuse, one at a time. In all 3 models,
the effect of ACH medications remained significant after adjusting for each
of these additional covariates, whereas none of these additional covariates
was statistically significant (P>.05) (data not shown).
In each model, we also tested the interaction between ACH medication exposures
and dementia. No significant interactions were detected (P = .21-.89) (data not shown).
Models 1 and 3 were similar in terms of goodness of fit to data and
the significance of the estimate of ACH medication exposure (last row of Table 4). Translating the estimated regression
coefficients (0.27 and 0.52, respectively) into practical meaning, daily exposure
to ACH medications equivalent to 2 points (population mean scores) of clinician-rated
ACH score, or to 1.4 ACH medications (population mean number), would be associated
with an approximately 0.5- to 0.7-point increase in the subsequent DI score
when the values of all other covariates in the model remain unchanged. Sensitivity
analysis, in which antipsychotic agents were excluded, gave similar effect
estimates for the clinician-rated ACH score (0.26; 95% confidence interval,
0.07-0.44) and number of ACH medications (0.47; 95% confidence interval, 0.18-0.76)
(in models 1 and 3, respectively, data not shown).
Because the total number of medications in model 2 includes ACH medications,
the clinician-rated ACH score hypothetically represents only the "net" ACH
effect of these medications. This model provides more convincing statistical
evidence of the importance of ACH effect but has limited clinical interpretability.
Thus, model 2 is not selected as the final model.
COMMENT
In this cohort of older medical inpatients with delirium, we observed
that change in exposure to ACH medications, as defined by a clinician-rated
ACH score, was independently associated with change in severity of delirium
symptoms. This association persisted after adjusting for the total number
of medications, indicating that it is specific to the medications with suspected
ACH effect and is independent of initial severity of delirium and presence
of dementia or other comorbid conditions. Although previous studies have suggested
use of ACH medications to be a significant risk factor precipitating onset
of delirium,27-29
this study is the first, to our knowledge, to investigate the dynamic nature
of the relation between ACH exposures and the severity of delirium symptoms
and, as such, provides additional evidence for the hypothesized ACH-delirium
association. These findings may be of particular practical relevance given
the fact that increased numbers of medications are prescribed for hospitalized
elderly patients, many of whom are already at increased risk of delirium by
virtue of dementia and other acute and chronic medical problems.
Considerable evidence suggests that failure of cholinergic transmission
plays a key role in several memory disorders, including Alzheimer disease.52 Decreased synthesis of cerebral acetylcholine and
epinephrine has been postulated to account for the cognitive and attentional
impairment and for the slowing of the electroencephalographic background activity
commonly seen in delirium.53 In addition, serum
ACH activity has been associated with delirium in medical18
and postoperative patients25, 27-28
or patients who had received electroconvulsive therapy.54
Elderly patients might be more susceptible to ACH intoxication because of
aging-related reductions in cholinergic brain receptors20, 23, 27, 55
and metabolizing capacity of hepatic enzymes and because of concurrent use
of several ACH medications.17, 43
However, the results of clinical and epidemiological studies on the
ACH-delirium association are conflicting. At least 3 large prospective studies
have reported negative findings.12-14
In a cohort of elderly institutionalized patients, Schor et al14
intensively evaluated the effect of 8 pure ACH medications and a broad class
of ACH medications, including neuroleptics and tricyclic antidepressants,
on risk of developing delirium, finding no significant associations. Marcantonio
et al,12 in a nested case-control study, also
did not detect a significant effect of using ACH medications, including antihistamines,
tricyclic antidepressants, antiemetics, and certain neuroleptics. Low exposure
of the study population to ACH medications was cited as an explanation in
both studies. On the other hand, Francis et al13
observed a higher, but statistically nonsignificant, frequency of ACH medication
use in the delirium group than in controls (24% vs 15%). Because patients
with symptoms of delirium often use more ACH medications than nondelirium
patients17-18,27
or patients whose delirium has resolved,26
measuring ACH exposure by proportions of exposed persons between the 2 groups
rather than number of ACH medications taken may lead to underestimation of
the ACH-delirium association.
Our second research objective was to evaluate the interaction between
ACH medication use and dementia. Previous research has reported that patients
with Alzheimer disease are more sensitive to ACH drugs because of a central
cholinergic deficit.16, 52-53,56
Sunderland et al16 found that patients with
Alzheimer disease showed greater impairments than controls in most cognitive
tasks after receiving low doses of scopolamine hydrobromide. The absence of
a significant interaction between dementia and use of ACH medications in our
study may be due to 3 reasons. First, most of our patients had mild to moderate
severity of dementia (Mini-Mental State Examination mean score, 15.1); a cerebral
cholinergic deficit might be less evident in these patients.57
Second, the measurement error in classifying dementia or quantifying ACH exposure
might have prevented an otherwise significant interaction from being detected.
Because the Mini-Mental State Examination score of patients with dementia
might be confounded by superimposed delirium symptoms, we instead used the
IQCODE to define dementia. Although this instrument has been reported to have
good validity in the elderly population, it has not been validated in demented
patients with delirium. Third, dementia may modify the effect of ACH medications
before but not after the development of delirium.
Our study has several limitations. First, our quantitative measures
of ACH exposure may not accurately represent the ACH effect. The observable
therapeutic or adverse effect of medications on which the clinicians' ratings
are based may involve non-ACH effects, eg, antidopaminergic, antiadrenergic,
or antihistaminic effects. On the other hand, although the ratings were in
agreement with other clinical observations or experimental studies addressing
ACH properties for some of the study medications,30, 39-41
several medications rated as having little or no ACH effect by the clinicians
have been reported to have detectable serum ACH activities by radioreceptor
assay.43 Our clinicians' ACH ratings may not
take into account medications with in vitro ACH activity but without observable
clinical effects because of their inability to pass the blood-brain barrier
or another mechanism.58 However, because the
medication data were abstracted from patient medical charts using standard
procedures and the abstracter was masked to DI assessment, a differential
misclassification or systematic overcounting or undercounting of ACH medications
would be unlikely. In addition, misclassification between ACH vs non-ACH medications
would most likely be nondifferential. Thus, the expected overall impact of
potential misclassification of ACH medication exposure would be to attenuate
rather than exaggerate the true association. Similarly, the possible loss
of precision due to the use of weighted rather than exact dose change might
have biased the estimates toward rather than away from the null for ACH and
non-ACH medications. Finally, because delirium symptoms can vary over the
course of a day and our DI assessments were made at an interval of more than
a day, it is possible that some patients were given ACH medications by physicians
in response to the increase in delirium on a previous day. To assess the impact
of such a "reverse causality" or uncontrolled confounding by indication, we
excluded all the antipsychotic medications, those most likely to have been
prescribed to control delirium symptoms, with no change in the magnitude of
the association.
In conclusion, reasonable use and timely adjustment in the dose and
frequency of ACH medications used might have significant implications for
managing delirium symptoms in older medical inpatients. Further effort is
warranted to test the replicability and clinical importance of these findings
using alternative measures of delirium symptoms, ACH medications, and other
potentially important risk factors of delirium.
AUTHOR INFORMATION
Accepted for publication October 18, 2000.
This study was partially supported by grant 6605-4686-403 from the National
Health Research Development Program, Health Canada, Ottawa, Ontario (Drs McCusker
and Cole); grant MA-14709 from the Medical Research Council of Canada, Ottawa
(Drs McCusker and Cole); grant 980892 from the Fond de la Recherche en Sante
du Québec, Quebec (Drs Cole and McCusker); and a clinical research
fellowship in geriatric psychiatry from Novartis Canada Inc, Montreal, Quebec
(Dr Han).
We are deeply indebted to Eric Belzile, MSc, for his statistical assistance
in managing and analyzing the data set. We thank all the nurses and research
assistants for their assistance in collecting the data.
Corresponding author and reprints: Ling Han, MD, MSc, c/o Jane McCusker,
MD, DrPH, Department of Clinical Epidemiology and Community Studies, St Mary's
Hospital Center, 3830 Lacombe Ave, Montreal, Quebec, Canada H3T 1M5 (e-mail: jane.mccusker{at}smhc.qc.ca).
From the Departments of Psychiatry (Drs Han, Cole, Primeau, and Élie)
and Clinical Epidemiology and Community Studies (Drs Han, McCusker, and Abrahamowicz),
St Mary's Hospital Center, and the Department of Clinical Epidemiology, Montreal
General Hospital, McGill University (Dr Abrahamowicz), Montreal, Quebec.
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