Multicenter Comparison of Processes of Care Between
Stroke Units and Conventional Care Wards in Australia
Dominique A. Cadilhac, MPubHlth; Joeseph Ibrahim, PhD; Dora C. Pearce, MIT;
Kathryn J. Ogden, MPubHlth; John McNeill, PhD; Stephen M. Davis, MD; Geoffrey A. Donnan, MD;
for the SCOPES Study Group
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
Background and Purpose—Approximately 23% of Australian hospitals provide Stroke Units (SUs). Evidence suggests
that clinical outcomes are better in SUs than with conventional care. Reasons may include greater adherence to processes
of care (PoC). The primary hypothesis was that adherence to selected PoC is greater in SUs than in other acute care
models.
Methods—Prospective, multicenter, single-blinded design. Models of care investigated: SUs, mobile services, and
conventional care. Selected PoC were related to care models and participant outcomes. Data were collected at acute
hospitalization (medians 9 days) and at median of 8 and 28 weeks after stroke.
Results—1701 patients were screened from 8 hospitals, 823 were eligible, and 468 participated. Response rate was 96%
at final follow-up. Mean age was 73 years (SD 14). Overall PoC adherence rates for individual care models were SU
75%, mobile service 65%, and conventional care 52% (P⬍0.001). The adjusted odds of participants being alive at
discharge if adhering to all or all but 1 PoC was significant (aOR 3.63; 95% CI: 1.04 to 12.66; P⫽0.043). Important
trends at 28 weeks were found for being at home (aOR 3.09; 95% CI: 0.96 to 9.87; P⫽0.058) and independent (aOR
2.61; 95% CI: 0.96 to 7.10; P⫽0.061), with complete PoC adherence.
Conclusion—Adherence to key PoC was higher in SUs than in other models. For all patients, adherence to PoC was
associated with improved mortality at discharge and trends found with independence at home, providing support for the
need to increase access to stroke units. (Stroke. 2004;35:000-000.)
Key Words: stroke 䡲 stroke units 䡲 outcome and process assessment (health care)
troke affects ⬎44 000 Australians each year, with a
28-day case fatality rate of 20% for a first-ever stroke.1
Approximately 89% of people who have a stroke in Australia
are admitted to hospital,1 with care provided mainly within
the public system.2 Approximately 23% of Australian hospitals provide a dedicated service for stroke,2 with no formal
prioritization of patients who have stroke to be transported to
a hospital with a stroke unit (SU) raising equity issues.
Randomized, controlled trials of SUs have shown consistent and significant trends toward improved patient outcome
compared with conventional care.3– 6 Reductions in mortality
have been demonstrated at 3 and 12 months, and even at 10
years after stroke.7 The aspects of care delivery responsible
for better outcomes in organized services remain unclear.8
Important practices include early mobilization, physiological
homeostasis, early initiation of aspirin, and, when appropriate, thrombolysis, anticoagulation in patients with atrial
fibrillation, measures to avoid aspiration, early nutrition,
S
frequent monitoring, and management of co-morbidity to
avoid complications.6,9
The SCOPES (Stroke Care Outcomes: Providing Effective
Services) study was undertaken to provide evidence to
support greater uptake of SUs by delineating the factors that
make them more effective. This was achieved by using a
quality assessment framework underpinned by the work of
Donabedian.10 Structure (model of care), process (clinical
practices), and outcomes of care delivery were examined to
better understand the differences between alternate stroke
care models.11 The primary hypothesis was that adherence to
selected processes of care (PoC) is greater in SUs than in
other acute care models.
Materials and Methods
This was a prospective, observational, multicenter cohort design.
Researchers collecting follow-up data were blinded to the model of
care.
Received January 21, 2004; accepted January 29, 2004.
From National Stroke Research Institute (D.A.C., D.C.P., K.J.O., G.A.D), Heidelberg Heights, Victoria, Australia; Victorian Institute of Forensic
Medicine (J.I.), Southbank, Victoria, Australia; Department of Epidemiology and Preventative Medicine (J.M.), Monash University; Level 4 Department
of Neurology (S.M.D.), Royal Melbourne Hospital, Parkville, Victoria, Australia; and Department of Medicine (S.M.D., G.A.D.), The University of
Melbourne, Australia.
Correspondence to Dominique A. Cadilhac, National Stroke Research Institute, Level 1 Neurosciences Building, Repatriation Hospital, 300 Waterdale
Road, Heidelberg Heights, Victoria, Australia 3081. E-mail [email protected]
© 2004 American Heart Association, Inc.
Stroke is available at http://www.strokeaha.org
DOI: 10.1161/01.STR.0000125709.17337.5d
1
2
Stroke
May 2004
Hospital Sample
All public hospitals in metropolitan Melbourne (Victoria, Australia)
were screened. Eligibility criteria included facilities admitting ⬎100
stroke patients per year, willingness to provide patient and hospital
level data, a stroke care model operating ⬎12 months, and were not
participating in research that could influence data. All eligible
hospitals (n⫽8) participated and provided ethics committee approval. All nonparticipating hospitals were sent a questionnaire
regarding stroke care provision at their site.
Inclusion/Exclusion Criteria
TABLE 1.
of Care
SCOPES: Descriptive Summary TABLE of Processes
Applicability of
PoC n/N (%)
Adherence to
PoC i/n (%)
CT scan ⬍24 h since admission
468 (100)
447/467* (96)
Swallow ⬍24 h since admission
468 (100)
259/467* (56)
Allied health ⬍24 h since admission
468 (100)
283/467* (61)
Process Indicator
Incontinence addressed
198/468 (42)
117/198 (59)
Discharged on antiplatelet agent†
142/466 (31)
130/142 (92)
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All consecutive admissions were screened over 12 months. Inclusion
criteria were first ever or recurrent stroke (ischemic or intracranial
hemorrhage only), hospital presentation within 3 days of onset, age
older than 18, and written consent provided. Exclusion criteria were
patients transferred after admission to another hospital or an inpatient
stroke.
Fever ⱖ38.5 managed
Regular neurology observations for
the first 24 h of admission
468 (100)
341/465* (73)
Sample Size
Physiotherapist within 24 h
468 (100)
200/467* (43)
Sample sizes were estimated at 64 patients per model of care, based
on the detection of a clinically important difference in adherence
rates to a single PoC for SU 95%, mobile service 85%, and
conventional care 75%, with power of 0.8 and alpha of 0.05. We
aimed to recruit 80 patients per hospital to allow between-hospital
comparisons and to account for loss to follow-up.
Occupational therapist within 24 h
468 (100)
125/467* (27)
468 (100)
183/466* (39)
Definitions
Values are N (%). N indicates number of patients with valid data (⬍1%
missing); n, number of applicable PoC; i, number of PoC adhered to.
*Missing data in denominator.
†Appropriate new therapy for this group.
SU—a designated (geographically localized) acute ward area where
a multidisciplinary stroke team focuses its expertise. Patients were
classified as being treated in SU if admitted there directly or
transferred there during the acute admission, irrespective of duration
of stay.
Mobile service—a dedicated, hospital-based, multidisciplinary
team who review stroke patients located throughout a hospital.
Conventional care—no specific service or health professional
team dedicated to hospital stroke management.
Stroke—vascular lesion of the brain resulting in a neurological
deficit persisting for at least 24 hours or resulting in the death of the
individual.12
Overall PoC adherence—number of PoC completed/number applicable for patients within each care model.
Thorough adherence—adherence to all or all except 1 applicable
PoC.
Complete adherence—adherence to all applicable PoCs.
Outcomes—alive at discharge, alive and independent at final
follow-up, and home at final follow-up.
Independence—modified Rankin score of 0 to 2, dependence
score is 3 to 5, and death score is 6.13
Institutionalization—placement in an aged care or other medical
facility.
Data Collection
Data related to hospitalization (PoC, case-mix, and outcome) were
extracted retrospectively from case notes. When possible, case-mix
information was obtained directly from respondents to augment
information in their medical records and limit missing data. Longterm outcome data were prospectively collected.
Process of Care Data
We undertook literature reviews and expert consensus meetings (as
robust evidence of improved outcomes for some PoCs was limited)
to derive a set of clinically important PoC, which could be abstracted
from medical records. The final 21 PoCs reflected aspects of care
within 24 hours of admission, documentation, and general
management.
Additional data were collected to further explain differences
between the models of care, including use of clinical management
plans, managing doctor, number and type of clinical investigations,
the receipt of palliative care, and intrahospital transfers.
Documented premorbid function
Documented discharge needs
Speech pathologist within 24 h
53/466 (11)
468 (100)
413/466 (89)
45/53 (85)
452/467* (97)
394/413 (95)
Enteric feeding if nil by mouth ⬎48 h
130/468 (28)
100/130 (77)
Aspiration avoidance
244/467 (52)
230/244 (94)
DVT prophylaxis if not ambulant
386/468 (83)
227/386 (59)
Clinical Outcome Measures
Outcome data were collected at acute hospitalization (median 9 days;
quartiles 5, 16), median 8 weeks (quartiles 6, 10), and median 28
weeks (quartiles 26, 33). Most patient and informal career follow-up
data were collected using validated measures via telephone interview
and postal survey, unless face-to-face or proxy interviews were
required. The measures qualified stroke type and severity, quality of
life, disability, handicap, satisfaction with services, and career strain.
Data Management
Data were double-entered and a computer program was used to
identify discrepancies. An alternative researcher audited a random
selection of 10% of all acute hospital data. Variables identified as
having a low level of interobserver agreement were subjected to
auditing in all cases.
Analysis Framework
A multidisciplinary panel of local and international experts agreed on
the analysis framework.11 Fifteen of the 21 PoCs were used in the
analysis because they were deemed high-priority (selection criteria
included consideration of reliability, robustness, and importance
regarding outcome). In brief, these were: (1) activities within 24
hours, which included CT scan, swallowing assessment, allied health
assessment, neurological observations; (2) documentation of premorbid function and discharge needs; and (3) management practices:
enteric feeding if nil by mouth ⬎48 hours, measures to avoid
aspiration, deep vein thrombosis prophylaxis, fever management,
and use of antiplatelet agents at discharge (protocol developed before
published guidelines for acute therapy) (Table 1).
Patient Applicability, Adherence to Processes of
Care, and Stroke Outcomes
PoCs were examined for their applicability to participants (number
of cases applicable/total cases) and adherence rates (number of cases
adhered/total applicable cases) calculated for each (Table 1).
Cadilhac et al
TABLE 2.
Evaluation of Stroke Services in Australia: SCOPES
3
Participant Characteristics by Stroke Service
Stroke Service
Stroke Unit
(n⫽175)
Mobile Service
(n⫽209)
Conventional Care
(n⫽84)
71.1⫾14.5
73.3⫾14.4
76.1⫾12.1
P*
Baseline demographics
Age, y
Mean⫾SD
Median
77.3
0.028
Female
71 (41%)
105 (50%)
39 (46%)
0.166
Living alone before stroke
44 (25%)
58 (28%)
24 (29%)
0.790
145 (83%)
171 (82%)
59 (70%)
0.042
Current smoker†
31 (18%)
43 (21%)
11 (13%)
0.499
Atrial fibrillation
29 (17%)
46 (22%)
28 (33%)
0.010
Independent stroke‡
74.3
76.3
Risk factors
Hypertension
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103 (60%)
113 (54%)
44 (52%)
0.521
Diabetes
36 (21)
44 (21%)
17 (20%)
0.986
IHD
56 (32%)
64 (31%)
24 (29%)
0.853
TIA
14 (8%)
27 (13%)
14 (17%)
0.100
Previous stroke
36 (21%)
44 (21%)
25 (30%)
0.205
TACI
22 (13%)
47 (23%)
17 (20%)
PACI
47 (27%)
45 (22%)
24 (29%)
POCI
29 (17%)
29 (14%)
6 (7%)
LACI
47 (27%)
65 (31%)
31 (37%)
ICH
30 (17%)
23 (11%)
6 (7%)
Stroke type and severity
Oxford classification
Glasgow Coma Score (GCS)⫽15§
0.022
109 (62%)
123 (59%)
52 (62%)
0.765
117 (67%)
130 (62%)
56 (67%)
0.586
Yes
33 (19%)
44 (21%)
16 (19%)
No
129 (74%)
145 (69%)
59 (70%)
13 (7%)
20 (10%)
9 (11%)
81 (46%)
97 (46%)
35 (42%)
0.737
MRC ⱖ3 upper limb§II
124 (71%)
139 (67%)
67 (80%)
0.079
MRC ⱖ3 lower limb§¶
133 (76%)
149 (71%)
67 (80%)
0.277
GCS verbal (normal) §
Able to walk§
Not assessed
Incontinence (⬍72h)
0.851
Values are N (%) unless indicated otherwise. *Statistical comparisons of indicated values between stroke services
obtained from 1-way ANOVA for age and ␹2 tests for categorical variables.
†Data missing for 8 cases.
‡Independence⫽modified Rankin Score 0 to 2.
§On admission.
¶Medical Research Council (MRC) scale for grading muscle strength: 3⫽moves against gravity but no resistance,
4⫽moves against resistance supplied by examiner, 5⫽normal. (Merck Manual 16th Ed. Rahway, NJ: Merke Research
Laboratories;1992:1433.)
Participants were then categorized into those with thorough and
complete adherence, and the effect of adherence levels on long-term
outcomes was investigated.
Statistical Analysis
Statistical analysis was performed with SPSS for Windows, Version
10.0.5. Categorical variables were analyzed using the ␹2 test.
ANOVA was used to compare age across care models. Logistic
regression was used to investigate the effect of model of care and
adherence to PoC on outcome measures, adjusted for case-mix.
Level of significance was P⬍0.05.
Adjustment for patient case-mix was undertaken using variables
identified as predictive of stroke outcome:14,15 age, premorbid
function, living alone, normal verbal Glasgow Coma Score, ability to
lift both arms, ability to walk alone, and urinary incontinence within
72 hours of stroke.14 Discrimination of this statistical model for
predicting independence at 28 weeks was greater than a model using
age, gender, and variables that differed significantly (Table 2)
between care modalities (areas under receiver operating characteristic curves 0.887 and 0.841, respectively).
Inter-rater reliability was assessed using the kappa (␬) statistic for
dichotomous variables and the weighted ␬ statistic for ordinal
variables.
4
Stroke
May 2004
Results
Hospital Sample
The 3 SU hospitals were tertiary teaching hospitals. The 3
mobile services and 2 conventional care hospitals were
smaller suburban centers. Various models of care could be
received within SU or mobile service hospitals, such as
mobile service care in SU hospital when beds were unavailable; hence, patients were categorized according to treatment
actually received.
Patient Sample
Number of applicable processes of care per patient.
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Of 1701 patients screened between September 1998 and
October 1999, 878 were ineligible and 468 patients provided
informed consent to participate (57%). The main causes of
ineligibility were TIA and nonstroke (45%). The SU hospitals
recruited significantly greater participants (SU 63%, mobile
service 56%, conventional care 43%, P⬍0.01). No statistically significant differences between participants and nonresponders for age and gender were detected. Further, the
proportions of stroke patients admitted with ischemic and
hemorrhagic stroke did not vary significantly across the
hospitals, suggesting similar stroke type presentations.
Key variables used for case-mix adjustment, subgroup
classification, or as PoC measures, were assessed for interrater reliability and demonstrated excellent agreement (␬
⬎0.8).
Outcome data were available on 96% of patients (survivors
n⫽357, deceased n⫽91). Nine participants (2%) withdrew
from the study and 11 (2%) were lost. Mean age was 73 years
(SD 14, range 19 to 102 years) (Table 2).
Of the 230 participants presenting to an SU hospital, 138
(60%) were admitted directly to the SU, 175 (76%) spent
some time in the SU, and 55 (24%) did not receive any SU
care. Of these, the SU team saw 52 as a mobile service. Of the
169 participants from mobile service hospitals, 12 received
conventional care. Conventional care hospitals recruited 69
participants.
Age, prestroke level of independence, history of atrial
fibrillation, Oxfordshire stroke subtype classification,16 and
admission muscle strength varied significantly across the care
models (Table 2). There were no significant differences in
occupational classification (used as a surrogate for socioeconomic status and education level) or length of stay for
patients experiencing no discharge delays (SU median 7 days,
quartiles 4, 9; mobile service median 7 days, quartiles 5, 9;
and conventional care 6 days, quartiles 4, 8). The variances
observed in the baseline characteristics atrial fibrillation and
stroke subtype when included in a multivariate model were
found to not influence the effect of model of care for
outcomes evaluated (discharged P⫽0.528; at 28 weeks: home
P⫽0.467, independent P⫽0.729, and alive P⫽0.563).
Most participants were discharged to a rehabilitation facility (43%) or home (32%); 10% went to an aged care facility
and 3% were transferred to another hospital for interim or
palliative care. At final follow-up, 62% were home, 16%
were in an aged care facility, 3% were in hospital, and 20%
had died.
Quality of Acute Care
More than half of the PoC were considered applicable in all
cases, with applicability ranging from 11% to 100%. Overall
adherence rates to PoC varied from 27% for “occupational
therapist within 24 hours” to 97% for “documentation of
premorbid function” (Table 1).
Completeness of Care and Stroke Service Model
Irrespective of the total number of applicable PoC, which
ranged from a minimum requirement of 9 to all 15, a
consistent level of adherence was observed in each model
(Figure). The overall adherence rate was significantly greater
in SUs, and a significantly greater proportion of SU participants had thorough and complete adherence than the other
models (Table 3).
TABLE 3. Patient Level Adherence to Applicable Processes of Care by
Stroke Service
Stroke Unit
(n⫽175)
Mobile Service
(n⫽209)
Conventional Care
(n⫽84)
P*
i/n
1491/2001
1559/2391
483/921
SU vs MS ⬍0.001
%
75
65
52
SU vs CC ⬍0.001
Overall adherence
Adherence category†
Thorough (n-I ⱕ1)
59 (34%)
27 (13%)
3 (4%)
⬍0.001
Complete (n-I ⱕ0)
19 (11%)
11 (5%)
0 (0%)
0.003
n indicates number of applicable PoC; i, number of PoC adhered to; MS, mobile service; CC,
conventional care.
*Statistical comparisons by ␹2 tests.
†For definitions of thorough and complete adherence, see Definitions in text.
Cadilhac et al
Evaluation of Stroke Services in Australia: SCOPES
5
TABLE 4. Association Between Level of Adherence to PoC Variables and Outcomes Adjusted
for Case-Mix
Adherence
Thorough (n⫽89) (n-iⱕ1)
Complete (n⫽30) (n-i⫽0)
Outcome
n
AR Dif (%)
aOR
95% CI
P*
Discharged§
415
9.8
3.63
1.04–12.66
0.043
Independent at 28 wk†
184
14.0
1.78
0.93–3.38
0.080
Home at 28 wk‡
280
13.6
1.69
0.86–3.32
0.130
Alive at 28 wk§
377
11.5
2.10
0.92–4.82
0.080
Discharged§
415
8.6
3.40
0.41–28.28
0.258
Independent at 28 wk†
184
21.1
2.61
0.96–7.10
0.061
Home at 28 wk‡
280
19.0
3.09
0.96–9.87
0.058
Alive at 28 wk§
377
19.6
3.22
0.66–15.86
0.150
*P values obtained from logistic regression models.
†N of cases included in analysis 434.
‡N of cases included in analysis 455.
§Case-mix variables exclude ability to walk on admission because of computational difficulties with empty cells.
AR Dif indicates absolute risk difference; aOR, odds ratio adjusted for case-mix; n, number of applicable PoC; i,
number of PoC adhered to.
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Completeness of Care and Patient Outcome
Adjusting for case-mix, the odds of being alive at discharge
for participants with thorough adherence (n⫽89, 19%) was
significantly higher. No significant differences in longer-term
outcomes between the 2 groups for either adherence category
was found, although there were trends suggesting improved
outcomes. The absolute differences in outcomes for those that
did or did not have thorough or complete adherence ranged
from 8.6% to 19% (Table 4).
Model of Care and Explanatory Variables
Clinical management plans detail the activities of care over
time. Of the 8 hospitals, 2 had established use of clinical
management plans (SU⫽1, MS⫽1), 3 hospitals were in
various trial phases (MS⫽1, CC⫽2), and 2 hospitals did not
use them (SU⫽1, MS⫽1). Clinical management plans were
more likely to be used (SU 81 [46%], mobile service 87
[42%], conventional care 9 [11%]; P⬍0.001) and completed
in SU participants (SU 55 [68%], mobile service 43 [49%],
conventional care 1 [11%]; P⫽0.001). The managing physician was more likely to be a stroke specialist (neurologist) in
the SU (P⬍0.001). There was no significant difference
between patients receiving palliative care (SU 8%, mobile
service 12%, and conventional care 6%; P⫽0.161). Patients
treated by a mobile service were less likely to be transferred
to another ward during their acute hospitalization. The main
reasons for ward transfers were to receive care in a SU,
coronary care ward, intensive care unit, general medical
ward, or an on-site rehabilitation ward.
Univariate analysis demonstrated significant differences in
types of diagnostic tests undertaken by participants treated in
SU and mobile service compared with conventional care.
Conventional care patients were less likely to have a transesophageal echocardiogram (P⫽0.043), undergo a carotid
ultrasound (P⫽0.001), cerebral or carotid angiogram
(P⬍0.001), transcranial Doppler ultrasound (P⬍0.001), or
magnetic resonance imaging (P⬍0.001).
Discussion
We have demonstrated that adherence to high-priority PoC
was associated with improved mortality at discharge and
trends found toward independence at home. These management practices are more consistently delivered in a geographically localized SU setting. Overlapping care models existed
within some participant hospitals. An intention to treat
analysis in this evaluation would be expected to underestimate the true effect of the intervention (model of care). The
models of care compared were representative of those in
nonparticipating metropolitan hospitals.
Important trends emerged, suggesting improved patient
outcomes at 28 weeks with complete adherence to PoC. A
⬎3-fold increase in odds of being discharged from acute care
was found if all applicable PoC or all except 1 (thorough
adherence) were undertaken by health professionals.
Because PoCs were completed more often in SUs, these
findings may explain why SUs achieve better outcomes
compared with other models. Other studies have also shown
that SUs have greater adherence to aspects of evidence-based
investigation and treatment.9,17,18 We acknowledge that the
overall effectiveness of SU care may be influenced by
intangible factors such as greater enthusiasm and specialization of staff, which cannot be attributed completely by the
sum of individual PoC. The categorization of SU care was
based on a conservative premise that any care in the SU
should result in patient benefits,11 potentially underestimating
benefits observed.
Seven of the 15 PoC had adherence rates ⬍65%, indicating
the capacity for improvement. Some of the parameters were
more complex because they required the availability of allied
health staff or had time restrictions. Improvement in adherence is possible regardless of the model of care. A systematic
method for measuring clinical practice for stroke is needed to
ensure that optimal care is delivered.17
The finding that 24% of patients admitted to SU hospitals
received no care in the SU raises issues with respect to equity
of care and capacity to meet demand. Limited diagnostic
services at conventional care hospitals may have influenced
the reduced investigations performed at these hospitals.
Because proportions of patients receiving palliative care were
similar across models, this could not be attributed to the PoC
adherence rates.
6
Stroke
May 2004
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Differences in patient case-mix between models were
appropriately adjusted for in the analysis and can be explained, in part, by referral and response bias. Our response
rate is in line with similar studies examining PoC.19 The
SCOPES cohort may not be representative of the entire stroke
population but contained sufficient breadth to be able to
examine quality of care, particularly because process indicators are often universally applicable irrespective of stroke
type or severity. The higher refusal rate in conventional care
hospitals was possibly influenced by these patients tending to
be older and more dependent before their stroke. Studies that
tend to include mild/moderate strokes have higher response
rates.5 A randomized, controlled trial was not possible because of the potential for contamination and crossovers of the
control group within an SU or mobile service hospital,
although perhaps studies of particular components of care
could be undertaken. In addition, the size of SU hospitals may
have influenced PoC adherence because of the availability of
resources and, coincident with higher recruitment rates,
thorough and complete care rates may have been
overestimated.
Several potential sources of reporting bias arise in this
study, such as use of telephone follow-up surveys or abstracting PoC from medical records, which may have been undertaken but not routinely recorded or required subjective
judgements. Criteria to select reliable PoC for the analysis
were used. In addition, SUs may be more adept at documenting PoC given their specialized focus and better use of
clinical management tools, although the spread of use of these
tools across models should have limited this form of bias.
Abstracting PoC from medical records may underestimate
rates up to 10%.19
Although not a primary hypothesis, we did not demonstrate
significant differences in long-term outcomes associated with
PoC adherence. This may reflect a lack of statistical power,
because the number of participants receiving thorough or
complete adherence was small. Nonetheless, important trends
were demonstrated and a 50% improvement in independence
at 28 weeks with complete adherence compared with thorough adherence was observed.
This study demonstrates aspects of care that may be
important in determining outcome and further highlights the
better clinical practices of SUs. Establishing key factors that
contribute to optimal management in SUs provides additional
incentive to increase equitable access to dedicated SUs in
Australia and elsewhere.
Acknowledgments
The Department of Human Services Victoria, the Ian Potter Foundation, and the National Stroke Foundation of Australia supported
this study. We thank Maria Di Pietro, Michelle Fox, Tamara
Clements, Jason Faux, and Karen Martin for their contribution as
research officers. We also thank Sonia Morrissey and Penny Bisset
for their contribution to database management. In addition, we thank
Franca Smarrelli, previously of the National Stroke Foundation of
Australia, Professor Shah Ebrahim (UK), and Professor Peter Langhorne (UK) for their support and contribution to this research.
Finally, we thank our consumer representative Gillian Simons,
President of Stroke Association of Victoria.
Authors Donnan and Davis are the heads of dedicated stroke units
in their respective hospitals. They were not directly involved in the
collection of data or analysis of the results.
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Multicenter Comparison of Processes of Care Between Stroke Units and Conventional
Care Wards in Australia
Dominique A. Cadilhac, Joeseph Ibrahim, Dora C. Pearce, Kathryn J. Ogden, John McNeill,
Stephen M. Davis and Geoffrey A. Donnan
for the SCOPES Study Group
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