Effect of a Cognitive Aid on Adherence to Perioperative Assessment and Management Guidelines for the Cardiac Evaluation of Noncardiac Surgical Patients

Article (PDF Available)inAnesthesiology 59(1) · April 2014with347 Reads
DOI: 10.1097/ALN.0000000000000251 · Source: PubMed
Abstract
The 2007 American College of Cardiologists/American Heart Association Guidelines on Perioperative Cardiac Evaluation and Care for Noncardiac Surgery is the standard for perioperative cardiac evaluation. Recent work has shown that residents and anesthesiologists do not apply these guidelines when tested. This research hypothesized that a decision support tool would improve adherence to this consensus guideline. Anesthesiology residents at four training programs participated in an unblended, prospective, randomized, cross-over trial in which they completed two tests covering clinical scenarios. One quiz was completed from memory and one with the aid of an electronic decision support tool. Performance was evaluated by overall score (% correct), number of incorrect answers with possibly increased cost or risk of care, and the amount of time required to complete the quizzes both with and without the cognitive aid. The primary outcome was the proportion of correct responses attributable to the use of the decision support tool. All anesthesiology residents at four institutions were recruited and 111 residents participated. Use of the decision support tool resulted in a 25% improvement in adherence to guidelines compared with memory alone (P < 0.0001), and participants made 77% fewer incorrect responses that would have resulted in increased costs. Use of the tool was associated with a 3.4-min increase in time to complete the test (P < 0.001). Use of an electronic decision support tool significantly improved adherence to the guidelines as compared with memory alone. The decision support tool also prevented inappropriate management steps possibly associated with increased healthcare costs.

Figures

Anesthesiology, V 120 • No 6 1339 June 2014
P
OSTOPERATIVE cardiac complications are a major
source of morbidity, mortality, and cost in the periop-
erative period.
1
To preserve patient safety and reduce unnec-
essary testing, the American College of Cardiology (ACC)
and the American Heart Association (AHA) have published
consensus guidelines for preoperative cardiac evaluation and
management of noncardiac surgical patients.
2
e main pur-
pose of these guidelines is to aid clinicians in performing risk
stratification and appropriate cardiac evaluation of patients
having intermediate to high-risk noncardiac surgery.
Appropriately applied, these guidelines are intended
to accomplish several goals. First, the guidelines identify
patients needing additional preoperative assessment via
diagnostic tests and imaging techniques (e.g., stress test or
echocardiogram). Second, the guidelines identify patients
who may benefit from the institution or continuance of pre-
operative pharmacologic management, such as β-blockers
for targeted perioperative heart rate control.
3
ird, the
guidelines may facilitate the informed consent process, help-
ing physicians quantify perioperative risk. Taken together,
these guidelines are intended to improve patient safety and
use of resources.
Although these guidelines have been accepted as stan-
dard practice parameters for preoperative cardiac evalua-
tion by the American Society of Anesthesiologists, recent
What We Already Know about This Topic
• Guidelinesforperioperativecardiacevaluationfornoncardiac
surgeryareinconsistentlyapplied
What This Article Tells Us That Is New
• The investigators conducted a randomized trial of an elec-
tronicdecisionsupporttoolamongresidentsatfourtraining
programs
• Useofthetoolmarkedlyimprovedadherencetotheguide-
linescomparedwithmemoryalone
Copyright © 2014, the American Society of Anesthesiologists, Inc. Lippincott Williams & Wilkins. Anesthesiology 2014; 120:1339-53
ABSTRACT
Background: e 2007 American College of Cardiologists/American Heart Association Guidelines on Perioperative Cardiac
Evaluation and Care for Noncardiac Surgery is the standard for perioperative cardiac evaluation. Recent work has shown that
residents and anesthesiologists do not apply these guidelines when tested. is research hypothesized that a decision support
tool would improve adherence to this consensus guideline.
Methods: Anesthesiology residents at four training programs participated in an unblinded, prospective, randomized,
cross-over trial in which they completed two tests covering clinical scenarios. One quiz was completed from memory and
one with the aid of an electronic decision support tool. Performance was evaluated by overall score (% correct), number of
incorrect answers with possibly increased cost or risk of care, and the amount of time required to complete the quizzes both
with and without the cognitive aid. e primary outcome was the proportion of correct responses attributable to the use of
the decision support tool.
Results: All anesthesiology residents at four institutions were recruited and 111 residents participated. Use of the decision
support tool resulted in a 25% improvement in adherence to guidelines compared with memory alone (P < 0.0001), and
participants made 77% fewer incorrect responses that would have resulted in increased costs. Use of the tool was associated
with a 3.4-min increase in time to complete the test (P < 0.001).
Conclusions: Use of an electronic decision support tool significantly improved adherence to the guidelines as compared with
memory alone. e decision support tool also prevented inappropriate management steps possibly associated with increased
healthcare costs. ( Anesthesiology 2014; 120:1339-53)
This article is featured in “This Month in Anesthesiology,” page 1A. Corresponding article on page 1309.
Submitted for publication July 21, 2013. Accepted for publication January 7, 2014. From the Department of Anesthesia and Perioperative
Medicine (W.R.H., K.H.B.) and Department of Public Health Sciences (P.J.N.), Medical University of South Carolina, Charleston, South Caro-
lina; Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (M.P.S.); Department of Anesthe-
siology, University of Kentucky, Lexington, Kentucky (R.M.S., A.N.D.); Departments of Anesthesiology, Surgery, and Biomedical Informatics
(J.M.F.) and Department of Anesthesiology (M.D.M.), Vanderbilt University Medical Center, Nashville, Tennessee.
Effect of a Cognitive Aid on Adherence to Perioperative
Assessment and Management Guidelines for the
Cardiac Evaluation of Noncardiac Surgical Patients
WilliamR.Hand,M.D.,KathrynH.Bridges,M.D.,MarjorieP.Stiegler,M.D.,
RandallM.Schell,M.D.,MACM,AmyN.DiLorenzo,M.A.,JesseM.Ehrenfeld,M.D.,M.P.H.,
PaulJ.Nietert,Ph.D.,MatthewD.McEvoy,M.D.
Anesthesiology 2014; 120:1339-53 1340 Hand et al.
Decision Support for Perioperative Assessment
studies have demonstrated that anesthesiologists and anes-
thesiology residents often fail to follow these guidelines
when assessed with multiple-choice questions (MCQs).
4,5
Reasons for poor adherence to established guidelines seem
to be multifactorial, with several general categories of bar-
riers having been identified.
6
Barriers include inadequate
physician knowledge (lack of familiarity), physician atti-
tudes about the guidelines (lack of efficacy, outcome expec-
tancy, agreement, or motivation due to previous practice),
and behavior (communication issues between patient and
practitioner, characteristics of each guideline, and envi-
ronmental factors effecting the marginal effort to follow
guidelines).
6
e authors of the two previous studies in this
specific area recommended evaluation of decision support
tools (DSTs) as a future direction to improve adherence to
published guidelines.
4,5
Decision support tools often improve adherence to pub-
lished guidelines and in many instances improve clinical
outcomes although this has not been universally true.
7–25
However, most research on clinical decision support has
focused on patient management after initial assessment and
diagnosis or through mandatory alerts for providers, such
as reminders for intraoperative antibiotic prophylaxis, post-
operative nausea and vomiting prophylaxis, or proper use
of alarms for separation from cardiopulmonary bypass.
26–34
In contrast, the use of DSTs for patient assessment or diag-
nosis and the subsequent application of evidence-based
protocols have yet to be rigorously tested.
26
Furthermore,
we are unaware of any studies that have investigated the
effect of DSTs on adherence to guidelines for the preop-
erative assessment and planning for noncardiac surgical
patients. Accordingly, we tested the hypothesis that an
electronic DST would improve assessment of patient status
and subsequent adherence to the ACC/AHA 2007 guide-
lines on perioperative cardiovascular evaluation and care
for noncardiac surgery among anesthesiology residents as
compared with memory alone.
Materials and Methods
Four anesthesiology training programs participated in this
prospective randomized trial with cross-over design which
was completed between February and May 2013. After
investigational review board approval was granted at all
institutions (Charleston, South Carolina; Nashville, Tennes-
see; Lexington, Kentucky; Chapel Hill, North Carolina), all
residents (total 211) were recruited via electronic communi-
cation, with 111 (52.6%) self-selecting to consent to partici-
pate (fig. 1). As no baseline performance measure was known
for the study population, an a priori power analysis was not
performed. e recruited sample size (n = 111) provided
80% power to detect very subtle increases (approximately
0.3 SD units) in the primary outcome, the proportion of
correct responses attributable to the use of the cognitive
aid, assuming two-sided hypothesis testing and an α level of
0.05. Secondary outcomes, described in the last paragraph
of the Materials and Methods section, included the per-
formance of test-takers related to the types of errors and
efficiency with which they completed the tests. Multi-site
compliance and logistics were regulated in the Department
of Anesthesia and Perioperative Medicine at the Medical
University of South Carolina (MUSC). Randomization was
done using computer-generated number generation and was
assigned by MUSC as described below. e programs that
participated in the study were MUSC (Charleston, South
Carolina), Vanderbilt University Medical Center (VUMC,
Nashville, Tennessee), University of North Carolina (Chapel
Hill, North Carolina), and University of Kentucky (Lexing-
ton, Kentucky).
Demographic information of participants including year
in training, sex, and race was collected. Participants were
then randomly assigned to groups A to D by a statistician
with no personal experience with any of the participants.
Group assignment A to D determined the order of quiz-
zes and the availability of the DST for each quiz (fig. 1).
Differences between group demographics were assessed via
chi-square tests.
e DST containing the AHA/ACC evaluation and
management guidelines was presented in electronic form
on either an Apple iPad or iPhone (Apple, Inc., Cuper-
tino, CA). A subset of the authors (M.D.M. and W.R.H.)
designed the logic of the DST based directly on the AHA/
ACC guidelines. An iOS programmer created the executable
application. Various screenshots of the DST can be seen in
appendix 1, which demonstrate how a user would navigate
the ACC/AHA guidelines using the DST. Participants had
no previous exposure or formal training to the DST before
this study. e DST was distributed electronically to partici-
pants on the day of testing and the software expired immedi-
ately after use, so that participants tested on subsequent days
would not have confounding access to the DST before their
testing session.
A single, optional, online didactic lecture provided a
short introduction to the ACC/AHA perioperative car-
diac guidelines and an introduction to the DST. e same
online training was used at all study sites. is didactic
Fig. 1. Groups A–D were randomly assigned to complete ei-
ther quiz 1 or 2 rst with or without the decision support tool
(DST). Participants in each group subsequently completed
the remaining quiz with or without the DST to ensure both
quizzes and both testing conditions were evaluable. *One
resident from Group C was paged for an emergency during
testing. His incomplete results were removed from analysis.
Anesthesiology 2014; 120:1339-53 1341 Hand et al.
PERIOPERATIVE MEDICINE
was intentionally brief (22 min), as its goal was orienta-
tion to the use of the DST rather than extensive education
and review of the ACC/AHA guidelines. e goal of keep-
ing this brief was to test the true capacity of residents to
apply guidelines based on their existing functional knowl-
edge of these published guidelines, rather than testing
them immediately after an extensive review, as was used
in two previous studies.
4,5
Orientation was performed a
minimum of 2 weeks before implementation of the cog-
nitive aid at all sites to prevent score inflation associated
with very recent exposure. Testing was performed with
local participation in a standardized (classroom) environ-
ment, which included supervision from trained study per-
sonnel who administered the test and gave access to the
DST according to group assignment. All participants were
excused from active clinical duties and asked to silence all
pagers except hospital-wide mayday pagers when appro-
priate. Testing of participants was accomplished within a
10-day period at all sites.
To build on the recent works by Vigoda et al.,
4,5
MCQ
stems were created with a correct answer representing
each evaluation path and terminal end point of the ACC/
AHA perioperative cardiac evaluation algorithm. Although
there is no defined guideline for the design and assess-
ment of MCQs, our process was similar to that previously
described.
4,5
e clinical stems and corresponding MCQ
answer options were assessed for construct and content
validity by evaluating whether they actually represented an
adequate test of knowledge of the ACC/AHA guidelines
under consideration, both in the specifics of each question
being asked and in the scope of the questions as a whole
concerning the information contained in the guidelines.
is was done by having a cohort of anesthesiology faculty
from three institutions who are actively involved in preop-
erative assessment take the quizzes and assess the manner in
which the content of the quiz reflected the content of the
guidelines and adequately tested the scope of knowledge
represented in the guidelines. is procedure followed a
modified Delphi technique method, and as such the quiz-
zes went through several iterative changes that incorpo-
rated faculty input until no further improvements to the
questions were suggested. As the grading of the quiz was
objective with correct answers agreed upon by the group
of faculty that evaluated the quiz, there was no reliability
assessment of grading. ere was also no reliability mea-
sure of participant performance as each quiz was only taken
once and under different testing conditions (i.e., memory
alone vs. cognitive aid). e stems and MCQs can be
reviewed in appendix 2.
e response options were also categorized into four
groups: correct, incorrect and associated with increased risk
to the patient, incorrect and associated with increased cost
to the patient or medical system, and incorrect and associ-
ated with both increased risk and cost. e categorization
of incorrect responses was created by two authors (W.R.H.
and M.D.M.) and subsequently validated by the remaining
authors. Categorization was based on the intervention each
answer option would require (i.e., obtaining an unneces-
sary electrocardiogram posed no risk to a patient needing
nonemergent surgery but would increase cost and there-
fore would be categorized as incorrect and associated with
increased cost to the patient or medical system). Participants
were instructed to select the single best answer for each ques-
tion from memory or as directed by the DST depending on
the testing condition (with or without DST). e answers
given to each item were recorded in a database along with
the duration required to complete each nine-question quiz.
Statistical Analyses
Generalized linear mixed models (GLMMs) were used to
evaluate the data because participants took two separate
quizzes under two testing environments (with and without
DST).
35
e GLMMs were used to determine the indepen-
dent effect of the use of the DST on overall scores, on indi-
vidual quiz item scores, and on the duration of time spent
taking the quizzes. ese models adjusted for the potential
influence of the specific quiz being administered (quiz 1
vs. 2), the order in which the quiz was taken (first vs. sec-
ond), the presence or absence of the DST, and the study
site (MUSC, University of North Carolina, University of
Kentucky, or VUMC). By including these covariates, the
GLMMs provided the opportunity for us to conduct F tests
on each of these, to determine whether any of them had any
independent association with the participants’ scores and
quiz durations, and they provided, via the intraclass correla-
tion coefficient, a means of estimating the reliability between
subjects’ scores on the two quizzes. Interaction terms
between the use of the DST and quiz order were assessed
and included in the final models when they were moderately
significant (P < 0.10) because of the potential for the DST to
be differentially beneficial when taking the first versus second
quiz (i.e., to account for a possible learning effect from use of
the DST first). Overall quiz scores, durations, and number
of incorrect responses that would result in increased health-
care costs were treated as continuous variables with a linear
link function in their GLMM, whereas individual quiz item
scores were treated as binary variables with logit link func-
tions in their GLMM. Random participant effects were used
in the GLMMs to account for correlation between scores
within the same participants. For continuous measures,
adjusted mean scores (i.e., scores that adjusted for order,
site, and quiz effects) were estimated along with their 95%
CIs using the GLMMs. For binary responses, the use of the
DST was quantified using adjusted probabilities of correct
responses, along with odds ratios and their 95% CIs. For
each quiz, we also investigated internal consistency using
Cronbachs alpha. Analyses were conducted using SAS v9.3
(SAS Institute Inc., Cary, NC) Proc MIXED, Proc GLIM-
MIX, and the Proc CORR procedures, and P values less than
0.05 were considered statistically significant.
Anesthesiology 2014; 120:1339-53 1342 Hand et al.
Decision Support for Perioperative Assessment
Results
A total of 111 residents (of 211 recruited) participated among
the four sites (MUSC 37, University of Kentucky 33, Uni-
versity of North Carolina 25, and VUMC 16). One partici-
pant at MUSC consented to participate and finished 5 of 18
questions before being paged for an emergent intubation; his
results were removed from the dataset. All datasets were com-
plete with no ambiguity in answer selection or other potential
source of error apparent to the authors. Of the 111 residents
participating, 36 (32%) were postgraduate year (PGY) 4 or
higher, 29 (26%) were PGY-3, 30 (27%) were PGY-2, and
16 (14%) were PGY-1 or interns (table 1 for demographic
breakdown by group). Based on sex, race (Caucasian vs. non-
Caucasian), or PGY, there were no significant differences
between the four groups. Cronbachs alpha coefficients were
calculated to be 0.66 for quiz 1 and 0.65 for quiz 2, indicat-
ing a moderate level of internal consistency for each.
Table 2 summarizes the findings of the GLMM for par-
ticipants’ overall scores. e intraclass correlation coefficient
was estimated to be 0.26, indicating that subjects’ scores on
quiz 1 tended to be moderately correlated with their scores
on quiz 2. e scores on the quizzes (quiz 1 and 2) were
comparable (P > 0.05), validating that there was not a signif-
icant difference in difficulty of the quiz. However, there was
a clear order effect, meaning that subjects tended to score
better on the second quiz when compared with the first,
regardless of whether it was quiz 1 or 2. e results indicated
that the DST had a statistically significant impact on both
the first (P < 0.0001) and second (P < 0.0001) quiz scores,
and the DST benefit was significantly more pronounced (P
< 0.01) when used during the first quiz as compared with
being used during the second quiz, as seen in table 2. On
average, the DST resulted in participants scoring 2.9 points
higher when the DST was used during their first quiz and
1.6 points higher when used during their second quiz, which
represents an absolute improvement in performance of 32
and 18%, respectively, and a relative improvement of 60.4
and 23.9%, respectively. e DST by quiz interaction was
not significant, providing some evidence of the DST’s gen-
eralizability. ere was no significant (P = 0.98) difference
in scores across the four sites (table 2). PGY also had no
significant effect on performance, except that subjects with
more years of training were significantly less likely (P = 0.04)
to have an incorrect response that would increase risk to the
patient. e odds of an incorrect response that increased risk
to the patient declined by 31% with each additional year of
training (odds ratio, 0.69; 95% CI, 0.48 to 0.99).
Table 3 summarizes the findings of the GLMM assess-
ing the impact of the factors associated with the duration
of time it took participants to take their quizzes. Durations
were comparable on the two quizzes, and participants’ sec-
ond quizzes took less time, on average, than their first quiz-
zes. Use of the cognitive aid did increase the quiz duration
on both their first (P < 0.0001) and second (P < 0.001) quiz-
zes, with the added duration being moderately longer on the
first quiz compared with their second (4.4 vs. 2.4 added min-
utes, P = 0.06). ere were differences in the quiz durations
by site, with participants taking longer time at University of
Kentucky and MUSC (10.9 and 11.0 min) than at Univer-
sity of North Carolina and VUMC (9.0 min for both).
Table 4 summarizes the findings of the GLMMs assessing
the impact of the cognitive aid on each of the individual quiz
items. e cognitive aid was associated with a statistically
significant improvement of correct responses for scenario
question numbers 1, 3, 4, 7, 8, and 9, even after adjusting
for quiz type, quiz order, and study site.
Use of the DST also had statistically significant impact on
the number of incorrect quiz responses that were considered to
increase healthcare costs, but not on incorrect responses that
Table 1. Demographics
Group A B C D Total
N 31 26 26 28 111
Male 22 19 18 19 78 (70%)
Caucasian 27 21 22 25 95 (86%)
PGY-1 4 4 4 4 16 (14%)
PGY-2 9 6 7 8 30 (27%)
PGY-3 9 7 5 8 29 (26%)
PGY-4 9 9 10 8 36 (32%)
Demographics: the number of participants in groups A–D is shown (N). Further
detail includes the number of male, Caucasian, and PGY 1–4 participants.
N = number; PGY = postgraduate year.
Table 2. Results of the Generalized Linear Mixed Model
Analyses Assessing the Impact of Cognitive Aid on Participants’
Overall Scores
Effect
Adjusted
Mean
Score
(Number
Correct) 95% CI P Value‡
Quiz 1 6.9 6.7–7.2 0.74
Quiz 2 6.9 6.6–7.1
Site
UK 6.9 6.6–7.3 0.98
UNC 6.9 6.5–7.3
MUSC 6.8 6.5–7.2
VUMC 6.9 6.4–7.4
First quiz
Without cognitive aid 4.8 4.5–5.2
With cognitive aid 7.7 7.4–8.1
Impact of cognitive aid* 2.9† 2.4–3.4 <0.0001
Second quiz
Without cognitive aid 6.7 6.4–7.1
With cognitive aid 8.3 8.0–8.7
Impact of cognitive aid* 1.6† 1.1–2.1 <0.0001
* Impact of cognitive aid is dened as the difference between the scores
with and without the cognitive aid. † P < 0.01 comparing impact of cogni-
tive aid on rst quiz compared with impact of cognitive aid on second quiz.
‡ Obtained via F tests based on results from the generalized linear mixed
models.
MUSC = Medical University of South Carolina; UK = University of Ken-
tucky; UNC = University of North Carolina; VUMC = Vanderbilt University
Medical Center.
Anesthesiology 2014; 120:1339-53 1343 Hand et al.
PERIOPERATIVE MEDICINE
would increase risk to patients. Overall, there were 468 incor-
rect selections. Of the incorrect selections, 387 (83% of incor-
rect) were assigned to the “increased cost” category, 38 (8%)
to the “increased risk” category, and 43 (9%) to the “increased
cost and risk” category. Table 5 highlights the fact that the
impact of the DST on quiz items healthcare costs was statisti-
cally significant (P < 0.0001) for both quizzes. Interestingly,
the impact was greater if the DST was used during the partici-
pants’ first quiz when compared with their second (P < 0.001).
Discussion
Our results demonstrate three important findings. First, the
use of an electronic DST increased adherence to the 2007
ACC/AHA guidelines. Second, use of the electronic DST
reduced management errors associated with increased costs,
such as ordering unnecessary tests and consults. ird, use
of the DST resulted in a longer time to complete the test.
e electronic, smart-phone–based DST improved abso-
lute performance, as measured by adherence to the ACC/
AHA preoperative assessment algorithm, by an average of
25% as compared with performance from memory alone.
is result was consistent across residents at all years of
training at four different institutions. In specific, the DST
provided a statistically significant benefit for six of the nine
MCQ scenarios (table 4). e recent works by Vigoda et al.
4,5
highlight the inability of residents and practicing anesthesi-
ologists to apply the ACC/AHA guidelines on perioperative
cardiovascular evaluation and care for noncardiac surgery.
Our results among residents taking the first test without the
assistance of DST correctly answered an average of 4.8/9
(53%) questions. Similar to Vigodas studies, our rate of
error beckons for a mechanism of improvement—and the
improvement is seen with our simple DST. Under the same
Table 3. Results of the Analyses Assessing the Impact of
Cognitive Aid on Participants’ Quiz Durations
Effect
Adjusted
Quiz
Duration
(in Minutes) 95% CI P Value†
Quiz 1 9.8 9.1–10.4 0.30
Quiz 2 10.2 9.5–10.8
Site
UK 10.9 10.0–11.8 <0.01
UNC 9.0 8.0–10.1
MUSC 11.0 10.1–11.9
VUMC 9.0 7.6–10.3
First quiz
Without cognitive aid 8.5 7.7–9.4
With cognitive aid 12.9 12.0–13.9
Impact of cognitive aid* 4.4 3.1–5.6 <0.0001
Second quiz
Without cognitive aid 8.0 7.1–8.9
With cognitive aid 10.4 9.6–11.3
Impact of cognitive aid* 2.4 1.2–3.7 <0.001
* Impact of cognitive aid is dened as the difference between the scores
with and without the cognitive aid. † Obtained via F tests based on results
from the generalized linear mixed models.
MUSC = Medical University of South Carolina; UK = University of Ken-
tucky; UNC = University of North Carolina; VUMC = Vanderbilt University
Medical Center.
Table 4. Results of GLMMs Assessing the Impact of the
Cognitive Aid on Individual Questions
Quiz Item Number
Adjusted*
Probability
of
Responding
Correctly,
%
Odds
Ratio
and
95% CI P Value†
1: Without cognitive aid 55.3 Reference
1: With cognitive aid 94.6 14.1 (6.6–30.2) <0.0001
2: Without cognitive aid 83.3 Reference
2: With cognitive aid 89.9 1.8 (0.9–3.6) 0.11
3: Without cognitive aid 64.0 Reference
3: With cognitive aid 93.3 7.8 (3.4–18.1) <0.0001
4: Without cognitive aid 66.4 Reference
4: With cognitive aid 92.2 6.0 (2.5–14.0) <0.0001
5: Without cognitive aid 89.3 Reference
5: With cognitive aid 93.8 1.8 (0.7–4.7) 0.20
6: Without cognitive aid 73.1‡ Reference
6: With cognitive aid 99.1‡ 38.8 (0.0–61,436) 0.33
7: Without cognitive aid 38.4 Reference
7: With cognitive aid 79.6 6.3 (3.8–10.3) <0.0001
8: Without cognitive aid 66.5 Reference
8: With cognitive aid 93.9 7.7 (2.7–22.1) <0.001
9: Without cognitive aid 48.0 Reference
9: With cognitive aid 88.4 8.3 (4.0–17.1) <0.0001
* Probability of correct responses was adjusted for quiz type (1 vs. 2), order
(rst vs. second), study site (University of Kentucky, University of North
Carolina, Medical University of South Carolina, or Vanderbilt), and partici-
pant effects using GLMMs. † Obtained via F tests based on results from
the GLMMs. ‡ On quiz item number 6 in quiz 2, all participants responded
correctly (with and without the cognitive aid), and this resulted in nones-
timable parameters within the GLMM; thus, the results only reect the
impact of the cognitive aid within quiz 1.
GLMM = generalized linear mixed model.
Table 5. Results of GLMMs Assessing the Impact of the
Cognitive Aid on Incorrect Responses That Would Result in
Increased Cost
Study Outcome
Adjusted*
Mean
(Number
Incorrect) 95% CI P Value†
Incorrect responses that increase costs
First quiz
Without cognitive aid 4.0 3.7–4.4
With cognitive aid 1.0 0.6–1.3
Impact of cognitive aid‡ −3.1 −3.6 to −2.6 <0.0001
Second quiz
Without cognitive aid 2.1 1.8–2.5
With cognitive aid 0.5 0.2–0.9
Impact of cognitive aid‡ −1.6 −2.1 to −1.1 <0.0001
* Mean number of incorrect responses were adjusted for quiz type (1 vs. 2),
order (rst vs. second), study site (University of Kentucky, University of
North Carolina, Medical University of South Carolina, or Vanderbilt), and
participant effects using GLMMs. † Obtained via F tests based on results
from the GLMMs. ‡ Impact of cognitive aid is dened as the difference
between the scores with and without the cognitive aid.
GLMM = generalized linear mixed model.
Anesthesiology 2014; 120:1339-53 1344 Hand et al.
Decision Support for Perioperative Assessment
first-quiz conditions, the DST group correctly answered
7.7/9 (86%) questions. e efficacy of the DST is consistent
with other treatment algorithms that have been assessed with
DSTs with excellent uptake and success.
19–24,36–38
Perhaps,
the reason DSTs were adopted for Advanced Cardiac Life
Support is related to the high-stakes and high-stress clinical
environments in which code scenarios usually occur, but this
research advocates the use of a DST in the calm and routine
preoperative care setting. e authors’ propose that better
adherence to published guidelines may, in fact, reduce the
number of high-stakes events being managed with a DST
due to higher quality care.
e DST provided statistically significant improvement
for six of the nine possible scenarios when evaluating perfor-
mance on both quiz 1 and 2. Scenario 6 did not reach statis-
tical significance because all respondents correctly answered
this question on quiz 2, creating nonestimable parameter for
the GLMM; we remain encouraged by the absolute improve-
ment despite the mathematical nuance created by the lack of
an estimable error rate. at said the composite percentages
of 73 versus 99% for testing without and with DST, respec-
tively, certainly seem to show benefits consistent with the
other scenarios showing statistically significant improvement.
For clinical scenarios number 2 and 5, statistical significance
was not reached despite the fact that the scores with DST
were among the highest for any scenario. We are encouraged
that the absolute percentages were higher with the DST and
believe with a large sample size we would have had the power
to demonstrate a statistically significant improvement. e
high scores for these scenarios in the memory-alone group
were similar to these in the DST group.
e second finding of interest is that 83% of the incorrect
options selected by participants would increase the cost of care
either to the patient directly or the medical system providing
care to the patient (e.g., requesting an unwarranted electrocar-
diogram or choosing to “delay until blood pressure controlled”).
During the design of this study, the authors associated each pos-
sible incorrect MCQ option with increasing the cost, risk, or
both cost and risk to the patient. Participants testing with or
without the DST did well avoiding selections that increased risk
to the patient; however, 83% of incorrect selections included
decisions to overtest or delay procedures at an increased cost
to the patient and healthcare system. When using the DST,
participants made 77% fewer incorrect responses that would
have resulted in increased costs compared with answering from
memory alone (3.1/4 for quiz 1 and 1.6/2.1 for quiz 2).
e third finding of interest is that participants required
an average of 3.4 min (204 s) longer to complete the quiz-
zes when using the DST. is equates to a mere 23 addi-
tional seconds per question (204 s ÷ 9 questions) required to
improve adherence to an important guideline in perioperative
medicine. Hospitals face the demand to deliver higher qual-
ity care with greater efficiency than ever before. However, we
posit that the investment of fractionally more time using a
DST would be more than offset by the correct application of
best-practice guideline. We also expect that efficiency using
this DST would improve with familiarity—as previously
described, the residents had almost no previous exposure to
the DST software to prevent any excessive learning effect that
would have confounded the “from memory” test scores. ese
23 s may be seen as an investment in quality as they portended
improved adherence to the guideline in question. In compari-
son, “the average surgical ‘time out’ at VUMC consumes 57
s (average of last 172,000 electronically observed time outs),
and the ‘time out’ has been credited with reducing surgical site
and procedure errors.” (Jesse Ehrenfeld, MD, Associate Pro-
fessor, Department of Anesthesiology, Vanderbilt University,
Nashville, Tennessee) Several studies have shown that devia-
tion from published guidelines increases morbidity and over-
all hospital cost. As any DST is considered for adoption, one
must recognize that implementation requires planning and
education as there is often hidden work associated with new
technologies.
39–41
With adequate education, orientation to a
DST, and rapid practitioner adoption, the authors believe the
benefits shown in this study could be magnified.
A limitation is that we evaluated performance of
anesthesiologists-in-training, not practicing physicians.
Improved performance by nonresidents cannot be inferred
from this research, despite similarly poor performance by prac-
ticing physicians demonstrated by Vigoda et al.
4,5
In addition,
although the results of this study seem generalizable among
several institutions, the generalizability of the use of an elec-
tronic DST beyond the scope of the ACC/AHA guidelines
cannot be made at this time. Future research needs to address
whether similar tools will be of benefit for other perioperative
management guidelines.
42–44
It would be intuitive to assume
that any such DST could improve adherence to guidelines,
but we and others have shown that simply having guidelines
in front of the clinician does not guarantee adherence.
9,45,46
Specific work is needed to investigate the human factors in
each application. In addition, the quizzes used in this study
were not created using rigorous psychometric methods; given
their moderate degree of consistency (Cronbachs alpha coef-
ficients), it is possible that the DST might perform differently
(better or worse) if different assessment tools were used.
We found that the use of the DST was associated with more
work per simulated patient encounter. Future research in this
domain of implementation science needs to address whether
this differential amount of time exists in simulated encounters
with human standardized patients and in clinical settings. Our
design could not determine the effect that order had beyond
a simple description of percent correct. For example, we were
unable to identify which type of errors might be prevented by
a test taker who did or did not have previous exposure to the
DST. We, therefore, are unable to make specific statements
about prospective benefits of DST use.
Our small sample size prevented us from making state-
ments about each question stem independently and resulted
in the conclusion that the DST improved performance on
only six of nine MCQ stems. Sample size was estimated based
Anesthesiology 2014; 120:1339-53 1345 Hand et al.
PERIOPERATIVE MEDICINE
on overall percent correct and an improvement with the DST.
We did not perform a reliability assessment (e.g., test–retest)
to test whether the grade achieved precisely represents a sta-
ble description of participant knowledge. Finally, we did not
achieve perfect adherence to and application of the AHA/
ACC guidelines under consideration, even though the DST
contained all of the proper logic for such application. is
failure is likely multifactorial: participant error, lack of atten-
tion, lack of knowledge (e.g., what constitutes an emergent
case), or lack of familiarity with the DST itself. e design of
the study prevented substantial practice navigating the DST
to maintain a valid baseline for scores without DST assistance.
In summary, we demonstrated that a simple DST improved
resident adherence to the ACC/AHA guidelines on periopera-
tive cardiovascular evaluation and care for noncardiac surgery
evaluated via MCQs with clinical stems. ere was a signifi-
cant improvement in overall performance of residents when
using the DST. Residents selected fewer incorrect options in
six of nine scenarios and primarily reduced incorrect selections
that would increase the cost of medical care. As anesthesiolo-
gists strive to practice evidence-based medicine, these results
indicate that it may be time to embrace decision support
technology during the preoperative assessment and planning
phase of patient care. Future research needs to address the
implementation of such tools in the clinical setting.
Acknowledgments
This study was supported by the Foundation for Anesthesia
Education and Research (Rochester, Minnesota), Research in
Education Grant (Principal Investigator: Dr. McEvoy). This
project was also supported by the South Carolina Clinical and
Translational Research Institute, Medical University of South
Carolina’s Clinical and Translational Institute (Charleston,
South Carolina); National Institutes of Health/National Cen-
ter for Advancing Translational Sciences (Bethesda, Maryland)
grant no. UL1TR000062; and National Center for Research Re-
sources award no. UL1RR029882 (Charleston, South Carolina).
Competing Interests
The authors declare no competing interests.
Correspondence
Address correspondence to Dr. Hand: Department of Anes-
thesia and Perioperative Medicine, Medical University of South
Carolina, 167 Ashley Avenue, Suite 301, Charleston, South Car-
olina 29425. handw@musc.edu. This article may be accessed
for personal use at no charge through the Journal Web site,
www.anesthesiology.org.
References
1. Fleischmann KE, Goldman L, Young B, Lee TH: Association
between cardiac and noncardiac complications in patients
undergoing noncardiac surgery: Outcomes and effects on
length of stay. AmJ Med 2003; 115:515–20
2. Fleisher LA, Beckman JA, Brown KA, Calkins H, Chaikof
EL, Chaikof E, Fleischmann KE, Freeman WK, Froehlich
JB, Kasper EK, Kersten JR, Riegel B, Robb JF, Smith SC Jr,
Jacobs AK, Adams CD, Anderson JL, Antman EM, Buller CE,
Creager MA, Ettinger SM, Faxon DP, Fuster V, Halperin JL,
Hiratzka LF, Hunt SA, Lytle BW, Nishimura R, Ornato JP, Page
RL, Riegel B, Tarkington LG, Yancy CW; American College
of Cardiology; American Heart Association Task Force on
Practice Guidelines (writing Committee to Revise the 2002
Guidelines on Perioperative Cardiovascular Evaluation for
Noncardiac Surgery); American Society of Echocardiography;
American Society of Nuclear Cardiology; Heart Rhythm
Society; Society of Cardiovascular Anesthesiologists; Society
for Cardiovascular Angiography and Interventions; Society for
Vascular Medicine and Biology; Society for Vascular Surgery:
ACC/AHA 2007 guidelines on perioperative cardiovascular
evaluation and care for noncardiac surgery: A report of the
American College of Cardiology/American Heart Association
Task Force on Practice Guidelines (Writing Committee to
Revise the 2002 Guidelines on Perioperative Cardiovascular
Evaluation for Noncardiac Surgery) developed in collabo-
ration with the American Society of Echocardiography,
American Society of Nuclear Cardiology, Heart Rhythm
Society, Society of Cardiovascular Anesthesiologists, Society
for Cardiovascular Angiography and Interventions, Society
for Vascular Medicine and Biology, and Society for Vascular
Surgery. JAm Coll Cardiol 2007; 50:e159–241
3. Fleischmann KE, Beckman JA, Buller CE, Calkins H, Fleisher
LA, Freeman WK, Froehlich JB, Kasper EK, Kersten JR, Robb
JF, Valentine RJ: 2009 ACCF/AHA focused update on peri-
operative β blockade: A report of the American college of
cardiology foundation/American heart association task force
on practice guidelines. Circulation 2009; 120:2123–51
4. Vigoda MM, Sweitzer B, Miljkovic N, Arheart KL, Messinger
S, Candiotti K, Lubarsky D: 2007 American College of
Cardiology/American Heart Association (ACC/AHA)
Guidelines on perioperative cardiac evaluation are usually
incorrectly applied by anesthesiology residents evaluating
simulated patients. Anesth Analg 2011; 112:940–9
5. Vigoda MM, Behrens V, Miljkovic N, Arheart KL, Lubarsky
DA, Dutton RP: Perioperative cardiac evaluation of simulated
patients by practicing anesthesiologists is not consistent with
2007 ACC/AHA guidelines. JClin Anesth 2012; 24:446–55
6. Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud
PA, Rubin HR: Why don’t physicians follow clinical prac-
tice guidelines? A framework for improvement. JAMA 1999;
282:1458–65
7. Sintchenko V, Coiera E, Iredell JR, Gilbert GL: Comparative
impact of guidelines, clinical data, and decision support on
prescribing decisions: An interactive web experiment with
simulated cases. JAm Med Inform Assoc 2004; 11:71–7
8. Litvin CB, Ornstein SM, Wessell AM, Nemeth LS, Nietert PJ:
Adoption of a clinical decision support system to promote
judicious use of antibiotics for acute respiratory infections in
primary care. IntJ Med Inform 2012; 81:521–6
9. Milani RV, Lavie CJ, Dornelles AC: The impact of achiev-
ing perfect care in acute coronary syndrome: The role of
computer assisted decision support. Am Heart J 2012;
164:29–34
10. Haut ER, Lau BD, Kraenzlin FS, Hobson DB, Kraus PS,
Carolan HT, Haider AH, Holzmueller CG, Efron DT, Pronovost
PJ, Streiff MB: Improved prophylaxis and decreased rates of
preventable harm with the use of a mandatory computer-
ized clinical decision support tool for prophylaxis for venous
thromboembolism in trauma. Arch Surg 2012; 147:901–7
11. Milani RV, Lavie CJ, Dornelles AC: The impact of achieving
perfect care in acute coronary syndrome: The role of com-
puter assisted decision support. AmHeart J 2012; 164:29–34
12. Dechant LM: UA/NSTEMI: Are you following the latest guide-
lines? Nursing 2012; 42:26–33; quiz 34
13. Lodewijckx C, Sermeus W, Vanhaecht K, Panella M,
Deneckere S, Leigheb F, Decramer M: Inhospital manage-
ment of COPD exacerbations: A systematic review of the lit-
erature with regard to adherence to international guidelines.
JEval Clin Pract 2009; 15:1101–10
Anesthesiology 2014; 120:1339-53 1346 Hand et al.
Decision Support for Perioperative Assessment
14. Loomba RS, Arora R: ST elevation myocardial infarction
guidelines today: A systematic review exploring updated
ACC/AHA STEMI guidelines and their applications. Am J
Ther 2009; 16:e7–13
15. O’Donnell C, Verbeek R: Guidelines for STEMI. CMAJ 2005;
172:1425–6; author reply 1426
16. Sykes PK: Prevention and management of postoperative
delirium among older patients on an orthopedic surgical
unit: A best practice implementation project. J Nurs Care
Qual 2012; 27:146–53
17. Asche CV, Leader S, Plauschinat C, Raparla S, Yan M, Ye
X, Young D: Adherence to current guidelines for chronic
obstructive pulmonary disease (COPD) among patients
treated with combination of long-acting bronchodilators or
inhaled corticosteroids. Int J Chron Obstruct Pulmon Dis
2012; 7:201–9
18. Calvin JE, Shanbhag S, Avery E, Kane J, Richardson D, Powell
L: Adherence to evidence-based guidelines for heart failure
in physicians and their patients: Lessons from the Heart
Failure Adherence Retention Trial (HART). Congest Heart
Fail 2012; 18:73–8
19. Haut ER, Lau BD, Kraenzlin FS, Hobson DB, Kraus PS,
Carolan HT, Haider AH, Holzmueller CG, Efron DT, Pronovost
PJ, Streiff MB: Improved prophylaxis and decreased rates
of preventable harm with the use of a mandatory com-
puterized clinical decision support tool for prophylaxis
for venous thromboembolism in trauma. Arch Surg 2012;
147:901–7
20. Kalla K, Christ G, Karnik R, Malzer R, Norman G, Prachar
H, Schreiber W, Unger G, Glogar HD, Kaff A, Laggner AN,
Maurer G, Mlczoch J, Slany J, Weber HS, Huber K; Vienna
STEMI Registry Group: Implementation of guidelines
improves the standard of care: The Viennese registry on
reperfusion strategies in ST-elevation myocardial infarction
(Vienna STEMI registry). Circulation 2006; 113:2398–405
21. Mangin D: Adherence to evidence-based guidelines is the key
to improved health outcomes for general practice patients:
NO. JPrim Health Care 2012; 4:158–60
22. Vause J: Adherence to evidence-based guidelines is the key
to improved health outcomes for general practice patients:
YES. JPrim Health Care 2012; 4:156–8
23. Okelo SO, Butz AM, Sharma R, Diette GB, Pitts SI, King TM,
Linn ST, Reuben M, Chelladurai Y, Robinson KA: Interventions
to modify health care provider adherence to asthma guide-
lines: A systematic review. Pediatrics 2013; 132:517–34
24. McGinn TG, McCullagh L, Kannry J, Knaus M, Sofianou A,
Wisnivesky JP, Mann DM: Efficacy of an evidence-based clini-
cal decision support in primary care practices: A randomized
clinical trial. JAMA Intern Med 2013; 173:1584–91
25. Flynn D, Ford GA, Stobbart L, Rodgers H, Murtagh MJ,
Thomson RG: A review of decision support, risk communica-
tion and patient information tools for thrombolytic treatment
in acute stroke: Lessons for tool developers. BMC Health
Serv Res 2013; 13:225
26. El-Kareh R, Hasan O, Schiff GD: Use of health information
technology to reduce diagnostic errors. BMJQual Saf 2013;
22(suppl 2):ii40–51
27. Wanderer JP, Ehrenfeld JM: Clinical decision support for
perioperative information management systems. Semin
Cardiothorac Vasc Anesth 2013; 17:288–93
28. Wanderer JP, Sandberg WS, Ehrenfeld JM: Real-time alerts
and reminders using information systems. Anesthesiol Clin
2011; 29:389–96
29. St Jacques P, Sanders N, Patel N, Talbot TR, Deshpande JK,
Higgins M: Improving timely surgical antibiotic prophylaxis
redosing administration using computerized record prompts.
Surg Infect (Larchmt) 2005; 6:215–21
30. O’Reilly M, Talsma A, VanRiper S, Kheterpal S, Burney R:
An anesthesia information system designed to provide
physician-specific feedback improves timely administration
of prophylactic antibiotics. Anesth Analg 2006; 103:908–12
31. Wax DB, Beilin Y, Levin M, Chadha N, Krol M, Reich DL:
The effect of an interactive visual reminder in an anesthesia
information management system on timeliness of prophylac-
tic antibiotic administration. Anesth Analg 2007; 104:1462–6
32. Kooij FO, Klok T, Hollmann MW, Kal JE: Decision support
increases guideline adherence for prescribing postopera-
tive nausea and vomiting prophylaxis. Anesth Analg 2008;
106:893–8
33. Eden A, Pizov R, Toderis L, Kantor G, Perel A: The impact
of an electronic reminder on the use of alarms after sepa-
ration from cardiopulmonary bypass. Anesth Analg 2009;
108:1203–8
34. Zilberberg MD, Chaudhari P, Nathanson BH, Campbell RS,
Emons MF, Fiske S, Hays HD, Shorr AF: Development and
validation of a bedside risk score for MRSA among patients
hospitalized with complicated skin and skin structure infec-
tions. BMCInfect Dis 2012; 12:154
35. McCulloch C, Searle SR: Generalized, Linear, and Mixed
Models. New York, John Wiley & Sons, 2001, pp 28–57
36. Haynes AB, Weiser TG, Berry WR, Lipsitz SR, Breizat AH,
Dellinger EP, Herbosa T, Joseph S, Kibatala PL, Lapitan MC,
Merry AF, Moorthy K, Reznick RK, Taylor B, Gawande AA;
Safe Surgery Saves Lives Study Group: A surgical safety
checklist to reduce morbidity and mortality in a global popu-
lation. NEngl J Med 2009; 360:491–9
37. van Klei WA, Hoff RG, van Aarnhem EE, Simmermacher
RK, Regli LP, Kappen TH, van Wolfswinkel L, Kalkman CJ,
Buhre WF, Peelen LM: Effects of the introduction of the WHO
“Surgical Safety Checklist” on in-hospital mortality: A cohort
study. AnnSurg 2012; 255:44–9
38. Mills PD, DeRosier JM, Neily J, McKnight SD, Weeks WB,
Bagian JP: A cognitive aid for cardiac arrest: You can’t use it if
you don’t know about it. JtComm J Qual Saf 2004; 30:488–96
39. Novak LL: Finding hidden sources of new work from BCMA
implementation: The value of an organizational routines per-
spective. AMIA Annu Symp Proc 2012; 2012:673–80
40. Wears RL, Berg M: Computer technology and clinical work:
Still waiting for Godot. JAMA 2005; 293:1261–3
41. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E,
Morton SC, Shekelle PG: Systematic review: Impact of health
information technology on quality, efciency, and costs of
medical care. AnnIntern Med 2006; 144:742–52
42. Neal JM, Hsiung RL, Mulroy MF, Halpern BB, Dragnich AD,
Slee AE: ASRA checklist improves trainee performance dur-
ing a simulated episode of local anesthetic systemic toxicity.
RegAnesth Pain Med 2012; 37:8–15
43. Practice alert for the perioperative management of patients
with coronary artery stents: A report by the American Society
of Anesthesiologists Committee on Standards and Practice
Parameters. A
NESTHESIOLOGY 2009; 110:22–3
44. Douketis JD, Spyropoulos AC, Spencer FA, Mayr M, Jaffer
AK, Eckman MH, Dunn AS, Kunz R; American College of
Chest Physicians: Perioperative management of antithrom-
botic therapy: Antithrombotic Therapy and Prevention of
Thrombosis, 9
th
ed: American College of Chest Physicians
Evidence-Based Clinical Practice Guidelines. Chest 2012;
141(2 suppl):e326S–50S
45. Arriaga AF, Bader AM, Wong JM, Lipsitz SR, Berry WR,
Ziewacz JE, Hepner DL, Boorman DJ, Pozner CN, Smink DS,
Gawande AA: Simulation-based trial of surgical-crisis check-
lists. NEngl J Med 2013; 368:246–53
46. Ziewacz JE, Arriaga AF, Bader AM, Berry WR, Edmondson L,
Wong JM, Lipsitz SR, Hepner DL, Peyre S, Nelson S, Boorman
DJ, Smink DS, Ashley SW, Gawande AA: Crisis checklists for
the operating room: Development and pilot testing. J Am
Coll Surg 2011; 213:212–217.e10
Anesthesiology 2014; 120:1339-53 1347 Hand et al.
PERIOPERATIVE MEDICINE
Appendix 1. Various Screenshots from the Decision Support Tool as Seen by a User. The
Screenshots Demonstrate Both User Interface (Questions to Answer) and Specific End
Points of the Preoperative Testing Algorithm. The Actual Decision Support Tool Uses a
Touchscreen Interface and Only One Screen Is Visible at Any Time
(Continued )
Anesthesiology 2014; 120:1339-53 1348 Hand et al.
Decision Support for Perioperative Assessment
(Continued )
Appendix 1. (Continued )
Anesthesiology 2014; 120:1339-53 1349 Hand et al.
PERIOPERATIVE MEDICINE
Appendix 2. Multiple Choice Questions. Quizzes A and B. The Scenario Descriptions and
Multiple Choice Questions Were Presented with and without the Decision Support Tool
Available and in Assigned Order to Each Participant
P # ________ Cognitive Aid YES NO Time Start:__________
Quiz A Order: 1st 2nd Time Finish:_________
1. 65-yr-old male with 40 pack-year tobacco history, obesity, and anxiety disorder was recently diagnosed with laryngeal
cancer presenting for radical neck dissection. He does no physical activity due to social anxiety. His father died of heart
disease at 45 yr. e patient’s preoperative vital signs are T: 36.5, HR: 82, BP: 155/72, RR: 16.
Multiple choice:
A. Proceed with planned surgery.
B. Refer for treadmill cardiac stress test.
C. Need ECG before I can make a recommendation.
D. Delay surgery until BP is <140/70.
E. Refer for cardiac consultation.
2. 61-yr-old female with controlled hypertension, insulin-dependent diabetes (Hbg A1c 9.9%), and gastro-esophageal
reflux disease is scheduled for metatarsal tendon release for bilateral “hammer toes.” Glucose ranges from 110 to 230 mg/
dl. She works as a waitress and walks to and from work, worsening her foot pain. e patient’s preoperative vital signs are
T: 36.5, HR: 78, BP: 124/82, RR: 14.
Multiple choice:
A. Proceed to surgery.
B. Order a stress test (dobutamine echo, exercise, or nuclear imaging).
C. Start β-blocker therapy and delay surgery until heart rate adequately controlled.
D. Delay surgery until Hgb A1c <7%.
E. Patient needs ECG before I can make recommendation.
Appendix 1. (Continued )
Anesthesiology 2014; 120:1339-53 1350 Hand et al.
Decision Support for Perioperative Assessment
3. 72-yr-old male with a history of poorly controlled diabetes on insulin therapy, CHF, TIA with residual weakness on his
left side, hypertension, and a 50 pack-year history of smoking presents for elective repair of 6.2-cm abdominal aortic
aneurysm. He his able to walk only one block at a time because of left sided weakness. e patient’s preoperative vital
signs are T: 36.5, HR: 98, BP: 165/85, RR: 18.
Multiple choice:
A. Proceed to planned surgery.
B. Patient needs to have PFTs before proceeding.
C. Consider testing if it will change management.
D. Delay surgery overnight to collect records from outside providers.
E. Obtain immediate cardiology consultation.
4. 4.71-yr-old male with poorly controlled insulin-dependent diabetes and hypertension which led to renal dysfunction and
peripheral neuropathy describes his activity as being limited to dressing, brushing his teeth, and clicking the television
remote control. His preoperative creatinine is 2.3 mg/dl. Colonoscopy revealed colon cancer and you are evaluating him
for a colectomy in 5 days. e patient’s preoperative vital signs are T: 36.5, HR: 82, BP: 144/78, RR: 14.
Multiple choice:
A. Stress test is needed before proceeding to OR.
B. Start β-blockers and delay surgery 1 month.
C. Refer for 2-D echocardiogram.
D. Need ECG before I can make recommendation.
E. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
5. 68-yr-old female with poorly controlled diabetes and hypertension presents after an automobile accident with an open
femur fracture and cold right foot. e patient is stable and answers your questions. She is unable to do more than her
daily chores without becoming short of breath and had a stroke 2 yr ago. e patient’s preoperative vital signs are T: 36.5,
HR: 98, BP: 156/55, RR: 14.
Multiple choice:
A. Obtain immediate cardiology consultation.
B. Patient needs to have 2-D echocardiogram before I make recommendation.
C. Need ECG before I make recommendation.
D. Consider noninvasive testing if it would change management.
E. Proceed to planned surgery.
6. 62-yr-old male with 40 pack-year smoking history and ischemic stroke 2 yr ago presents for an elective femoral-popliteal
bypass due to claudication. He can walk several blocks but is ultimately limited by leg pain. e patients preoperative
vital signs are T: 36.5, HR: 78, BP: 178/82, RR: 12.
Multiple choice:
A. Refer for cardiology consultation.
B. Need ECG before I can make recommendation.
C. Delay surgery until BP <140/70.
D. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
E. Stress test is required before going to OR.
7. 66-yr-old male active smoker (1ppd × 20 yr) with severe osteoarthritis is scheduled for total knee arthroplasty. He suf-
fered an MI 10 weeks ago but did not require percutaneous intervention and is completing rigorous cardiac rehabilitation
despite knee pain. He was discharged with β-blocker, statin, and ACE inhibitor. He is compliant with medications and
endorses only knee pain as limitation to exertion. e patient’s preoperative vital signs are T: 36.5, HR: 62, BP: 118/59,
RR: 14.
Multiple choice:
A. Surgery should be delayed at least 3 months after a myocardial infarction.
B. Patient should have a stress test (dobutamine echo, exercise, or nuclear image) before surgery.
C. Proceed with planned surgery.
D. Surgery should be delayed at least 6 months after a myocardial infarction.
E. Need ECG and pulmonary function tests before I can make a recommendation.
Anesthesiology 2014; 120:1339-53 1351 Hand et al.
PERIOPERATIVE MEDICINE
8. 72-yr-old male with history of calf claudication who has type 2 diabetes requiring insulin therapy, hypertension and chronic
renal insufficiency (Cr 2.2 mg/dl) is scheduled for femoral-popliteal bypass. He endorses worsening chest pain and shortness
of breath while doing household chores. e patients preoperative vital signs are T: 36.5, HR: 55, BP: 145/85, RR: 18.
Multiple choice:
A. Patient needs a cardiac catheterization immediately.
B. Patient should have a stress test, β-blockade, and cardiology consult before surgery.
C. Proceed with surgery if todays ECG is unchanged from ECG performed 5 months ago.
D. Proceed with planned surgery with HR control.
E. Proceed with planned surgery.
9. 68-yr-old male presents with poorly controlled diabetes on insulin therapy, hypertension, chronic kidney dysfunction
with creatinine of 2.1, congestive heart failure, and 40 pack-year history of smoking. He is unable to walk more than three
blocks secondary to fatigue and some knee pain. He is presenting for total knee replacement. e patient’s preoperative
vital signs are T: 36.5, HR: 98, BP: 165/85, RR: 18.
Multiple choice:
A. Proceed to planned surgery.
B. Patient needs to have PFTs before I make recommendation.
C. Need ECG before I make recommendation.
D. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
E. Obtain immediate cardiology consultation.
P # ________ Cognitive Aid YES NO Time Start:__________
Quiz B Order: 1st 2nd Time Finish:_________
1. 66-yr-old female with uncontrolled diabetes with diabetic nephropathy (creatinine of 2.1), hypertension, TIA 2 yr ago
with no residual symptoms, and a symptomatic pituitary adenoma presents for elective transphenoidal resection of pitu-
itary tumor. She manages her ADLs but does not leave her residence due to fatigue. e patients preoperative vital signs
are T: 36.5, HR: 98, BP: 165/85, RR: 18.
Multiple choice:
A. Proceed to planned surgery.
B. Patient needs to have 2-D echocardiogram before I make recommendation.
C. Need ECG before I make recommendation.
D. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
E. Obtain immediate cardiology consultation.
2. 67-yr-old female with uncontrolled diabetes, hypertension, and dialysis-dependent renal disease presents for same-day
evaluation from dialysis clinic. She has developed a clot and infection in her A-V fistula, which has since developed sub-
cutaneous crepitus. Patient is posted for exploration and revision. She has poor functional capacity and her last stress test
(last week) was consistent with inducible ischemia. Patient has scheduled an appointment with her cardiologist in 1 week
to discuss intervention. e patient’s preoperative vital signs are T: 36.5, HR: 128, BP: 156/55, RR: 14.
Multiple choice:
A. Obtain immediate cardiology consultation.
B. Patient needs to have 2-D echocardiogram before I make recommendation.
C. Need ECG before I make recommendation.
D. Consider noninvasive testing if it would change management.
E. Proceed to surgery.
3. 70-yr-old female who had a myocardial infarction 6 yr ago presents for preoperative evaluation for nephrectomy to treat renal cell
carcinoma. She has chronic renal insufficiency (Cr 2.5 mg/dl), hypertension, and indicates she has to rest after walking two blocks on
flat ground but symptoms resolve quickly with rest. e patient’s preoperative vital signs are T: 36.5, HR: 82, BP: 144/78, RR: 14.
Multiple choice:
A. PFTs needed before proceeding to OR.
B. Start β-blockers and delay surgery 4 weeks.
C. Refer for 2-D echocardiogram.
D. Need ECG before I can make recommendation.
E. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
Anesthesiology 2014; 120:1339-53 1352 Hand et al.
Decision Support for Perioperative Assessment
4. 66-yr-old female with hypertension and hypothyroidism presents for evaluation before her scheduled lumbar laminec-
tomy and fusion. Her leg pain prevents her from standing and she is not sure what exertion she is capable of. Patient takes
a statin for hypercholesterolemia and thyroid hormone replacement. e patients preoperative vital signs are T: 36.5,
HR: 82, BP: 125/72, RR: 16.
Multiple choice:
A. Proceed with planned surgery.
B. Refer for dobutamine stress test due to inactivity.
C. Need ECG before I can make a recommendation.
D. Delay surgery until BP is <140/70.
E. Refer for cardiac consultation.
5. 66-yr-old female with hypertension, chronic renal insufficiency (Cr 2.2 mg/dl), and type 2 diabetes endorses fatigue
while folding laundry and new chest pressure walking to mailbox on flat land. e patient presents for elective
knee arthroplasty for degenerative joint disease. e patient’s preoperative vital signs are T: 36.5, HR: 55, BP:
145/85, RR: 18.
Multiple choice:
A. Patient needs a cardiac catheterization immediately.
B. Patient should have a stress test, β-blockade, and cardiology consult before surgery.
C. Proceed with surgery if todays ECG is unchanged from ECG performed 5 months ago.
D. Proceed with planned surgery with HR control.
E. Proceed with planned surgery.
6. 64-yr-old female with poorly controlled type 2 diabetes (Hbg A1c 10.1%), controlled hypertension, and gout
presents for laparoscopic cholecystectomy. Blood glucose ranges from 110 to 230 mg/dl and she says she is able to
keep up with her peers in a twice-per-week aerobics class. e patients preoperative vital signs are T: 36.5, HR: 78,
BP: 124/82, RR: 14.
Multiple choice:
A. Proceed to surgery.
B. Order a stress test (dobutamine echo, exercise, or nuclear imaging).
C. Start β-blocker therapy and delay surgery until heart rate adequately controlled.
D. Delay surgery until Hgb A1c <7%.
E. Patient needs ECG before I can make recommendation.
7. 70-yr-old female with long smoking history (1 ppd) presents for lumbar laminectomy due to radicular pain unsuccess-
fully treated by steroid injections. After her myocardial infarction two and a half months ago, she was medically treated
with β-blocker, ACE inhibitor, and statin. She swims 20 min without stopping every day. e patient’s preoperative vital
signs are T: 36.5, HR: 62, BP: 118/59, RR: 14.
Multiple choice:
A. Surgery should be delayed at least 3 months after a myocardial infarction.
B. Patient should have a stress test (dobutamine echo, exercise, or nuclear image) before surgery.
C. Proceed with planned surgery.
D. Surgery should be delayed at least 6 months after a myocardial infarction.
E. Need ECG and pulmonary function tests before I can make a recommendation.
8. 74-yr-old female with controlled hypertension, uncontrolled type 2 diabetes, and peripheral vascular disease presents
for evaluation before having a newly diagnosed 5.6-cm abdominal aortic aneurysm repair. Her exertional capacity is
limited to light housework before fatiguing. e patients preoperative vital signs are T: 36.5, HR: 78, BP: 178/82,
RR: 12.
Multiple choice:
A. Refer for cardiology consultation.
B. Need ECG before I can make recommendation.
C. Delay surgery until BP <140/70.
D. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
E. Stress test is required before going to OR.
Anesthesiology 2014; 120:1339-53 1353 Hand et al.
PERIOPERATIVE MEDICINE
9. 74-yr-old male who suffered an ischemic stroke 2 yr ago presents with stable calf claudication after two blocks that limits
exertion, uncontrolled hypertension, and uncontrolled insulin-dependent diabetes is scheduled for elective ileo-femoral
bypass. Patient states he does not think he has any heart issues and has not visited a cardiologist since his stroke. e
patient’s preoperative vital signs are T: 36.5, HR: 98, BP: 165/85, RR: 18.
Multiple choice:
A. Proceed to planned surgery.
B. Patient needs to have PFTs before I make recommendation.
C. Proceed to surgery with heart rate control or consider noninvasive testing if it will change management.
D. Delay surgery overnight to collect records from outside providers.
E. Obtain immediate cardiology consultation.
ANESTHESIOLOGY REFLECTIONS FROM THE WOOD LIBRARY-MUSEUM
AdvertisingMaltinewithCocaWine
The roots of the Maltine tree (left) were advertised as “concentrated extract of malted wheat, oats, and barley.”
A major branch of the Maltine tree was the tonic “Maltine with Coca Wine,” each ounce of which contains “thirty
grains of assayed Huanaco Coca leaves….” According to an 1894 issue of the National Medical Review, the “Coca
boosts the patient and the maltine furnishes the peg that prevents him from slipping back.” Because Maltine had
been widely distributed to the public as a stimulant in beverages and in foods (such as Maltine with Coca Wine),
cocaine was rapidly accepted by laymen when used as a local anesthetic by dentists and physicians. (Copyright ©
the American Society of Anesthesiologists, Inc.)
George S. Bause, M.D., M.P.H., Honorary Curator, ASAs Wood Library-Museum of Anesthesiology, Park Ridge,
Illinois, and Clinical Associate Professor, Case Western Reserve University, Cleveland, Ohio. UJYC@aol.com.