DELIRIUM IN THE PCICU

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Delirium in the Pediatric Cardiovascular Intensive Care Unit

Ricardo Hernandez
Edison College of Nursing and Health Innovation, Arizona State University

Author Note
Ricardo Hernandez is a registered nurse at Phoenix Children's Hospital in the nursing
department.
He has no conflict of interest to disclose.
Correspondence should be addressed to Ricardo Hernandez, Edison College of Nursing and
Health Innovation, Arizona State University, Downtown Campus, 550 N. 3rd Street,
Phoenix, AZ 85004. Email: Raherna2@asu.edu

DELIRIUM IN THE PCICU
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Abstract
Objective: Delirium frequently occurs in Pediatric Cardiac intensive Care Units (PCICU)
of hospitals with critically ill patients. An inflammatory process of the brain causes
neurotransmitters imbalances and neuronal alterations, leading to increased days on mechanical
ventilation, length of stay in the ICU, and possible self -harm. Delirium can be reduced and
controlled if detected early through frequent patient monitoring and screening. The purpose of
this project is to evaluate the implementation of a delirium screening tool along with education
on delirium
Study Selection: An education module on delirium and the Cornell Assessment of
Pediatric Delirium (CAP-D) screening tool along with a non-pharmacological bundle was
implemented on a pediatric cardiac intensive care unit for patients 2 to 18 years of age, admitted
to an Arizona metropolitan children’s hospital. All nurses were required to attend the education
session. Data was collected by using pre- and post-survey questions on delirium for those nurses
who chose to participate in the study.
Data Synthesis: The results from the pre- and post-tests suggest there was an increase in
education. The average score for the 15 nurses on the pre-test was 87.1% while the same nurses
scored 100% on the post-test. Chart reviews of the CAP-D screening tool from November 2000 –
February 2021 had 71 patients on the unit and had 8 patients scored on the CAP-D screening
tool. Chart reviews after implementation of the education module from November 2021 –
February 2022, were conducted and 118 patients were on the unit while only 23 patients were
scored on the CAP-D screening tool.

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Conclusion: So far, the use of an education module and implementation of a nonpharmacological bundle has proven some promising results in helping with delirium and its
reduction in the PCICU.
Keywords: Delirium, delirium prevention, pediatrics delirium, cardiac intensive care
unit, education, nonpharmacological bundle.
Delirium in the Pediatric Cardiovascular Intensive Care Unit
Delirium frequently occurs in intensive care units (ICU) of hospitals with critically ill
patients. It is associated with increased length of stay and long-term cognitive impairment. An
inflammatory process of the brain causes neurotransmitter imbalances and neuronal alterations.
Delirium can be controlled and reduced if it is detected early through frequent patient monitoring
and screening.
Background and Significance
Pediatric Patients in the PCICU
Delirium has been well researched in adults since the 1940s and is heavily prevalent in
adult ICU settings (Bryant, 2018). The patients that are affected by delirium the most are those
who are mechanically ventilated, are receiving continuous sedation, and have greater severity of
illness (Alvarez et al., 2018; Bryant, 2018; Patel et al., 2017; Simeone et al., 2018; Staveski et
al., 2018; Staveski et al., 2020). Delirium causes disturbances in attention, awareness, behavior,
cognition, and perception increasing the patient’s potential to hurt themselves (Staveski et al.,
2018). In adult studies, after postoperative cardiac surgeries, it has been determined that
prolonged bypass time, longer aortic clamp time, atrial fibrillation, lower systemic perfusion
pressures, and lower cerebral oxygenation saturation levels lead to a higher incidence of delirium
(Bryant, 2018).

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This data has been studied in pediatric populations with recent and proper additions to
pediatric delirium screening tools. There is a 25% point prevalence rate of delirium in the PICU
(Alvarez et al., 2018). More recently, authors have reported an increased delirium prevalence of
49% in the PCICU because children have cyanotic heart diseases and complex surgical
intervention (Staveski et al., 2018). Surgical interventions play a role in delirium because the
children can spend an extended amount of time on cardiac bypass (Patel et al., 2017). To date,
studies of children after cardiopulmonary bypass document 49%-57% of postoperative cardiac
surgery patients screening positive for delirium (Staveski et al., 2020). Many patients in the
PCICU are mechanically ventilated, on sedating medication, and are severely ill for an extended
time (Alvarez et al., 2018; Bryant, 2018; Patel et al., 2017; Simeone et al., 2018; Staveski et al.,
2018; Staveski et al., 2020)
Delirium Screening Tool
In recent years the importance of a screening tool has been identified because the absence
of such tools has resulted in delirium going unnoticed in 70% of patients (Bannon et al., 2016).
Two main screening tools have been proven effective for screening delirium in children. The
first is the preschool and pediatric version of the Confusion Assessment Method for the Intensive
Care Unit (CAM-ICU). The second is the Cornell Assessment of Pediatric Delirium (CAP-D).
However, before using either one of these tools, one must complete an arousal assessment using
the Richmond Agitation-Sedation Scale (RASS). The RASS scale assesses three different arousal
states: plus one through plus four for agitation, minus one through minus three for decreased
arousal with retained responsiveness to verbal stimulation, and minus four through minus five for
unresponsiveness to verbal stimulation or comatose (Bryant, 2018). Thus, if the patient has a

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minus-four or minus five score, they cannot continue with the delirium screening tool because
verbal stimulation is required to diagnose delirium.
The CAM-ICU was developed from the adult version and is the most commonly used
delirium tool in adults. The pediatric version (pCAM-ICU) is highly valid in critically ill
children from the age of six months to children older than five years of age (Alvarez et al., 2018;
Bryant, 2018; Patel et al., 2017; Simeone et al., 2018; Staveski et al., 2020; Valdivia & Carlin,
2019). It tests four features of delirium, the first being acute change in mental status, the second
is inattention, the third is altered level of consciousness, and the fourth is disorganized thinking.
The presence of delirium requires the first and second features plus feature three or feature four.
When one scores a feature three, it means the patient’s RASS score is anything other than zero.
The RASS score can also determine the type of delirium. The CAM-ICU uses the patient's RASS
score as a prescreening and for scoring the patient’s delirium (Alvarez et al., 2018; Bryant, 2018;
Patel et al., 2017; Simeone et al., 2018; Staveski et al., 2020; Valdivia & Carlin, 2019).
The CAP-D was adapted and revised from the Pediatric Anesthesia Emergence Delirium
tool and used the Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-V)
criteria for diagnosing delirium (Patel et al., 2017). This revised edition better identified delirium
in pediatric patients by identifying fluctuations and alterations in cognitive functioning. In
addition, anchor points were made for the CAP-D screening tool that described age-appropriate
developmental expectations. The tools ask a total of eight questions regarding patients'
consciousness, cognition, psychomotor activity, and affect, with scoring a zero through four for
each question. Scoring a nine or above is considered a diagnosis of delirium. Unlike the CAMICU, the CAP-D is designed to be performed halfway through each 12-hour nursing shift, after
the nurses have observed the patient's behavior for several hours and should only take two

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minutes to complete (Alvarez et al., 2018; Bryant, 2018; Patel et al., 2017; Simeone et al., 2018;
Staveski et al., 2020; Valdivia & Carlin, 2019).
PCICU Current Utilization and Desired outcome
A PICU in an urban freestanding children's hospital in the southwestern United States (U.
S.) is currently using the CAP-D screening tool. The nurse's chart on the tool once shifts at noon
and midnight in the electronic medical record. However, the PCICU currently does not use a
delirium screening tool.
To decrease PCICU length of stay and minimize mechanical ventilation time, this project
will focus on screening for delirium in PCICU patients. Specifically, this project aims to improve
the nurse's knowledge of screening for delirium and the need for interventions. In addition, the
educational interventions will focus on their use of the CAP-D screening tool.
Search Strategy
A review of the most current evidence was used to answer the PICOT question stated
above. Three databases were searched – CINAHL, PubMed, and Cochrane Database. These were
used for their unique and relevant information on delirium and screening in the ICU. These
databases are well known and used continuously for their up-to-date data in medical journals.
Search Yield
The initial search in PubMed used the key terms delirium, PCICU, Screening, and tools
yielded only one article. When rephrasing and using other key terms such as delirium, children,
screening, and intensive care, the results yielded 100 articles. When searching the CINAHL
database, the same key terms were used delirium and intensive care. The first search yielded 900
results, while the last searched use key terms delirium, pediatric, and screening produced 50
articles within the inclusion and exclusion criteria. With the COCHRANE database, the first

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search used key terms delirium and intensive care unit and yielded 575 articles when subsequent
searches resulted in 71 articles using the key terms delirium, screening, and intensive care.
Among all these searches, careful consideration led to ten articles being chosen for this review.
These ten studies address the PICOT question appropriately and accurately.
Key Terms
The databases were searched using combinations of key terms that address all aspects of
the PICOT questions. The terms included: delirium, intensive care unit, pediatric, and screening.
The key terms, however, did not include cardiac because it narrowed the search too greatly.
Each database was used with a different combination of the key terms to yield articles
appropriate for the research.
Inclusion Criteria, Exclusion Criteria, and Limitations
The inclusion criteria focused on articles in English and the date range of 2016 to present.
Articles that were published greater than five years ago were excluded except the article dated
2014 that studies the reliability and validity of the CAP-D screening tool in the PICU. Inclusion
criteria also focused on pediatrics, delirium in the ICU setting, and the screening tools used to
acknowledge delirium. All studies were completed in the U. S., with the exception of two
articles. Studies that were excluded were those that were opinion-based and studies that lacked
evidence. The inclusion and exclusion criteria were used on all three databases.
Critical Appraisal and Synthesis of Evidence
Melnyk and Finout-Overholt (2019) developed the rapid critical appraisal process to
determine the quality and strength of the selected articles for this study. The studies were of the
highest quality because they were conducted without bias. All the studies used (see Appendix A,
Table A1), were quantitative and compared to each other (see Appendix A, table A2). The

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majority of the studies were conducted within the last five years and in the U. S. The average
sample size of the studies was between 100-200 people from 2-18 years of age, and all genders
were included in each study, one exception, which only included males. Upon further
comparison, the most common screening tools were the CAP-D in conjunction with the RASS
tool.
The delirium screening times varied with five studies screening twice a day, and the
remaining five screening only once a day. Six studies used or implied the social cognitive theory
(SCT). Most of the studies concluded delirium was prevalent in most of the population, and
screening led to a decreased length of stay in the ICU.
Theory Application
The underpinning theory that best fits this project is the Social Cognitive Theory (SCT).
The core of the SCT consists of three factors associated with behavior change, personal/cognitive
and environmental factors (see appendix B, Figure 1). Each of these three factors influences one
another and determines if a change will occur. Personal/ cognitive factors are comprised of selfefficacy, outcome expectations, and knowledge. Self-efficacy is defined as the confidence one
feels to carry out a specific behavior (Bandura, 1986). These factors are represented in the
evidence as becoming knowledgeable about delirium and how it is an issue in the PCICU and its
causes. Environmental factors can be physical or social and are represented in the evidence as
non-pharmacological bundles that the studies implemented to treat delirium before using
medications. Finally, behavior factors are skills used by the nurses to help reinforce to the
families the importance of environmental factors and reducing environmental stimuli. The
studies all used one of the constructs of the SCT design with hopes of creating a change. For
instance, many of the studies implemented a non-pharmacological bundle to help combat

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delirium in the PCICU, which focuses on the patient’s environment and helps influence selfefficacy when scoring the patients on one of the delirium screening tools to find out how
delirious the patient is.
Implementation Framework
This project will be guided by the plan-do-study-act (PDSA) cycle and the model for
improvement. Using PDSA cycles enables one to test out changes on a small scale, building on
the learning from these test cycles structured before wholesale implementation. This allows
stakeholders to determine if the proposed change will succeed. This way, the change process is
safer and less disruptive for patients and staff (Coury et al., 2017). In addition, the PDSA model
for improvement provides a framework for developing, testing, and implementing changes
leading to improvement.
This framework has three simple steps that build on one another. To begin the first step,
one must ask the questions "what is one trying to accomplish," "How will one know that change
is an improvement," and "what changes can be made that will result in improvement?" Once
these questions are answered, and structured step two takes place by starting the PDSA cycle.
The plan defines the objectives in questions and the predictions and plans data collection to
answer the questions. Next is Do, which is done by carrying out the plan and beginning the
analysis of data. The study comes next in the cycle, which consists of completing the data
analysis, comparing the predictions, and summarizing what is learned. Lastly, Act plans the next
cycle if another is needed or decides whether the change can be implemented (see appendix B,
Figure 2) (Coury et al., 2017).
Planning the Intervention

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Evidence has shown that when combining a non-pharmacological bundle and a delirium
screening tool such as the CAP-D tool, the incidence of delirium has decreased in several ICUs.
Among patients seen in the PCICU, how will education improve nurses' ability to screen for
delirium after implementing a delirium screening tool that can help identify and treat
delirium? The development of a non-pharmacological bundle explicitly tailored to the PCICU
and teaching the nurses how to score delirium correctly according to the CAP-D screening tool
will decrease the incidence of delirium in the PCICU.
This will begin with a pre-project survey that assesses nursing knowledge regarding
delirium, delirium screening, and non-pharmacological ways to treat delirium. After this, a
learning module will be generated based on the results of the nursing survey. This can be
introduced to the nurses during the annual review course for critical care nursing or through the
companies online learning system used for continuing education. The learning module will
define delirium, review the tools needed to screen for delirium, and describe the implementation
of the new non-pharmacological bundle in the PCICU. Once the project has been conducted, a
post-survey will be given to the nurses to assess barriers that hinder effective delirium screening.
Step by Step:
1. Participants (Nurses in the PCICU) will have two weeks to complete the pre-survey
a. QR codes will be placed around the unit for easy access to the survey
2. The learning module will be presented via the online learning module system at the
organization.
a. Participants will be given three weeks to finish the learning module
3. CAP-D screening tool will be implemented into the electronic medical record, and
scoring will commence.

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a. QR codes with easy access to the developmental anchor points placed at
computers along with reminders on when to score delirium
4. After four months of scoring delirium, a post-survey will be conducted, and nurses will
have two weeks to complete
a. QR codes for the survey were also placed around the unit for easy access to
complete.
Participants and Recruitment
The study participants will be all the nurses for the day and night shifts in the PCICU.
Patients between the ages of 3 months and 18 years of age will be recruited for the study. They
each will be screened the same using the CAP-D screening tool at noon and midnight or the
hours of 0600 and 1800, depending on then the PCICU decides to score the patients. This allows
each patient to be scored the same way and makes the scoring fair and appropriate. If the score is
greater than 9, action will need to be taken to help the patient's delirium, either by the nonpharmacological bundle or by prescribing a medication to help with sleep and wake cycles.
Many can argue that a patient experiencing delirium can cause pain. It will be up to the providers
to use the delirium score to ease the patient's pain. Faculty mentors and the IRB will review the
project's methodology through ASU and Phoenix Children's Hospital (PCH) IRB. The patients,
however, will not need to consent to the study because the scoring will take place regularly with
the nurse's assessment. By sticking to the ethical principles, the rights and risks of every patient
will be protected and taken into consideration. Human rights will be protected by following the
policies of PCH and ensuring no violations of HIPAA will occur.
Data collection and outcomes measurement

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The outcome that plans to be measured is an increase in knowledge about delirium and
how to score delirium appropriately amongst the nurses in the PCICU. The initial pre-survey will
indicate the nurse's current knowledge about delirium as well as some demographic questions.
These demographic questions consist of how long they have been a nurse, their age range,
gender, and the ages of patients they take care of. The post-survey will be given and show if
nursing knowledge increased from the pre-survey and after education and a period of screening
patients. This outcome of increased knowledge links to both the SCT and PDSA cycle.
Increasing nursing knowledge and implementing a non-pharmacological bundle to help combat
delirium in the PCICU focuses on the patient's environment. It helps influence self-efficacy
when scoring the patients on the CAP-D screening tool, emphasizing the SCT model's point. The
PDSA model for improvement provides a framework for developing, testing, and implementing
changes leading to improvement; the core of this project is implementing change and leading to
scoring improvement amongst nurses in the PCICU.
Results
Descriptive Statistics
Frequencies and percentages were calculated for Age and Experience_in_years. The most
frequently observed category of Age was 20-25 (n = 6, 40.00%). The most frequently observed
category of Experience_in_years was 0-5 (n = 12, 80.00%). Frequencies and percentages are
presented in Table 1.
Table 1
Frequency Table for Nominal Variables
Variable
Age

n

%

DELIRIUM IN THE PCICU
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20-25
26-30
31-35
36+
Missing
Experience_in_years
0-5
6-10
11-20
21+
Missing
Note. Due to rounding errors, percentages may not equal 100%.
Two-Tailed Paired Samples t-Test

6
4
3
2
0

40.00
26.67
20.00
13.33
0.00

12
1
1
1
0

80.00
6.67
6.67
6.67
0.00

A two-tailed paired samples t-test was conducted to examine whether the mean difference
of Delirium Pretest and Delirium Posttest was significantly different from zero.
A Shapiro-Wilk test was conducted to determine whether the differences in Delirium
Pretest and Delirium Posttest could have been produced by a normal distribution (Razali & Wah,
2011). The results of the Shapiro-Wilk test were significant based on an alpha value of .05, W =
0.80, p = .004. This result suggests the differences in Delirium Pretest and Delirium Posttest are
unlikely to have been produced by a normal distribution, indicating the normality assumption is
violated.
Levene's test was conducted to assess whether the variances of Delirium Pretest and
Delirium Posttest were significantly different. The result of Levene's test was significant based
on an alpha value of .05, F (1, 28) = 28.00, p < .001. This result suggests it is unlikely that
Delirium Pretest and Delirium Posttest were produced by distributions with equal variances,
indicating the assumption of homogeneity of variance was violated.
The result of the two-tailed paired samples t-test was significant based on an alpha value
of .05, t (14) = -4.03, p = .001, indicating the null hypothesis can be rejected. This finding

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suggests the difference in the mean of Delirium Pretest and the mean of Delirium Posttest was
significantly different from zero. The mean of Delirium Pretest was significantly lower than the
mean of Delirium Posttest. The results are presented in Table 2. A bar plot of the means is
presented in Figure 1.
Table 2
Two-Tailed Paired Samples t-Test for the Difference Between Delirium Pretest and Delirium
Posttest
Delirium_Pretest
Delirium_Posttest
M
SD
M
SD
t
p
89.17
10.42
100.00
0.00
-4.03
.001
Note. N = 15. Degrees of Freedom for the t-statistic = 14. d represents Cohen's d.
Figure 1
The means of Delirium_Pretest and Delirium_Posttest with 95.00% CI Error Bars

Two Proportions z-Test

d
1.04

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A two proportions z-test was conducted to examine whether there was a significant
difference between the proportions of Patients Scored on CAP-D Pre-Intervention and Patients
Scored on CAP-D Post Intervention.
The assumption of normality was assessed using the Central Limit Theorem (CLT). The
mean of any random variable will be approximately normally distributed as sample size
increases according to the CLT. Therefore, with a sufficiently large sample size (n > 50),
deviations from normality will have little effect on the results (Pituch & Stevens, 2015). The
sample size (ns1 = 71, ns2 = 118) indicates that the CLT applies, and normality can be assumed
for the purposes of the z-test.
The result of the two proportions z-test was not significant based on an alpha value of .05,
z = -1.55, p = .122, 95.00% CI = [-.18, .02], indicating the null hypothesis cannot be rejected.
This suggests there was no significant difference between the proportions of Patients scored on
CAP-D Pre-Intervention and Patients scored on CAP-D Post Intervention. The 95.00%
confidence interval for the difference between the proportions of Patients scored on CAP-D PreIntervention and Patients scored on CAP-D Post Intervention is -.18 to .02. Table 3 presents the
results of the two sample proportions z-test.
Table 3
Two Proportions z-Test for the Difference between
Patients_Scored_on_CAPD_Pre_Intervention and
Patients_Scored_on_CAPD_Post_Intervention
Samples
Patients_Scored_on_CAPD_Pre_Intervention
Patients_Scored_on_CAPD_Post_Intervention
Note. z = -1.55, p = .122, 95.00% CI: [-.18, .02]

Responses
n
8 71
23 118

Proportion
.11
.19

SD
SE
0.32 0.04
0.40 0.04

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The impact of the project had some mixed reviews. Initially, 30 nurses opted for the
project by taking the pre-survey; however, only 15 nurses completed the post-survey resulting in
a 50% attrition rate. The feedback from providers and nurses was positive, and the providers
began asking each nurse during rounds what their patient's delirium score was, which helped the
nurses remember to score each patient. The project can be sustained by implementing a unit or
hospital-wide delirium screening policy in intensive care units.
Discussion
The results suggest that implementing a non-pharmacological bundle and education on
delirium increases knowledge of delirium, but the results did not suggest an increase in scoring
patients. This could be a limitation because most of the patients admitted into the PCICU are less
than two years of age and the study only focused on patients 2-18 years of age. The CAP-D
screening tool does have anchor points that help the nurse score patients less than 2; however,
the initial response on the unit made it difficult to add this into a focused part of the education.
With this being difficult to teach and bring up, the unit's clinical nurse attending, and educator
will give follow-up teaching on the anchor points so more patients can be scored. This is what
has been shown in other research that it is difficult for nurses to get into the habit of scoring for
delirium. The project's strengths were that the education was informative and received well by
most nurses on the unit. The providers and nursing education can further sustain the intervention
in this setting by having an auto-populating chart reminder at noon and midnight to remind the
nurse to score for delirium. Overall, with the importance of cutting delirium and being aware
patients experience delirium, we can decrease days on mechanical ventilation, length of stay in
the PCICU, and possible self-harm.

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Staveski, S. L., Pickler, R. H., Khoury, P. R., Ollberding, N. J., Donnellan, A. L., Mauney, J. A.,
Lincoln, P. A., Baird, J. D., Gilliland, F. L., Merritt, A. D., Presnell, L. B., Lanese, A. R.,
Lisanti, A., Large, B. J., Fineman, L. D., Gibson, K. H., Mohler, L. A., Callow, L.,
Barnes, S. S., & Curley, M. Q. (2020). Prevalence of ICU delirium in postoperative
pediatric cardiac surgery patients. Pediatric Critical Care Medicine, 22(1), 68–78.
https://doi.org/10.1097/pcc.0000000000002591
Staveski, S. L., Pickler, R. H., Lin, L., Shaw, R. J., Meinzen-Derr, J., Redington, A., & Curley,
M. Q. (2018). Management of pediatric delirium in pediatric cardiac intensive care
patients. Pediatric Critical Care Medicine, 19(6), 538–543.
https://doi.org/10.1097/pcc.0000000000001558

DELIRIUM IN THE PCICU
21
Traube, C., Silver, G., Kearney, J., Patel, A., Atkinson, T.M., Yoon, M.J., Halpert, S.,
Augenstein, J., Sickles, L.E., Li, C., & Greenwald, B. (2014). Cornell assessment of
pediatric delirium: a valid, rapid, observational tool for screening delirium in the
PICU. Critical Care Medicine, 42(3):656-663. doi:10.1097/CCM.0b013e3182a66b76
Traube, C., Silver, G., Reeder, R.W., Doyle, H., Hegel, E., Wolfe, H.A., Schneller, C., Chung,
M.G., Dervan, L.A., DiGennaro, J.L., Butram, S.D.W., Kudchadkar, S.R., Madden, K.,
Hartman, M.E., DeAlmeida, M.L., Walson, K., Ista, E., Baarslag, M.A., Salonia,
R.,…Bell, M. (2017). Delirium in critically ill children: An international point prevalence
study. Critical Care Medicine. doi:10.1097/CCM.00000000000002250
Valdivia, H. R., & Carlin, K. E. (2019). Determining interrater reliability of the Cornell
assessment of pediatric delirium screening tool among PICU nurses. Pediatric Critical
Care Medicine, 20(4), e216–e220. https://doi.org/10.1097/pcc.0000000000001896

DELIRIUM IN THE PCICU

16
Appendix A
Evaluations and Synthesis Tables

Table A1
Quantitative Evaluation Table

Citation

Simeone et
al., 2018.
Delirium in
ICU patients
following
cardiac
surgery: An
observationa
l study.
Country:
Italy
Funding:
None

Theory/
Conceptual
Framework

TVTF

Design/
Method/
Sampling

Type: OD
Method:
RASS
CAM-ICU

Sample/Setting

N= 89
Demographic
s:
Male,
Married, over
18 years old,
in ICU for
>24 hours
EC: Drug use,
RASS score
less than 4,
HD.

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

IV: Age,
Duration of
ICU stay, BP,
LRS.

RASS

DV:
Delirium

CAM-ICU

Data
Analysis

Findings/
Themes

Independent Reported as:
sample t
P values
test
% change
SD of 6.9 in
the study
showed that
the patients
age,
duration on
MV,
exposure to
artificial
light, and
sleep
disturbance

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

LOE: Level I
Strengths:
RASS, CAMICU
Results of
testing
Weakness:
Adult only
No test-retest
Feasibility:
yes
Good
indications

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; DSM-V Diagnostic Statistical manual of Mental Disorders 5th edition; EC Exclusion
Criteria; HD Hearing Disorder; IC Inclusion Criteria; ICU Intensive Care Unit; IV Independent Variable; LRS Location in regard to sunlight; LOS Length of
Stay; MV Mechanical Ventilation; OD Observational Design; PICU Pediatric Intensive Care Unit; RN Registered Nurse; RASS Richmond Agitation Sedation
Scale; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Bias:
Delirium
was
conducted
once daily
Valdivia et
al., 2019.
Determining
Interrater
Reliability
of the
Cornell
Assessment
of Pediatric
Delirium
Screening
Tool Among
PICU
Nurses.
Country:
USA

Theory/
Conceptual
Framework

23

Design/
Method/
Sampling

Sample/Setting

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

leads to
delirium.

SCT

Type: Cross
sectional
study
conducted
over the
course of a
year.
Purpose: To
determine
the interrater
reliability of
the CAP-D
screening
tool among
PICU
nurses.

N = 108
Demographic
s:
Sex: male and
female
Age 2-18years
of age
IC: All
patients
eligible for a
CAP-D
assessment
EC: All
patients who
had a RASS

Variables:
Primary
Diagnosis
Hospital
Unit: PICU
or CICU
Development
al delay
Patient
Intubated
Patient
Sedated

KC: 0.60
indicating
moderate
agreement
>2 years KC
was 0.85
Instrumentatio
n: Score
patients at Noon
with the CAP-D
tool. First the
RN then the
Research Nurse
scores keeping
blindly the

Scores
agree
72.4% of
the time for
patients < 2
years old
Scores
agree 94%
of the time
for patients
>2 years
old.

Findings:
Age: Higher
association
with
agreement

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

LOE: Level
II
Strengths:
Teaching staff
how to
correctly
score using
CAP-D
screening tool
before
beginning the
study
Weakness:
Dosage of
opioid
medications

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Funding:
Yes but was
not stated
from where.
Bias:
Delirium
screening
was
conducted
once daily

Theory/
Conceptual
Framework

24

Design/
Method/
Sampling

Sample/Setting

score of -4 or
-5 based on
CAP-D
procedure;
Five were
excluded for
missing
information

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

results of the
score.

Data
Analysis

Findings/
Themes

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

for sedations
patients were
on.
No patients
>8 years old

Conclusion:
Need to
accurately
identify
patients who
are at higher
risk for
delirium prior
to the study
and
encourage
consistency
among RN’s
scoring with
the CAP-D

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Alvarez et
al., 2017.
Delirium is a
Common
and Early
Finding in
Patients in
the Pediatric
Cardiac
Intensive
Care Unit.
Country:
USA
Funding:
Not Stated
Bias:
Delirium
screened
twice a day

Theory/
Conceptual
Framework

Theoretical
Framework

25

Design/
Method/
Sampling

Design:
SingleCentered
prospective
observationa
l study of
CICU
patients.
Purpose: To
determine
incidence,
associated
risk factors,
and
characteristi
cs of
delirium in
pediatric
CICU.

Sample/Setting

N = 99
Sex: male and
female
Age: 2-18
Primary
diagnosis
Comorbidities
Admission
type
Respiratory
Support
Mechanical
Ventilation

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Variables:
Types of
Delirium:
Hyperactive

Age 4- 46
months P <
0.001

Hypoactive
Mixed
Patterns:
Continuous
N =17
Intermittent
N = 12
Recovery
N=25
CPB time
MV

MV mean 39.9
vs 8.8 hours P =
.002
CPB mean 126
vs 81 minutes P
= 0.001
LOS 3 vs 1-day
P = 0.0001

Data
Analysis

Sample t
test

Findings/
Themes

Findings:
Incidence of
delirium was
57% and
median time
to be
diagnosed
with
delirium was
1 day.
Diagnosed
using CAPD screening
tool and a
RASS score
lower than 4.

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

LOE: Level
III

Strengths:
Using the
CAP-D
screening tool
for
consistency
Limitations:
compliance
with
screening was
85% resulting
in missing
data
Conclusion:
Delirium is
common in
patients in the
CICU and is

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Traube et
al., 2017.
Pediatric
Delirium in
critically ill
children: An
international
point
prevalence
study.
Country:
USA New
Zealand,
Australia,
Saudi

Theory/
Conceptual
Framework

Environment
al theory

26

Design/
Method/
Sampling

Design:
Multiinstitutional
point
prevalence
study
Purpose: To
determine
prevalence
of delirium
in critically
ill children
and explore
risk factors

Sample/Setting

N = 994
Demographic
:
Sex:
Male/female
Age: >2
Primary
diagnosis
Days in PICU
Admission
type

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Variables:
Age >2 years

Male n = 537
(54%)

Restraints.

LOS: > 6 days n
= 537

MV
Drugs used

RD: n = 415
MV n = 335

Data
Analysis

REDCap

Findings/
Themes

Findings:
Delirium
prevalence
was 38%
across 25
institutions.
Diagnosed
using the
CAP-D
screening
tool and
RASS score
lower than 4.

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

associated
with greater
LOS. Benefits
in prevention
by targeting
modifiable
risk factors.
LOE: Level
IV
Strengths:
The multi
institutional
nature proved
that delirium
is widely
prevalent in
children with
a critical
illness.
Limitations:
CAP-D is
originally

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Theory/
Conceptual
Framework

27

Design/
Method/
Sampling

Sample/Setting

Arabia, the
Netherlands

Respiratory
Support

Funding:
Gerber
foundation
and from
salary/
employment
form Seattle
Children’s
Hospital

Medications
used

Bias:
Delirium
was Scored
once daily
mid-day.

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

used to be
scored at end
of shift, but
this study did
score midday. Child
may not have
shown
fluctuating
symptoms of
delirium at
this time.

Conclusion:
84% of the
994 subjects
that were in
the study
proved to
have delirium
as a common
complication

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Patel et al.,
2017.
Delirium in
children
after cardiac
bypass
surgery.
Country:
USA
Funding:
undisclosed
Bias: No
biasness

Theory/
Conceptual
Framework

SCT

28

Design/
Method/
Sampling

Design:
prospective
observationa
l singlecenter study
Purpose: To
describe the
incidence of
delirium in
pediatric
patients after
CPB surgery

Sample/Setting

Major Themes
Studied/
Definitions

N= 194
Demographic
:
Sex

Variables:
Age >2 years

Age

MV

Primary
diagnosis

Drugs used

Days in PICU
Admission
type
Respiratory
Support
Medications
used

Restraints.

CPB

Measurement/
Instrumentation

LOS p<0.001
Delirium p
values < 0.03

Data
Analysis

Findings/
Themes

Findings:
49%
incidence of
delirium
Independent lasting 1-2
days and
sample tafter 1-3
tests
days of
Multivariab being on
CPB.
le linear
regression
60%
increase in
LOS
Chi square/
Fischer’s
exact tests

Diagnosed
using the
CAP-D
screening
tool and
RASS score

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

of critical
illness.
LOE: Level
III

Strengths:
First study to
be conducted
in PCICU
Limitation:
Delirium was
screened only
once/ day.
Delirium
subtype was
not
determined
Conclusion:
Children
undergoing

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Theory/
Conceptual
Framework

29

Design/
Method/
Sampling

Sample/Setting

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

lower than 4.

Staveski et
al., 2018.
Management
of pediatric
delirium in
pediatric
cardiac
intensive
care
patients: An
international
survey of
current
practice.
Country:

SCT

Design:
Descriptive
Self report
Survey

N = 173
Demographic
: PCICU
Clinicians

Purpose:
Describe
how PCICU
clinicians
assess for
delirium and
manage
delirium in
following
cardiac
surgery

Age
Gender
ICU Type
# of pediatric
cardiac
surgeries the
institution

Variables:
Physicians
and Nurses

Physicians n =
81
Nurses n = 92

REDCap
with
descriptive
statistics
chi- square
or exact
tests were
used.

Findings:
570
members
were
contacted
through email 58%
responded to
all survey
questions.
75% of
respondent
reported
their ICU
does not

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

CPB are at
high risk for
delirium postoperatively
causing an
increase in
LOS.
LOE: Level
II
Conclusion:
The study
results show a
need for the
importance of
delirium
education in
PCICU as
well as
appropriate
systematic
evaluation of
current

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

North
America,
European
Union,
United
Kingdom,
South
America,
Australia
and Asia.
Funding:
Internal
grants from
the
Cincinnati
Children’s
Hospital

Theory/
Conceptual
Framework

30

Design/
Method/
Sampling

Sample/Setting

performs per
year

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

screen for
delirium.
61% never
attended a
lecture on
delirium

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

delirium
assessment
and
management
practices.

86% were
not satisfied
with current
delirium
screening,
diagnosis,
and
management
practices.

Bias: Non
noted

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Staveski et
al., 2021.
Prevalence
of ICU
delirium in
postoperativ
e pediatric
cardiac
surgery
patients.
Country:
USA
Funding:
none noted
Bias: 1-day
study;
scored
CAPD in
am.

Theory/
Conceptual
Framework

SCT

31

Design/
Method/
Sampling

Design: 1Day
multicenter
pointprevalence
study

Sample/Setting

N = 181
Demographic
s:
Delirium
positive

Delirium
Purpose: To Negative
determine
the
prevalence
of ICU
delirium in
children <
18 years old
that
underwent
cardiac
surgery.

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Variable:
Age

Focus group

Gender
Race
Past medical
history
Cardiac
history
MV
Vasoactive
infusion
Invasive
Catheters
Respiratory
Support

Data
Analysis

REDCap
with
descriptive
statistics
exact tests,
and
Wilcoxon
sum tests
were used.

Findings/
Themes

Findings:
40%
screened
positive for
delirium
55%
required
longer MV
Diagnosed
using the
CAP-D
screening
tool and
RASS score
lower than 4.

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

LOE: Level
III
Strengths:

Limitations:
Inability to
estimate
occurrence
and
challenges of
disease
process.
Data was
collected
during day
shift
Conclusion:
Delirium is a
common
problem after
cardiac

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Bannon et
al., 2016.
Impact of
non-pharma
logical
interventions
on
prevention
and
treatment of
delirium in
critically ill
patients:
protocol for
a systematic
review
Country:
USA

Theory/
Conceptual
Framework

Environment
al theory

32

Design/
Method/
Sampling

Design:
metaanalysis
Purpose:
the interest
of nonpharma
logical
intervention
s and this
systematic
review
addresses
delirium in
critically ill
patients and
will help
guide

Sample/Setting

Demographic
s:
Critically ill
patients in the
ICU requiring
Oxygen
Pressor
medication
MV
Pediatric and
Adult
populations

Major Themes
Studied/
Definitions

Variables:
Exclude
studies of
interventions
delivered
after ICU
discharge

Measurement/
Instrumentation

DSM-V
CAM-ICU

Data
Analysis

Data
extraction
form

Findings/
Themes

Findings:
Nonpharmalogic
al
intervention
s have been
studied
outside of
the ICU

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

surgery with
several
modifiable
risk factors.
LOE: Level
II

Strengths:
Searched
many
different data
bases for
studies on
nonpharmalogical
studies.
Limitations:
Only used the
DSM-V and
CAM-ICU
assessments

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Theory/
Conceptual
Framework

Funding:
not disclosed

Country:

Design/
Method/
Sampling

Sample/Setting

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

delirium
management
.

Bias:
Cochran risk
of bias form
was used
Traube et
al., 2014.
Cornell
Assessment
of pediatric
delirium
: A valid,
rapid,
observationa
l tool for
screening
delirium in
the PICU.

33

SCT

Design:
Double
blinded
assessment

N = 111
Demographic
s:
Age
Gender

Purpose: To
Respiratory
determine
support
the validity
and
reliability of Diagnosis
the CAP-D
screening
tool.

Variables:
Age
Development
al delay
Mental status
examination

DSM-V
CAP-D

Sample ttest

Findings:
CAP-D has
overall 94%
sensitivity
rate.
Diagnosed
using CAPD screening
tool and a
RASS score
lower than 4.

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

Conclusion:
Further
studies need
to be done in
the ICU for
nonpharmalogical
interventions.
LOE: Level
III
Strengths:
The CAP-D
scoring tool
can be done
in > 2 min
Limitations:
Was only
tested in a
single
institution,
low screening

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Theory/
Conceptual
Framework

34

Design/
Method/
Sampling

Sample/Setting

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

USA

in
adolescents,
significant
developmenta
l delay can
create false
positives.

Funding:
None notes
Bias: not
identified

Kahn, B et
al., 2019.
Pharmalogic

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

SCT

Design:
Randomized

N = 351

Variables:

RASS
CAM-ICU

Fisher exact
test

Findings:

Conclusion:
Delirium is a
common
problem in
the PICU and
the CAP-D
scoring
assessment is
a rapid and
valid tool in
assessing
Delirium.
LOE: Level
I

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

al
management
of delirium
in the ICU:
A
randomized
pragmatic
clinical trial
Country:
USA
Funding:
National
institute on
aging
Bias: not
identified

Theory/
Conceptual
Framework

35

Design/
Method/
Sampling

pragmatic
clinical trial

Sample/Setting

Demographic
:

Purpose: To Age
test the
Efficacy of a Race
PMD bundle
in the ICU.
ICU
Diagnosis

Major Themes
Studied/
Definitions

Medications
used to
address
delirium

Measurement/
Instrumentation

LOS

Data
Analysis

Findings/
Themes

MannWhitney U

No
significant
difference
within
median
delirium
days versus
usual care
days. There
was a in
delirium
severity
days when
the CAMICU was
used.

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

Strengths:
Haloperidol
was
determined to
be efficacious
in treating
delirium
Limitations:
Single city
study
Some may
have received
intervention
48 hours post
randomization
decreasing the
efficacy.
Conclusion:
Implementati
on of a PMD

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

Citation

Theory/
Conceptual
Framework

36

Design/
Method/
Sampling

Sample/Setting

Major Themes
Studied/
Definitions

Measurement/
Instrumentation

Data
Analysis

Findings/
Themes

Level/Quality
of Evidence;
Decision for
practice/
application to
practice/
Generalization

bundle does
not
necessarily
reduce
delirium
among
critically ill
patients.

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU

37

Table A2
Synthesis Table
Study
(Author and
year)
Design/LOE

Simeone
et al.,
2018

Valdivia et
al., 2019

Alvarez et
al., 2017

Traube et al.,
2017

Patel et al.,
2017

Staveski et
al., 2018

Staveski et
al., 2021

Bannon et
al., 2016

Traube et
al., 2014

Kahn, B et
al., 2019

Level I

Level II

Level III

Level IV

Level III

Level II

Level III

Level II

Level III

Level I

89

108

99

994

194

173

181

85

111

351

Country

Italy

USA

USA

USE, New
Zealand,
Australia,
Saudi,
Netherlands

USA

USA

USA

USA

USA

Delirium
Assessed Daily

Once

Once

Twice

Once

Twice

USA,
Europe,
United
Kingdom,
South
America,
Australia,
Asia
Twice

Once

Once

Twice

Twice

Sample
N of subjects

MV

•

Diagnosis

•

•

•

•

•

•

•

•

•

•

•

•

•

•
•

•

Demographic
Age in years
Sex

>18

2-18

2-18

2-18

2-18

2-18

2-18

2-18

2-18

2-18

Male

Male/female

Male/female

Male/female

Male/female

Male/female

Male/female

Male/female

Male/female

Male/female

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

DELIRIUM IN THE PCICU
Applicable
measurement
tools

CAP-D

38

•

CAM-ICU

•

RASS

•

•

•

•

•

•
•

•

•

•

•

•

•

•

DSM-V

Framework

•

•
•

TVTF

SCT

TVTF

Environmental

SCT

SCT

SCT

Environmental

•

•
SCT

SCT

Outcomes
Length of Stay
Decrease in
hospital cost
Decrease
mortality

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

Key: BP Blood Pressure; CAM-ICU Confusion Assessment Method for the ICU; CAP-D Cornell Assessment of Pediatric Delirium; CICU Cardiac Intensive
Care Unit; CPB Cardiopulmonary Bypass; DV Dependent Variable; HD Hearing Disorder; ICU Intensive Care Unit; IV Independent Variable; KC Kappa
Coefficient; LRS Location in regard to sunlight; LOE Level of Evidence; LOS Length of Stay; MV Mechanical Ventilation; N Total number of Sample Size; n
Amount in sample group; OD Observational Design; PCICU Pediatric Cardiac Intensive Care Unit; PICU Pediatric Intensive Care Unit; PMD Pharmalogical
management of delirium; RASS Richmond Agitation Sedation Scale; RD Respiratory Disease; REDCap Research Electronic Data Capture; SCT Social
Cognitive Theory; SD Standard Deviation; TVTF Theoretical Validity Testing Framework

•

DELIRIUM IN THE PCICU

33
Appendix B
Models and Frameworks

Figure B1
Social Cognitive Theory

Bandura (1986)

DELIRIUM IN THE PCICU
40
Figure B2
Plan-Do-Study-Act (PDSA) Cycle

(Coury et al., 2017)