Quantitative Critical Appraisal

O R I G I N A L A R T I C L E

Delirium in ICU patients following cardiac surgery: An observational study

Silvio Simeone PhD, RN, MSN, Lecturer of Nursing1 | Gianluca Pucciarelli PhD, RN, MSN,

Post Doctoral Research Fellow2 | Marco Perrone RN, Ward Manager1 | Rea Teresa PhD,

RN, MSN, Lecturer of Nursing3 | Gianpaolo Gargiulo RN, MSN, Lecturer of Nursing4 |

Assunta Guillari RN, MSN, PhD Student, Lecturer of Nursing3 | Gaetano Castellano MD,

Anesthetist5 | Luigi Di Tommaso MD, Lecturer of Medical Science6 | Massimo Niola MD,

Professor7 | Gabriele Iannelli MD, Professor8

1Department of Cardiology, Cardiac Surgery

and Emergency, University of Naples

Federico II, Naples, Italy

2Department of Biomedicine and

Prevention, University of Rome Tor

Vergata, Rome, Italy

3Department of Hygiene, University of

Naples Federico II, Naples, Italy

4Pediatric Cardiac Surgery, University of

Naples Federico II, Naples, Italy

5Department of Clinical Neuroscience,

Anaesthesiology, University of Naples

Federico II, Naples, Italy

6Department of Cardiac Surgery, University

of Naples Federico II, Naples, Italy

7Department of Advanced Biomedical

Sciences, University of Naples Federico II,

Naples, Italy

8Department of Cardiology, Cardiac Surgery

and Cardiovascular Emergency, University

of Naples Federico II, Naples, Italy

Correspondence

Gianluca Pucciarelli, Department of

Biomedicine and Prevention, University of

Rome Tor Vergata, Rome, Italy.

Email: [email protected]

Aims and objectives: To observe the clinical and structural factors that can be asso-

ciated with the post-operative onset of delirium in patients who have undergone

heart surgery.

Background: Several risk factors could contribute to the development of delirium,

such as the use of some sedative drugs and a patient’s history with certain types of

acute chronic disease. However, in the literature, there is little knowledge about the

association between delirium in patients who have undergone cardiac surgical inter-

vention and their clinical and environmental predictors.

Design: We used an observational design.

Methods: We enrolled 89 hospitalised patients in the ICU. Patients were first eval-

uated using the Richmond Agitation Sedation Scale and subsequently using the Con-

fusion Assessment Method for the ICU. A linear model of regression was used to

identify the predictors of delirium in patients.

Results: The patients had an average age of 89 years (SD = 6.9), were predomi-

nantly male (84.3%) and were mostly married (79.8%). The majority of patients had

been subjected to bypass (80.9%), while 19.1% had undergone the intervention of

endoprosthesis. The logistic regression model showed that patient age, the duration

of mechanically assisted ventilation, continuous exposure to artificial light and the

presence of sleep disorders were predictors of the onset of delirium.

Conclusion: This study further confirms that clinical aspects such as insomnia and

one’s circadian rhythm as well as structural elements such as exposure to artificial

light are variables that should be monitored in order to prevent and treat the onset

of severe post-operative delirium.

Relevance to clinical practice: Identifying the possible factors that predispose a

patient to the onset of delirium during intensive therapy following cardiac surgery,

it is fundamental to implement interventions to prevent this syndrome.

K E Y W O R D S

cardiosurgery, delirium, environment, ICU, patient, predictors

Accepted: 18 February 2018

DOI: 10.1111/jocn.14324

1994 | © 2018 John Wiley & Sons Ltd wileyonlinelibrary.com/journal/jocn J Clin Nurs. 2018;27:1994–2002.

1 | INTRODUCTION

Delirium is an acute cognitive disorder that manifests itself via fluc-

tuations in cognition and disorganised thoughts (Shadvar, Baastani,

Mahmoodpoor, & Bilehjani, 2013). In the literature, several terms

have been used to describe this syndrome, and it is often incorrectly

defined as intensive therapy syndrome (Pun & Ely, 2007), psychosis

resulting from intensive therapy (Justic, 2000), an acute state of con-

fusion or confusion with an encephalopathy or with neurological def-

icits (McGuire, Basten, Ryan, & Gallagher, 2000).

Despite being a common disorder during intensive therapies

(Koster, Hensens, Schuurmans, & van der Palen, 2011; Salluh et al.,

2010), the literature showed conflicting data regarding its prevalence

rate (Barr et al., 2013; Cavallazzi, Saad, & Marik, 2012; Salluh et al.,

2010) with values between 16%–80% (Barr et al., 2013). This differ-

ence is mainly due to the different investigated populations and to

the different rating scales that are used (Allen & Alexander, 2012).

While this condition remains significantly underdiagnosed (Spronk,

Riekerk, Hofhuis, & Rommes, 2009), it is known that the prevalence

of delirium during the administration of intensive therapies following

cardiac surgery ranges between 23%–52% (Brown, 2014; Koster

et al., 2011), while the incidence between 8%–52% (Brown, 2014;

Rudolph et al., 2010; Schoen et al., 2011).

Associated with disorders of psychomotor activity, delirium can

be classified into several types: (i) hyperactive, (ii) hypoactive or

(iii) mixed (Heriot et al., 2017). Symptoms are used to differentiate

between the several types of delirium; for example, agitation, rest-

lessness and hallucinations can be present in patients with severe

hyperactive delirium (Barr et al., 2013). However, in cases of

hypoactive delirium, the patient often shows an apathetic attitude

that is accompanied by lethargy and drowsiness (Allen & Alexan-

der, 2012), while subjects who demonstrate both attitudes fall into

the category of displaying mixed delirium (Allen & Alexander,

2012).

These events, especially in the case of hyperactive delirium, are

strongly associated with various complications, such as the possibility

of a patient self-removing his/her gold-tracheal probe or the risk of

accidentally removed drains, central venous catheters or bladder

catheters (Arumugam et al., 2017; Crimi & Bigatello, 2012). More-

over, hyperactive delirium can create nonsynchronicity between the

patient and the mechanically assisted ventilation (VAM) system, thus

contributing to a consequent negative prognosis (Bakker, Osse,

Tulen, Kappetein, & Bogers, 2012; Chaput & Bryson, 2012). In the

specific case of cardiac surgery, delirium can be related to such post-

operative complications as respiratory insufficiency (Ely et al., 2004),

sternal problems (Bucerius et al., 2004) and the possibility that the

patient will require further surgery (Horacek, Krnacova, Prasko, &

Latalova, 2016). Complications could have an important impact on

the patient’s quality of life (Basinski, Alfano, Katon, Syrjala, & Fann,

2010). In addition, it could increase the patient’s hospitalisation (She-

habi et al., 2010), healthcare costs (Leslie & Inouye, 2011) and mor-

tality (Brummel & Girard, 2013; Kiely et al., 2009).

2 | BACKGROUND

The early detection, treatment and prevention of delirium are impor-

tant elements of rapid post-operative recovery (Lundstrom et al.,

2005). In a recent literature review (Kalabalik, Brunetti, & El-Srougy,

2014), the authors described several risk factors that may contribute

to the development of delirium, such as the use of some sedative drugs

and a patient’s past history with certain types of acute chronic dis-

eases. Dasgupta and Dumbrell (2006) further identified the following

circumstances to be risk factors: older age, elevated serum levels of

cortisol, poor renal function, a history of diabetes, presurgical demen-

tia, a long-duration intervention and complications during surgery.

Moreover, Bakker et al. (2012) described the following as predisposing

factors of delirium: advanced patient age, a history of diabetes and the

presence of pre-existing cerebrovascular disease. They also argued

that the duration of mechanical ventilation was a possible risk factor.

Other authors have also observed a significant association between

the development of delirium and the duration of heart surgery (Koster,

Hensens, Schuurmans, & van der Palen, 2013), the administration of

an improper sedative and/or the incorrect management of arterial

blood pressure (Brown, 2014). Because of the significantly associated

stress and the complexity of the operational procedure (Ogawa et al.,

2017), patients undergoing cardiac surgery may be at greater risk of

developing delirium during their post-operative care.

However, it is essential to observe that certain risk factors are

not modifiable (e.g., such predisposing factors as the past occurrence

of cognitive deficits), while others are modifiable (e.g., immobility).

This represents an opportunity for intervention. In fact, while non-

modifiable predisposing factors can be used for risk assessment prior

to surgery, causative factors (i.e., those that are modifiable) may be

What is already known about the topic?

• Delirium is a common disorder among patients in ICU.

• Delirium could increase hospitalisation, healthcare cost

and mortality.

• Older age, a history of diabetes, presurgical dementia, a

long-duration intervention and complications during sur-

gery could be risk factors for delirium.

What does this paper contribute to the wider

global clinical community?

• This study further confirms that clinical aspects such as

insomnia and one’s circadian rhythm as well as structural

elements such as exposure to artificial light are variables

that should be monitored in order to prevent and treat

the onset of severe post-operative delirium.

• Identifying clinical and environmental predictors, it is fun-

damental to implement specific intervention to prevent

delirium disorder.

SIMEONE ET AL. | 1995

taken into consideration to improve clinical care and to avoid the

onset of delirium. The recognition of risk factors, then, is a crucial

key to prevention and early identification of delirium. The identifica-

tion of risk factors would allow for the implementation of health

interventions that can improve the post-operative care of a patient

subjected to heart surgery (Jones & Pisani, 2012; Pun & Boehm,

2011). For these reasons, several studies (Bakker et al., 2012;

Brown, 2014; Dasgupta & Dumbrell, 2006; Kalabalik et al., 2014;

Koster et al., 2013) have identified various predictors that may affect

the onset of delirium during post-operative care. Although these

studies have identified risk factors and strategies for improving the

management of delirium in general ICU population, specific studies

are needed to translate these findings into improved management of

delirium in the cardiac surgical ICU. In the literature, there is little

knowledge about the association between delirium in patients who

have undergone cardiac surgical intervention and their clinical and

environmental predictors. Some studies (Brown, 2014; Hollinger,

Siegemund, Goettel, & Steiner, 2015; Kim, Kim, Oh, Park, & Park,

2017; Kumar, Jayant, Arya, Magoon, & Sharma, 2017; Zhang et al.,

2015), conducted on cardiac post-operative population, have identi-

fied several predictors of delirium, such as atrial fibrillation, cognitive

impairment, prolonged surgery duration, post-operative poor quality

of sleep and electrolyte disturbance were associated with delirium.

However, the same authors suggested future research to examine

delirium among this population. Future studies should be focused to

investigate the association between patient’s comorbidities, environ-

mental factors, shortening mechanical ventilation time and delirium.

As suggested by literature (Kukull & Ganguli, 2012), it is not possible

to generalise the prior studies’ results when the subpopulation

changes. Being both general and cardiac ICU population similar but

different at the same time, further studies are needed to identify

delirium predictors in this specific population. The identification of

factors that are not linked solely to the patient but also to the struc-

ture of the operating unit is essential in order to prevent the occur-

rence of this event in patients subjected to cardiac surgery.

For this reason, the aim of this study was to observe the clinical

and structural factors that can be associated with the post-operative

onset of delirium in cardiac surgery patients.

3 | METHODS

3.1 | Ethics committee

Members of the ethics committee of the Federico II Hospital Univer-

sity from which participating patients were recruited approved this

study according to protocol no. 139/17. The study was also

approved by the director of the operating unit where patients were

hospitalised.

3.2 | Design, participants and setting

We used a correlational design. In this study, we enrolled 89 hospi-

talised patients in the ICU Post-Operative Cardiac Surgery unit of

the Federico II Hospital University (Naples). The operating unit was

composed of 12 adult stations, including four stations located near

windows that allowed natural lighting to flow into the patient’s unit.

The other eight stations, however, did not allow for the inflow of

natural light. In these rooms, artificial light was present approxi-

mately 18–20 of 24 hr, while for the remaining 4–6 hr, the artificial

light inside the room was dimmed. Each station was identical, as was

the distance between them and the nurse station.

To be enrolled in this study, the patients had to meet the follow-

ing inclusion criteria: (i) they were ≥18 years old; (ii) have undergone

a heart surgery; (iii) have stayed in the ICU Post-Operative Cardiac

Surgery unit for more than 24 hr; and (iv) correctly speak and under-

stand the Italian language. The following exclusion criteria were also

considered: patients were not included in this study if they (i) had a

history of psychological pathology and/or psychogenic drug use; (ii)

had visual disturbances; (iii) had hearing disorders; or (iv) had a Rich-

mond Agitation Sedation Scale (RASS) score of equal to or less than

four.

Following heart surgery, all patients were evaluated by the

trained nurses. Their consciousness state was daily assessed with

simple and standardised questions that were drawn up in coopera-

tion with the medical staff. Patients who showed signs of disorgan-

ised thoughts were first evaluated using the RASS and subsequently

using the Confusion Assessment Method for the ICU (CAM-ICU).

Patients who had RASS scores between �1 and �3 were cate- gorised into hypoactive delirium. Patients with floating RASS scores

between +4 and �3 in conjunction with a positive screening for delirium were defined as having mixed or alternating delirium.

Patients with a RASS score of �5 (unresponsive to physical and ver- bal stimulus) and �4 (responsive only to physical stimulus) are classi- fied ineligible. If patient’s score ranged between �3 and +4, patient was evaluated with CAM-ICU assessment. The criteria of CAM-ICU

are as follows: (i) an acute change in any cognitive status; (ii) inatten-

tion; (iii) disorganised thoughts; and (iv) an altered level of conscious-

ness. Delirium was considered present when features 1 and 2 were

both present, while at the same time either feature 3 or 4 were dis-

played. Patients with CAM-ICU positive (+) and RASS score positive

(+) were categorised into hyperactive delirium, while patients with

CAM-ICU positive (+) and RASS score negative (�) were categorised into hypoactive delirium.

3.3 | Instruments

In this study, we used the Confusion Assessment Method for the

ICU (Ely et al., 2001) and the RASS (Sessler et al., 2002).

The CAM-ICU is a version of the CAM tool (Inouye et al., 1990),

which was developed on the basis of the DSM-IIIR. It is a tool that

facilitates the rapid exchange of four key criteria. An algorithm

allows for the rapid detection of delirium. These key criteria are as

follows: (i) an acute change in any cognitive status; (ii) inattention;

(iii) disorganised thoughts; and (iv) an altered level of consciousness

(detected via RASS). The CAM-ICU is the most frequently utilised

screening tool that aims to detect delirium in subjects who are both

1996 | SIMEONE ET AL.

mechanically and nonmechanically ventilated (Guenther et al., 2010).

Originally developed to recognise delirium in mechanically ventilated

patients, the CAM-ICU is limited to the context of intensive therapy

and utilises nonverbal means of identification, such as the recogni-

tion of figures, simple logical questions that require a dichotomic

answer (yes or not) and simple controls that facilitate the following

of a very precise algorithm. The CAM-ICU has demonstrated high

sensitivity (93%) and specificity (89%) during the diagnosis of delir-

ium. This instrument is also recommended within the guidelines

(Wei, Fearing, Sternberg, & Inouye, 2008) and by the National Insti-

tute for Health and Care Excellence (NICE, 2010) as the diagnostic

tool of choice for the detection of delirium during intensive therapy.

Valid and reliable tool (Boot, 2012), the scale has also been validated

and translated into the Italian language (Gaspardo et al., 2014). After

a minimum of training, the CAM-ICU could be used by nurses in the

Intensive Care Post-Operative Cardiac Surgery (Inouye et al., 1990).

The RASS (RASS; Ely et al., 2003; Sessler et al., 2002) is used by

physicians who seek to assess the level of consciousness/sedation

and agitation of patients in ICU. The scale is based on an evaluation of

verbal and physical stimuli and thus assesses 10 elements of compo-

sure, each of which is given a specific attribute score. Before one car-

ries out an assessment using the CAM-ICU, it is essential that one

assesses the patient’s level of consciousness via the RASS. RASS score

ranges between �5 (unarousable) and + 4 (combative). Patients with positive RASS score (from +1 to +4) were considered to have hyperac-

tive delirium, while patients with a RASS score ranged between �3 and �1 were considered as having hypoactive delirium. Only patients with a RASS score of ≥�3 can be evaluated using the CAM-ICU, as they have been assessed to show signs of responsiveness. Patients

with a RASS score of �5 (unresponsive to physical and verbal stimu- lus) and �4 (responsive only to physical stimulus) are considerate comatose, and for this reason, as suggested by the authors, these

patients were classified ineligible for delirium evaluation. This allows

to evaluate their clarity of thought and, possibly, the presence of delir-

ium. This detail could explain some of the variations in terms of the

sensitivity of the instrument (Neto et al., 2012).

3.4 | Data analysis

The data were analysed using procedures of descriptive and inferen-

tial statistics. Descriptive statistics were used to calculate the means,

standard deviations, frequencies and percentages of patient sociode-

mographic characteristics as well as the clinical features of the

patients. Demographic and clinical characteristics of the patients that

involved categorical variables were summarised using counts and

percentages (e.g., gender, type of intervention, blood pressure, loca-

tion with regard to sunlight, comorbidities and sleep disorder), while

for continuous variables mean and standard deviation (e.g., age,

VAM duration and length of ICU stay) were used to describe data

distribution. An independent sample t test was used to analyse the

significant differences between delirium/no delirium group and con-

tinuous variables (such as age, the duration of VAM and the length

of hospitalisation). Categorical variables were compared using chi-

square test. The significant continuous and categorical variables were

used in the regression model. Delirium was used as dependent vari-

able, while age, duration of VAM, length of ICU stay, blood pressure

(BP), location with regard to sunlight and sleep disorder were used

as independent variables. The independent variables were adjusted

according to the logistic regression model. A logistic model of regres-

sion was used to identify the predictors of delirium in patients.

Results from logistic regression model are reported in adjusted rela-

tive risk ratios (RRR), 95% confidence intervals and p-values.

4 | RESULTS

4.1 | Sociodemographic characteristics and clinical features of patients

In this study, 89 patients were enrolled. Their sociodemographic and

clinical characteristics are reported in Table 1. The patients had an

average age of 89 years (SD = 6.9), were predominantly male

(84.3%) and were mostly married (79.8%). The majority of patients

had been subjected to bypass (80.9%), while 19.1% had undergone

the intervention of endoprosthesis. Moreover, patients exhibited

comorbidities such as diabetes (39.3%), arrhythmia (37.1%) and renal

disease (6.7%). Additionally, more than half of the recruited sample

also experienced sleep disorders (62.9%). On average, patients expe-

rienced 5.5 days (SD = 2.0) of hospitalisation and were subjected to

VAM for 5.4 hr (SD = 1.6).

TABLE 1 Sociodemographic and clinical characteristics of patients (n = 89)

Characteristics N (%) %

Age (mean, SD) 89 (SD = 6.9)

Gender

Male 75 84.3

Female 14 15.7

Marital status

Married 71 79.8

Widowed 18 20.2

Type of intervention

Bypass 72 80.9

Endoprosthesis 17 19.1

Comorbidities

Diabetes 35 39.3

Arrhythmia 33 37.1

Renal insufficiency 6 6.7

Sleep disorder 56 62.9

VAM duration, in hr (mean, SD) 5.4 (SD = 1.6)

Length of ICU stay, in days (mean, SD) 5.5 (SD = 2.0)

Delirium 65 73.0

Hypoactive delirium 17 26.2

Hyperactive delirium 48 73.8

SIMEONE ET AL. | 1997

4.2 | Differences between patients with and patients without delirium

In Table 2, it is possible to observe the differences in age, duration

of VAM and length of hospitalisation between patients who mani-

fested delirium and those who did not. For instance, patients who

exhibited delirium were significantly older than those who did not

(M = 68.1 years old vs. M = 62.3 years old; p < .001). We also

observed that the duration of VAM (M = 6.0 hr vs. M = 4.9 hr;

p = .003) and the length of hospitalisation (M = 6.1 days vs.

M = 4.9 days; p = .005) were significantly higher in patients who

manifested delirium than in those who did not present with such a

condition.

As described in Table 3, no significant differences were observed

regard to gender between patient with and without delirium (86.1%

vs. 83.0%, p = .774). More Patients with delirium showed higher

blood pressure than 140/90 pressure compared with patients with-

out delirium (66.7% vs. 32.1%, p = .002). In addition, compared to

patients without delirium, more patients with delirium experienced

sleep disorder (86.1% vs. 47.2%, p < .001) and had location with

regard to sunlight (80.6% vs. 67.9%, p = .032).

The variables that were significantly more prevalent between

patient with and without delirium were incorporated into logistic

regression models. Age, duration of VAM and length in ICU stay

were incorporated as continuous variables, while blood pressure,

location with regard to sunlight and sleep disorder as categorical

variables. The regression model showed that patient age (p = .001),

the duration of VAM (p = .025), continuous exposure to artificial

light (p = .034) and the presence of sleep disorders (p = .024) were

predictors of the onset of delirium. The results of the logistic regres-

sion model are reported in Table 4.

5 | DISCUSSION

This study identified not only clinical but also environmental risk fac-

tors that may be responsible for the onset of delirium during inten-

sive therapy following cardiac surgery. Delirium is a frequent

phenomenon that could occur during such treatment (Koster et al.,

2013). Importantly, the onset of this disorder may significantly wor-

sen the patient’s health (Arumugam et al., 2017). In fact, patients

who have this disorder are more likely to self-removing their gold-

tracheal probes and to the accidental removal of drains, central

venous catheters and bladder catheters. Furthermore, such delirium

can create nonsynchronicity between the patient and the mechani-

cally assisted ventilation system.

As reported in the literature (Brown, 2014; Koster et al., 2013),

we observed that delirium manifested in 40.4% of the recruited

patients. Several important results have been emphasised in our

study. Firstly, we observed that age is a risk factor for the onset of

delirium. Differently from our study, other authors (Ouimet, Kava-

nagh, Gottfried, & Skrobik, 2007; Van Rompaey et al., 2009) did not

observe a significant association between delirium and age. It is

important to note, however, that in our study, the sample had a

higher average age. This could explain the differences between our

results and those of the above-mentioned studies. This is confirmed

in the literature, wherein authors (Wass, Webster, & Nair, 2008)

have observed that the likelihood of delirium rises with increasing

age. As described by Wass et al. (2008), in fact, the prevalence of

delirium ranged between 1% in patients with 55 years of age and

14% in patients older than 85 years.

With regard to gender, our findings are in line with those of

other studies (Van Rompaey et al., 2009). There is no significant dif-

ference between male and female patients. In contrast to our find-

ings, however, Kolanowski et al. (2014) observed that women had a

higher chance than men to develop a more severe form of delirium.

Men, as described by authors, may be more resistant to this particu-

lar disorder because they have more cognitive reserve than do

women (Kolanowski et al., 2014). Moreover, Barnes et al. (2005)

showed that due to the greater physiological size of the brain and

greater number and density of neurons, men are more likely than

women to use compensating mechanisms in the face of delirium.

Another element that may predict the delirium is the duration of

VAM. In the literature, however, this is not an unknown phe-

nomenon (Leite et al., 2014; Salluh et al., 2010; Serafim et al., 2012;

Tsuruta et al., 2010). In fact, as described by various authors (Leite

et al., 2014; Ouimet et al., 2007), one complication seen in patients

who have been admitted to an intensive care unit, particularly in

TABLE 2 Independent sample t test for delirium (n = 89)

Characteristics Delirium M (SD)

No delirium M (SD) p-Value

Age (mean, SD) 68.1 (5.6) 62.3 (6.7) <.001***

VAM duration, in hr (mean, SD) 6.0 (1.5) 4.9 (1.5) .003**

Length of ICU stay,

in days (mean, SD)

6.1 (2.2) 4.9 (1.7) .005**

**p < .01; ***p < .001.

TABLE 3 Differences between delirium/no delirium (n = 89)

Characteristics Delirium N (%)

No delirium N (%) v2 p Value

Gender (male) 31 (86.1) 44 (83.0) 0.155 .774

Marital status 22 (61.1) 49 (92.5) 13.052 <.001**

Type of intervention

(bypass)

27 (75.0) 45 (84.9) 1.361 .280

Blood Pressure

(>140/90)

24 (66.7) 17 (32.1) 10.325 .002*

Diabetes 15 (41.7) 20 (37.7) 0.139 .826

TC 14 (38.9) 16 (30.2) 0.726 .846

Location with regard

to sunlight

29 (80.6) 36 (67.9) 9.737 .032*

Arrhythmia 16 (44.4) 17 (32.1) 1.406 .269

Sleep disorder 31 (86.1) 25 (47.2) 13.934 <.001**

TC, temperature.

Comparison were made using chi-square tests for categorical variables.

*p < .01; **p < .001.

1998 | SIMEONE ET AL.

those patients who receive mechanical ventilation, is delirium. In

addition, other authors (Shehabi et al., 2010) observed that delirium

is often associated with a slower weaning from mechanical ventila-

tion and an increase in the length of a patient’s stay in the intensive

care unit. The delirium’s incidence is often lower in those patients

who do not receive mechanical ventilation (Tsuruta et al., 2010; Van

Rompaey et al., 2008). However, the development of delirium in

patients who receive mechanical ventilation has been poorly

described. In several studies (Serafim et al., 2012; Shehabi et al.,

2010), the assessment of delirium was carried out through a single

assessment (and only after the patient had been extubated). How-

ever, this may not be enough to detect the exact moment of delir-

ium’s onset. Leite et al. (2014) observed that roughly 20.6% of

delirium diagnoses occur prior to extubation while only 14.7% occur

after extubation. It must be emphasised that in our study, the aver-

age duration of VAM was definitely low, equal to 5.4 hr after sur-

gery, differently from other studies, wherein the average length of

extubation was between 3 (Shehabi et al., 2010)–6 days (Leite et al.,

2014). Despite the fact that our sample was subjected to a VAM

duration that was shorter than that described in the literature, the

onset of delirium is still statistically associated with the duration of

VAM. In fact, we observed that patients who showed delirium had

an average VAM duration that was significantly higher than those

who did not manifest delirium.

Additionally, in our study, we observed that patients who had

problems with insomnia were more likely to develop delirium. Sleep

is an essential biological function (Watson, Ceriana, & Fanfulla, 2012)

both for physiological reasons (Sharma & Kavuru, 2010) and for

emotional well-being (Krystal, 2012). In intensive care units, patients’

sleep patterns could be modified due to the use of certain sedatives,

the occurrence of environmental noise and the presence of artificial

light put off by medical devices such as monitors and other device

(Beltrami et al., 2015; Pisani et al., 2015). According to the literature,

insomnia disorders reach its highest level in intensive therapy units

(Watson et al., 2012). In fact, during their time in an ICU, patients

often present a pattern of sleep that is prolonged and fragmented in

time (Trompeo et al., 2011). Unlike healthy individuals, patients in

intensive care units may have sleep patterns that are characterised

by lower or zero-phase REM (Trompeo et al., 2011). This could be

one of the reasons why patients who suffer from insomnia are more

likely to develop delirium. In fact, as showed in literature, suffering

from insomnia or having a low REM phase, it could change the mela-

tonin plasmatic level (Mo, Scheer, & Abdallah, 2016). Consequently,

patients, who had lower melatonin levels, are more likely to showed

delirium (Bellapart & Boots, 2012; Mo et al., 2016). In our study, the

location of a patient’s room with regard to available sunlight statisti-

cally represented a predictor with regard to the onset of delirium in

patients. The artificial light may change the patient’s sleep–wake bal-

ance and consequently lead to the development of post-operative

delirium (Bedrosian & Nelson, 2017; Duffy & Czeisler, 2009).

Patients who are positioned close to artificial light sources may have

more difficulty sleeping due to a lowering of their melatonin levels

(Duffy & Czeisler, 2009; Lee, Kim, & Kim, 2014). Exposure to light at

night disrupts the circadian system, as light is one of the first ele-

ments that the body uses to distinguish day from night (Bedrosian &

Nelson, 2017). When exposure to light is obscured or, conversely,

constant, patient’s biological rhythms and behaviours could become

desynchronised, thus leading to negative health consequences (Lam-

bert, Nelson, Jovanovic, & Cerda, 2015).

6 | CONCLUSIONS

Identifying the possible factors that predispose a patient to the onset

of delirium during intensive therapy following cardiac surgery, it is fun-

damental to implement specific interventions to prevent this syn-

drome. Further studies could lead to the correct identification of

subjective and structural factors that predispose to the onset of delir-

ium. In addition, this would allow interested parties to both prevent

delirium and to correctly structure hospitalisation units, thus directing

the final outcome to a reduction or elimination of delirium. Moreover,

this study further confirms that clinical aspects such as insomnia and

one’s circadian rhythm as well as structural elements such as exposure

to artificial light are variables that should be monitored in order to pre-

vent and treat the onset of severe post-operative delirium. As

described in various studies (Bellapart & Boots, 2012; Mo et al., 2016),

patients with severe delirium often experience lowered melatonin

levels. This lowering of melatonin may explain why patients with

TABLE 4 Logistic regression model to predict patient delirium (n = 89)

Delirium

Predictors E SE p-Value RRR (95% CI)

Intercept 16.004 5.069 .002

Age �0.191 0.058 .001** 0.826 (0.738–0.925) Duration of VAM, in hr �0.521 0.232 .025* 0.594 (0.377–0.936) Length of ICU stay, in days �0.290 0.188 .123 0.749 (0.518–1.082) Blood pressure (>140/90) 0.564 0.658 .391 1.758 (0.485–6.381)

Location with regard to sunlight �1.003 0.716 .034* 0.367 (0.090–1.494) Sleep disorder 1.704 0.753 .024* 5.493 (1.255–24.047)

E, estimate; SE, standard error; RRR, relative risk ratio.

*p < .05, **p < .001.

SIMEONE ET AL. | 1999

delirium also exhibit insomnia as a triggering factor. In the future, it will

be necessary to conduct a case–control study to observe whether

patients with a pharmacological contribution of melatonin have the

same likelihood of manifesting delirium as do patients who have not

been administered this pharmacological therapy.

This study has various limitations. First, this study is limited by

its sample size. The sample consisted of 89 subjects, which may not

be an adequate representation of the population. Another limitation

of this study involves the fact that delirium data detection was con-

ducted once daily, rarely twice, so data regarding its frequency could

be biased. We believe that a more constant assessment (and one

that is not spread so thin in terms of time) could provide more infor-

mation about the exact moment of the onset of delirium.

7 | RELEVANCE TO CLINICAL PRACTICE

Delirium is a common disorder among patients during intensive ther-

apies. It is fundamental to clinical practice to identify predictors, not

only related to patient’s clinical and sociodemographic characteristics

but also to environmental characteristics that could increase the

delirium onset. Identifying clinical and environmental predictors, it is

fundamental to implement specific intervention to prevent this disor-

der. This study showed that there are not only clinical predictors,

related to patient’s characteristics, but also environmental predictors,

such as exposure to artificial light, that could impact on patient’s

health. In addition, knowing which patients may be the most at risk

of delirium, it is essential to implement in this population tailor inter-

vention already during patient admission in the ICU.

CONTRIBUTIONS

Concept, design, analysis, writing or revision of the manuscript: SS,

GP, MP, RT, GG, AG, GC, LT, MN and GI. Furthermore, each author

certifies that this material or similar material has not been and will

not be submitted to or published in any other publication before its

appearance in the Journal of Clinical Nursing.

CONFLICT OF INTEREST

The authors declare that they have no conflict of interests.

ORCID

Silvio Simeone http://orcid.org/0000-0001-9266-0185

Gianluca Pucciarelli http://orcid.org/0000-0001-6915-6802

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How to cite this article: Simeone S, Pucciarelli G, Perrone M,

et al. Delirium in ICU patients following cardiac surgery: An

observational study. J Clin Nurs. 2018;27:1994–2002.

https://doi.org/10.1111/jocn.14324

2002 | SIMEONE ET AL.