Delirium is a common and serious clinical syndrome characterized by fluctuating cognitive dysfunction that affects 20–80% of ICU patients [57, 58]. The risk of delirium relies on the interaction between predisposing and precipitating risk factors [23, 29]. It is associated with increased short- and long-term morbidity and mortality [1–5, 7–9, 11–17]. Thus, a thorough understanding of mitigating and contributing factors is necessary to development of an accurate delirium prediction model for critically ill patients.
The incidence of delirium in this study (36.7%) was consistent with that of some published studies [59, 60], but lower than some other cohorts [2, 15]. The median time to ICU delirium onset was similar to other published studies [61, 62]. Moreover, the seven variables identified on Cox regression analysis were similar to other published reports [60, 63, 64] including: light category (artificial vs. natural), low level of family engagement (< 2 hours at bedside per day), high nurse burnout and anticipated turnover (ATS > 35), application of physical restraints, high nursing carerequirements (> 8 hours in 8 hours shift), ICU LOS > 15 days, and hospital LOS > 15 days. The five varibles noted to be most predictive of developing delirium on CHAID decision tree modeling were AL group and age > 65 years (high risk), APACHE IV score > 15 (moderate risk), and SOFA score ≤ 11 and female sex (low risk). As it pertains to light exposure, loss of NL exposure is associated with circadian rhythm disturbances that may affect delirium incidence and outcomes in the critically ill [26, 65–67]. The connection of NL vs. AL light exposure and delirium incidence has been variably reported [26, 68–70]. This discrepancy may be related to differences in delirium definition, screening method, NL category criteria, and sample size [26].
Beyond grouping by light exposure type, CHAID analysis further identified the female gender, SOFA > 11, and APACHE IV > 15 as a risk factors in the second and third layer of the decision tree model. These factors werelikely not detected in Cox regression analysis because of higher proportion of females in participants and the similar median score of APACHE IV and SOFA in two groups. In fact, one advantage of the CHAID decision tree is thatit can divide the population into subgroups with different characteristics and estimate the prevalence in each subgroup. While, regression analysis examines risk factors throughout the whole population and treats different factors equally [71]. However, we believed both models were clinically reasonable.
According to the Cox regression analysis, high nursing care and use of the physical restraint predisposed patients to 18% and 10% greater risk of delirium, respectively, and it is consistent with the other studies in this field [72, 73]. Physical restraints are often used for critically ill patients to ensure patient safety, ensure safety and prevent the removal of medical equipment (e.g., tracheal tubes) [74]. However, the use of physical restraints in different countries varies considerably. For example, the use of physical restraints in European general ICU populations ranges from 10–50%, 76% in Canada, and up to 87% in American surgical ICUs [75–77]. According to one meta-analysis, the prevalence of physical restraint use in Iranian medical-surgical ICUs was 47.6%, in keeping with the findings of this analysis[78]. Similarly, physical restraint applications havepreviously been identified as an independent risk factor for development of ICU delirium [75, 79]. As restraint use increased two- and three-fold, observed incidence of ICU delirium increased 2.38- and 3.62-fold respectively.
Additionally, the presence of family at bedside for > 2 hours per day (reported as family engagement) was identified as a potential mitigating factor for ICU delirium this study, similar to other published reports [80, 81]. This raises questions about the role that family may play in the care of a critically-ill loved one and presents an opportunity for inquiry as ICU visitation policies have been restricted in many cases during the current COVID-19 pandemic. Current evidence suggests that this may potentially be accomplished in the confines of traditional visiting hours, rather more flexible visitation policies that may contribute to staff burnout [82, 83].
Healthcare provider turnover is an important indicator for care qualityand is widelyused as a measure for health-care system analysis. Burnout and provider turnover may disrupt patient care quality and continuity [41–43, 84]. However, whether provider burnout is linked to patient development of ICU delirium remains unclear. In the current study, provider burnout and intent for job turnover was assessed as regards to its correlation to development of ICU delirium. This study found that delirium risk was higher in patients whose providers had higher rates of burnout and anticipated turnover as measured by ATS scores (HR 0.093, 95% CI: 0.014-0.600, P = 0.013).
To identify factors predictive of delirium recurrence amongst those patients with delirium present on ICU admission, backward logistic regression analysis and CHAID decision tree modeling identified exclusive AL light exposure and age > 65 years as major risk factors in the present study.Similar to prior studies, hospitalization in a room without NL exposure was associated with a 3.24-fold increase in delirium recurrence [26], whereas age > 65 years increased delirium recurrence by 2.19-fold [62]. This may not be entirely surprising, as the elderly may be more susceptible to the effects of metabolic disturbances, hypoxemia, and other stresses imposed by the critically ill state [62]. It remains unclear whether the high levels of nursing requirements associated with increased delirium recurrence are merely a reflection of patients with more severe illness or delirium, or whether it correlates with an as-yet unmeasured risk factor.
This report details the largest study of its type on ICU delirium. More than twenty related factors were analyzed using two different prediction model methods. Nevertheless, this study is not without limitations. First, our prediction model method requires knowledge of the patient’s medical history. In some cases, this may be limited by recall bias, or non-availability of information. Second, it’s related to the inherent limitations of an observational study design.