Predictors of Delirium Incidence, Time to Onset, and Recurrence in a Mixed Medical-surgical ICU Population: A Secondary Analysis Using Cox and CHIAD Decision Tree Modeling

Abstract

Background: To address whether in intensive care unit (ICU) patients, which factors correlate with development of delirium (primary outcome), as well as more rapid delirium onset and recurrence (secondary outcomes).

Methods: A retrospective secondary analysis of 4,200 patients was collected from two academic medical centers. Delirium was assessed with the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in all patients. Univariate and multivariate Cox models, logistic regression analysis, and Chi-square Automatic Interaction Detector (CHAID) decision tree modeling were used to explore delirium risk factors.

Results: Increased delirium risk as associated with exposed only to artificial light (AL) hazard ratio (HR) 1.84 (95% CI: 1.66-2.044, P<0.001), physical restraint application 1.11 (95% CI: 1.001-1.226, P=0.049), and high nursing care requirements (>8 hours per 8-hour shift) 1.18 (95% CI: 1.048-1.338, P=0.007). Delirium incidence was inversely associated with greater family engagement 0.092 (95% CI: 0.014-0.596, P=0.012), low staff burnout and anticipated turnover scores 0.093 (95% CI: 0.014-0.600, P=0.013), non-ICU length-of-stay (LOS)<15 days 0.725 (95% CI: 0.655-0.804, P<0.001), and ICU LOS ≤15 days 0.509 (95% CI: 0.456-0.567, P<0.001). CHAID modelling indicated that AL exposure and age <65 years conveyed a high risk of delirium incidence, whereas SOFA score ≤11, APACHE IV score >15 and natural light (NL) exposure were associated with moderate risk, and female sex were associated with low risk. More rapid time to delirium onset correlated with baseline sleep disturbance (P=0.049), high nursing care requirements (P=0.019), and prolonged ICU and non-ICU hospital LOS (P<0.001). Delirium recurrence correlated with age>65 years (HR 2.198; %95 CI: 1.101-4.388, P=0.026) and high nursing care requirements (HR 1.978, 95% CI: 1.096-3.569), with CHAID modeling identifying AL exposure (P<0.001) and age >65 years (P=0.032) as predictive variables.

Conclusion: Development of ICU delirium correlated with application of physical restraints, high nursing care requirements, prolonged ICU and non-ICU LOS, exposure exclusively to AL (rather than natural), less family engagement, and greater staff burnout and anticipated turnover scores. ICU delirium occurred more rapidly in patients with baseline sleep disturbance, and recurrence correlated with presence of delirium on ICU admission, exclusive AL exposure, and high nursing care requirements.

Backgrounds

Delirium is a transient fluctuating global disorder of cognition associated with increased morbidity and mortality [1–4], whose prevalence amongst intensive care unit (ICU) patients may reach 80% [5], with a daily probability up to 14% [6]. ICU delirium may be a predictor of increased complications, prolonged ICU [7] and non-ICU hospital length-of-stay (LOS) [8], increased hospital costs [9, 10], long-term disability [11], long-term cognitive impairment [12, 13], and decreased odds of discharge home [14–16]. Moreover, ICU delirium has been associated with the development of incident neuropsychiatric disorders including depression, anxiety, trauma and stress-related, and neurocognitive disorders [17]. Therefore, delirium prevention, early diagnosis and treatment are important aspects of caring for the critically-ill patient [18, 19]. The mechanism(s) of delirium remains unclear, and no diagnostic laboratory or imaging test is available [20, 21]. The American Psychiatric Association’s Diagnostic and Statistical Manual, 5th edition (DSM-V) defines delirium by: disturbances of (1) attention, (2) cognition, (3) that develops over a short period, (4) differs from baseline, (5) fluctuates, (5) is not otherwise explained by another neurocognitive disorder, and (6) with evidence suggesting a potential cause in the history, physical examination, or laboratory findings [22]. Risk factors are multifactorial and may be divided into patient-related and hospital-related [23]. Patient-related factors include: age, gender, underlying disease, baseline cognitive impairment, illness severity (measured as Acute Physiology and Chronic Health Evaluation (APACHE) IV score), and presence of deliriumatadmission. Hospital-related factors include medications (including sedatives), nursing care, staffburnout and turnover, mechanical ventilation (MV), non-ICU hospital or ICU LOS, isolation, physical restraint application, and artificial vs. natural light exposure [24–26].

This study aims to determine delirium incidence, recurrence rates, and associated risk factors in ICU patients with acute respiratory distress syndrome (ARDS) on MV using univariate and multivariate Cox models, logistic regression analysis, and Chi-square Automatic Interaction Detector (CHAID) decision tree.

Methods

Results

A total of 4,200 subjects were included in the analysis. Demographic and clinical characteristics are presented in Table 1. The mean participant age was 67.25 ± 11.5 years, with more than half with age > 65 years (51.2%) with a female preponderance (58.1%). No significant differences were noted in thise variables between those who did and did not develop delerium. Delirium was identified in 1,540 (36.7%) of patients during the ICU stay. The mean time to delirium recognition was 7.55 ± 1.88 days. The majority of patients did not have documented comorbidities (n = 3,289, 78.3%, Table 1), and comorbidities did not differ between groups (P = 0.102). However, of those 911 with documented comorbidities based on Charlson comorbidty index which includes 217 (23.8%) CHF, 183 (20.1%) malignancy, 145 (15.9%) chronic kidney diseases, 129 (14.1%) chronic liver diseases, 121 (13.2%) diabetes mellitus, 84 (9.2%) chronic pulmonary diseases, 16 (1.7%) hypertensionand 16 (1.7%) artritis.

Non-ICU hospital LOS (P = 0.584) and ICU LOS (P = 0.552) was similar between groups. Illness severity as measured by the APACHE IV and SOFA score was similar between groups (P = 0.357) and (P = 0.305), respectively.

Patients with and without delirium differed significantly in terms of cognitive impairment at the time of admission. Baseline cognitive impairment significantly was higher in patients with delirium (33.7% vs. 7.4%, P < 0.001). Additionally, a greater portion of delirium patients (62.7%) were observed in the AL group (P < 0.001).Other characteristics did not differ significantly between groups (P > 0.05) Table 1.

Discussion

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.

Conclusion

Development of ICU delirium correlated with application of physical restraints, high nursing care requirements, prolonged ICU and non-ICU hospital length-of-stay, exposure exclusively to artificial (rather than natural) lighting, less family engagement, and greater staff burnout and anticipated turnover scores. ICU delirium occurred more rapidly in patients with baseline sleep disturbance, and recurrence correlated with presence of delirium on ICU admission, exclusive artificial light exposure, and high nursing care requirements. Several of these factors are suitable for further study and intervention including natural light exposure, minimizing physical restraint application, and most notably the potential impacts of provider burnout and intent to turnover on patient development of ICU delirium.

Abbreviations

ICU:  Intensive care unit, CAM-ICU: Confusion Assessment Method for the Intensive Care Unit, APACHE: Acute physiology and chronic health evaluation, SOFA: Sequential organ failure assessment, LOS:  Length of stay, SD: Standard deviation, OR: Odds ratio, HR: Hazard ratio, CHAID: Chi-square Automatic Interaction Detector. MV: Mechanical ventilation, PSQI: Pittsburgh sleep quality index.

Declarations

Ethics approval and consent to participate:

The study was approved by the Investigative Review Board at the participating academic medical centers. Study participation was optional for respondents. Informed consent was obtained from the patient, legal guardian or healthcare surrogate or designated healthcare proxy.

Consent for Publication:

Informed consent was obtained from the patient, legal guardian or healthcare surrogate and allowed for both study participation and publication of de-identified aggregate results. There is no data contained within the manuscript from which individual patients or participants may be identified.

Availability of data and material:

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests:

The authors have no conflicts of interest to disclose.

Funding:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors' contributions:

The project was conceptualized and designed by A.VA, F.RB and KMG. Data collection was performed by A.VA and F.RB and K.GM. Data analysis was performed by A.VA, MS, AC.M and SA. Manuscript writing and revision was performed by all authors.

Acknowledgements:

The authors have no specific acknowledgements to disclose.

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