Incidence of Delirium in Critically Ill Patients With and Without COVID-19

Abstract

Background

It is known that patients with COVID-19 are at high risk of developing delirium. The aim of the study was to compare the incidence of delirium between critically ill patients with and without a diagnosis of COVID-19.

Methods

This is a retrospective study conducted in a southern Brazilian hospital from March 2020 to January 2021. Patients were divided into two groups: the COVID-19 group consisted of patients with a diagnosis of COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR) or serological tests who were admitted to specific ICUs. The non-COVID-19 group consisted of patients with other surgical and medical diagnoses who were admitted to non-COVID ICUs. All patients were evaluated daily using the Intensive Care Delirium Screening Checklist (ICDSC). The two cohorts were compared in terms of the diagnosis of delirium.

Results

Of the 649 patients who remained more than 48 h in the ICU, 523 were eligible for the study (COVID-19 group: 292, non-COVID-19 group: 231). There were 119 (22.7%) patients who had at least one episode of delirium, including 96 (32.9%) in the COVID-19 group and 23 (10.0%) in the non-COVID-19 group (odds ratio [OR] 4.42; 95% confidence interval [CI], 2.69 to 7.26; p < 0.001). Among patients mechanically ventilated for two days or more, the incidence of delirium did not differ between groups (COVID-19: 89/211, 42.1% vs non-COVID-19: 19/47, 40.4%; p = 0.82). Logistic regression showed that the duration of mechanical ventilation was the only independent factor associated with delirium (p = 0.001).

Conclusion

COVID-19 can be associated with a higher incidence of delirium among critically ill patients, but there was no difference in this incidence between groups when mechanical ventilation lasted two days or more.

Keywords

delirium COVID-19 mechanical ventilation intensive care unit

Background

Delirium is a common complication in critically ill patients that is caused by disturbances in neurochemical processes. This acute condition is characterized by difficulty sustaining attention, disorganized thinking, and an altered level of consciousness.¹ A recent systematic review and meta-analysis including 16,595 critically ill patients observed delirium in 31.8% of patients.² Moreover, each day that a patient remains in delirium is associated with an additional 10% risk of death within 6 months.³

Although the incidence of delirium during intensive care unit (ICU) stay due to coronavirus disease 2019 (COVID-19) is still uncertain, it is known that patients with COVID-19 are at high risk of developing delirium. A meta-analysis investigating the psychiatric features of coronavirus infections showed that signs suggestive of delirium are common in the acute stage of SARS, MERS, and COVID-19; however, data on COVID-19 are scarce.⁴ Different case-control and cohort studies involving COVID-19 patients have shown high incidences of psychiatric manifestations that can even exceed 80%.^4–6

The association of COVID-19 with delirium is attributed to different factors, such as the intensity of systemic inflammation and “neuroinflammation,” the occurrence of multiple organ dysfunction, an increased risk of thrombotic events in the brain, deep and prolonged sedation for mechanical ventilation, and social isolation due to restrictive ICU visitation policies.^{4, 5} Within this context, our purpose was to compare the incidence of delirium between critically ill patients with and without a diagnosis of COVID-19.

Methods

Design, Setting and Population

A retrospective study was conducted from March 2020 to January 2021 in four mixed ICUs, with 10 beds each, of a private tertiary hospital in southern Brazil. All patients older than 18 years admitted to the ICU, who remained in the ICU for more than 48 h, without sedation or low dose sedation and Richmond Agitation-Sedation Scale (RASS) + 2 to −1 were eligible. Patients with one of the following criteria were excluded: previous cognitive disorder, delirium on admission, neurological cause for admission, ICU re-admission, deep sedation with RASS −2 to −4 or psychomotor agitation with RASS + 3 to + 5 (making it impossible to assess and apply the delirium scale),^7–9 and incomplete records (Figure 1). The study was approved by the institutional Research Ethics Committee (CAAE No. 51661515.3.0000.5362).

Patients were divided into two groups. The COVID-19 group consisted of patients with a diagnosis of COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR) or serological tests who were admitted to specific ICUs. The non-COVID-19 group was composed of patients with other surgical and medical diagnoses who were admitted to non-COVID ICUs. In the COVID-19 ICUs, face-to-face visits were banned. Whenever possible, daily virtual calls were held between patients and their family and unrestricted smartphone use was allowed. In the non-COVID-19 ICUs, families’ visits were allowed once a day, as well as unrestricted smartphone use.

Delirium Assessment and Definition

Delirium was assessed once a day by trained psychologists using the Intensive Care Delirium Screening Checklist (ICDSC) validated for Portuguese.¹⁰ All data were recorded daily on the electronic health record. The ICDSC consists of eight DSM-IV (Diagnostic and Statistical Manual of Mental Disorders IV) items: level of consciousness, inattention, disorientation, hallucinations, psychomotor activity, speech or mood disturbance, sleep disturbance, and fluctuation of symptoms. Patients who exhibited four of the eight DSM-IV items at least once were classified as having delirium.^{6, 11} Patients who exhibited one to three of the eight DSM-IV items were classified as subsyndromal delirium. Patients who exhibited no of the eight DSM-IV items were classified as having no delirium.^12–14

Variables Analyzed

The patient data were retrieved from the electronic health record using specific software (QlikView BI Dashboard, Qlik Tech International AB). The following clinical characteristics were evaluated: gender, age, Simplified Acute Physiology Score 3 (SAPS 3), type of ICU admission (medical or surgical), diagnosis on admission, infusion of sedative drugs (midazolam, propofol, opioids, ketamine) and neuromuscular blockers, acute clinical alterations during ICU stay, and invasive procedures. The outcomes analyzed were the incidence of delirium and the incidence density of delirium (delirium/1000 patient-days).

Statistical Analysis

Statistical analysis was performed using the MedCalc 16.4.3 software (MedCalc Software bvba, Ostend, Belgium) and the IBM SPSS Statistics 20 package. The Kolmogorov-Smirnov test was applied to assess whether the data were normally distributed. The Student t test was used to compare means and the nonparametric Mann-Whitney test to compare variables with asymmetrical distribution. Categorical variables were expressed as absolute and relative frequencies and were compared by the chi-squared test. The effect of COVID-19 on the incidence density of delirium was calculated by Poisson regression. The 95% confidence intervals are reported for all effect measures. A multivariate logistic regression model was constructed to identify predictors of delirium. All variables with p < 0.10 in the univariate model were entered into the multivariate logistic regression model that included the occurrence of delirium as the dependent variable. An additional multivariate analysis was performed to identify factors associated with delirium exclusively among patients diagnosed with COVID-19. A p value <0.05 indicated statistical significance.

Considering the association of delirium with invasive mechanical ventilation in previous studies,^{1, 15, 16} we also analyzed the incidence of delirium in the population that remained on mechanical ventilation for two days or more.

Results

Of the 649 patients who remained more than 48 h in the ICU, 331 were admitted with COVID-19 and 318 had other diagnoses. One hundred twenty-six patients were not eligible. A total of 523 patients were included in the analysis, 292 in the COVID-19 group and 231 in the non-COVID group (Figure 1). Of these, 216 (41.1%) were female, the median age was 63 (49 to 73) years, and the median SAPS 3 score was 46 (39 to 56). Most baseline patient characteristics differed significantly between groups (Table 1). Patients with COVID-19 were younger and had higher severity scores, fewer previous comorbidities, lower PaO₂/FiO₂ levels on admission, and less kidney and hepatic dysfunction on admission. Invasive procedures were more common in the COVID-19 group. There was also a more frequent and longer use of sedatives, opioids, and neuromuscular blockers in this group. Additionally, the length of stay in the hospital and ICU was higher in the COVID-19 group (p < 0.001), as was the ICU mortality rate (odds ratio [OR] 3.00; 95% confidence interval [CI], 1.89 to 4.77; p < 0.001).

Table 1.

Patient characteristics in the COVID-19 and non-COVID-19 groups.

	Non-COVID-19	COVID-19
Characteristics	(n = 231)	(n = 292)	p value
Age (years), median (IQR)	68 (54–76)	59 (47–71)	<0.001
Age > 65 years, n (%)	126 (54.5)	122 (41.8)	0.003
Female gender, n (%)	101 (43.7)	115 (39.4)	0.31
SAPS 3 score, median (IQR)	42 (34–53)	49 (42–58)	<0.001
SAPS 3 > 50, n (%)	56 (24.2)	120 (41.1)	<0.001
Previous comorbidities, n (%)	193 (83.5)	194 (66.4)	<0.001
Reason for ICU admission, n (%)
Cardiovascular	110 (47.6)	23 (7.8)	<0.001
Gastrointestinal	21 (9.1)	2 (0.7)	<0.001
Respiratory	19 (8.2)	257 (89.0)	<0.001
Trauma	6 (2.6)	1 (0.3)	0.03
Metabolic	21 (9.1)	2 (0.7)	<0.001
Sepsis	26 (11.2)	7 (2.4)	<0.001
Malignancies	18 (7.7)	0 (0.0)	-
Others	29 (7.9)	0 (0.0)	-
PaO₂/FiO₂ on admission, median (IQR)	352 (253–506)	193 (125–270)	<0.001
Vasopressors, n (%)	65 (28.2)	86 (29.5)	0.74
Altered mental status, n (%)	127 (55.0)	153 (52.4)	0.55
Creatinine ≥ 1.2 mg/dL, n (%)	52 (22.1)	35 (12.0)	0.001
Bilirubin ≥ 1.2 mg/dL, n (%)	12 (5.2)	3 (1.0)	0.004
Sodium > 145 mEq/dL, n (%)	28 (12.1)	32 (11.0)	0.67
Sodium < 135 mEq/dL, n (%)	26 (11.3)	28 (9.6)	0.53
Medications, n (%)
Midazolam, n (%)	54 (23.4)	211 (72.3)	<0.001
Days, median (IQR)	4 (2–7)	15 (6–22)	<0.001
Propofol, n (%)	13 (5.6)	105 (36.0)	<0.001
Days, median (IQR)	4 (2–8)	6 (3–11)	0.006
Opioids, n (%)	66 (28.6)	212 (72.6)	<0.001
Days, median (IQR)	3 (1–5)	11 (5–17)	0.004
Ketamine, n (%)	18 (7.8)	59 (20.2)	<0.001
Days, median (IQR)	2 (1–4)	5 (3–8)	0.009
Neuromuscular blocker, n (%)	7 (3.0)	197 (67.5)	<0.001
Days, median (IQR)	1 (1–2)	10 (4–18)	<0.001
Mechanical ventilation, n (%)	113 (48.2)	225 (77.1)	<0.001
Mechanical ventilation ≥ 2 day, n (%)	47 (20.3)	211 (72.2)	<0.001
Duration of central venous catheter (days), median (IQR)	5 (2–9)	24 (11–45)	<0.001
Duration of urinary catheter (days), median (IQR)	3 (2–7)	15 (7–26)	<0.001
Duration of intubation (days), median (IQR)	1 (0–5)	16 (7–27)	<0.001
Surgical procedures*, n (%)	4 (1.7)	6 (2.1)	0.78
LOS in the ICU (days), median (IQR)	4 (3–6)	11 (6–21)	< 0.001
Hospital LOS (days), median (IQR)	7 (4–13)	22 (13–35)	<0.001
ICU mortality, n (%)	29 (12.6)	88 (30.1)	<0.001

FiO₂, fraction of inspired oxygen; ICU, intensive care unit; IQR, interquartile range; LOS, length of stay; PaO₂, partial fraction of oxygen; SAPS 3, Simplified Acute Physiology Score 3.

*Surgical procedures on ICU admission or during ICU stay.

The incidence of delirium was larger in patients with COVID-19 (96/292, 33% vs 23/231, 10%, p < 0.001) and the incidence of subsyndromal delirium did not differ between the two groups (74/292, 25.3% in COVID-19 vs 75/231, 32.5% in non-COVID-19 group, p = 0.07) (Table 2). As shown in Figure 2, 22.7% (119/523) of all patients had at least one episode of delirium, including 32.9% (96/292) in the COVID-19 group and 10.0% (23/231) in the non-COVID-19 group (OR 4.42; 95% CI, 2.69 to 7.26; p < 0.001). The incidence density of delirium was 42.3 per 1000 patient-days in the COVID-19 group compared to 22.2 per 1000 patient-days in the non-COVID-19 group (incidence rate ratio 1.93; 95% CI, 1.15 to 3.25; p = 0.003). Logistic regression showed that duration of mechanical ventilation of two days or more was the only independent factor associated with delirium (OR 5.41; 95% CI, 2.03 to 14.43; p = 0.001).

Table 2.

Frequency of delirium (ICDSC > 3), subsyndromal delirium (ICDSC 1-3) and no delirium (ICDSC 0) in covid-19 and non-covid-19 patients.

	Non-COVID-19 (n = 231)	COVID-19 (n = 292)	P value
ICDSC > 3, n (%)	23 (10)	96 (32.9)	< 0.01
ICDSC 1–3, n (%)	75 (32.5)	74 (25.3)	0.07
ICDSC 0, n (%)	109 (47)	45 (15.4)	< 0.01
Not available (coma, sedation), n (%)	24 (10.4)	77 (26.4)	< 0.01
Total	231	292

ICDSC, Intensive Care Delirium Screening Checklist.

Invasive mechanical ventilation for two days or more was required by 258 (49.3%) patients (COVID-19: 211/292, 72.2% vs non-COVID-19: 47/231, 20.3%; p < 0.001). Among patients mechanically ventilated for two days or more, neither the incidence of delirium (COVID-19: 89/211, 42.1% vs non-COVID-19: 19/47, 40.4%; p = 0.82) nor the incidence density of delirium (COVID-19: 46.1 per 1000 patient-days vs non-COVID-19: 43.2 per 1000 patient-days; p = 0.74) differed significantly between groups, regardless of acute respiratory distress syndrome (ARDS) severity (Table 3).

Table 3.

Data of patients with and without delirium.

Baseline characteristics	With delirium (n = 125)	Without delirium (n = 444)	p value
Age, years	60 (47–72)	64 (49–74)	0.35
Age > 65 years	53 (42.4)	214 (47.6)	0.25
Female gender, n (%)	46 (36.8)	188 (41.8)	0.26
SAPS 3 score	50 (43–57)	45 (37–55)	0.001
SAPS 3 score > 50	52 (41.6)	136 (30.2)	0.02
Previous comorbidities, n (%)	92 (73.6)	325 (72.2)	0.92
PaO₂/FiO₂ on admission	232 (166–357)	211 (135–334)	0.07
Vasopressors, n (%)	46 (36.8)	114 (25.3)	0.01
Altered mental status	82 (65.6)	232 (51.6)	0.008
Creatinine ≥ 1.2 mg/dL	12 (9.6)	79 (17.6)	0.03
Bilirubin ≥ 1.2 mg/dL	5 (4.0)	10 (2.2)	0.28
Sodium > 145 mEq/dL	14 (11.2)	24 (5.3)	0.02
Sodium < 135 mEq/dL	12 (9.6)	46 (10.2)	0.80
Reason for ICU admission, n (%)
Neurologic	26 (20.8)	30 (6.7)	<0.001
Cardiovascular	17 (13.6)	117 (26.4)	0.002
Gastrointestinal	2 (0.8)	21 (4.7)	0.11
Respiratory	56 (44.8)	220 (49.5)	0.42
Trauma	2 (1.6)	5 (1.1)	0.75
Metabolic	13 (10.4)	10 (2.2)	<0.001
Sepsis	5 (4.0)	28 (6.3)	0.32
Malignancies	4 (3.2)	13 (2.9)	0.87
Medications, n (%)
Midazolam, n (%)	99 (79.2)	149 (33.5)	<0.001
days, median (IQR)	13 (6–21)	5 (3–16)	0.03
Propofol, n (%)	80 (64.0)	45 (10.1)	<0.001
days, median (IQR)	6 (2–11)	5 (2–11)	0.41
Fentanyl, n (%)	117 (93.2)	176 (39.6)	<0.001
days, median (IQR)	10 (5–14)	5 (2–10)	0.006
Ketamine, n (%)	39 (31.2)	40 (8.9)	<0.001
days, median (IQR)	4 (3–8)	3 (1–5)	0.48
Neuromuscular blocker, n (%)	86 (68.8)	121 (26.9)	<0.001
days, median (IQR)	8 (3–17)	10 (4–18)	0.16
Mechanical ventilation, n (%)	116 (92.8)	250 (56.3)	<0.001
Mechanical ventilation ≥ 2 days, n (%)	111 (88.8)	163 (36.2)	<0.001
Duration of central venous catheter, days	21 (12–52)	8 (4–29)	<0.001
Duration of urinary catheter, days	18 (9–29)	6 (3–15)	<0.001
Duration of orotracheal tube, days	16 (9–38)	5 (1–17)	<0.001
Surgical procedures*, n (%)	8 (6.4)	4 (0.9)	<0.001
COVID-19	98 (78.4)	121 (34.9)	<0.001

FiO₂, fraction of inspired oxygen; ICU, intensive care unit; IQR, interquartile range;

PaO₂, partial fraction of oxygen; SAPS 3, Simplified Acute Physiology Score 3.

*Surgical procedures at ICU admission or during the ICU stay.

Discussion

Our results showed that COVID-19 can be associated with a higher incidence of delirium among critically ill patients. These findings are consistent with previous reports that suggested an association of COVID-19 with delirium.^4–6 We found a higher global incidence of delirium in the COVID-19 group compared to the control group (32.9% vs 10.0%). However, these results should be interpreted with caution, since there are other factors involved in neurological dysfunction in these patients. In patients with COVID-19, delirium may be a manifestation of direct central nervous system (CNS) invasion, induction of CNS inflammatory mediators, organ dysfunction, sedation, prolonged mechanical ventilation, or environmental factors including social isolation.^17–29 Studies describing the general neurological characteristics of COVID-19 suggest that 20–30% of hospitalized patients will present or develop delirium or mental status changes, increasing to 60–70% in severe cases. However, most are retrospective observational studies, with heterogeneity in the populations studied (comorbidities and patient severity), and multiple tools used for delirium screening with varying sensitivities and specificities.^29–39 For example, in a systematic review and meta-analysis, only 29.2% of the studies examined used validated instruments such as CAM, DOSS, DRS-R-98, MDAS or DSM-5 to identify delirium.³⁶ In our study, we used the ICDSC as a delirium screening tool, that in addition to its accuracy, is also able to identify symptoms of delirium below the clinical threshold, the subsyndromal delirium, characterized by a group of patients with neuropsychiatric symptoms that do not configure the diagnosis of delirium, but have higher mortality and longer hospital stay when compared to patients without delirium.^12–14 Despite the relatively low incidence of delirium among the groups in our study, a portion of the patients presented neuropsychiatric symptoms, configuring a diagnosis of subsyndromal delirium.

The incidence of delirium in the non-COVID-19 group (10.0%) that received family visits once a day is consistent with our previous findings showing an incidence of delirium of 12.1% among critically ill patients submitted to the same visiting policy (compared to 6.7% among patients who received 24-h extended visits).⁴⁰ The high incidence of delirium observed in the COVID-19 group (32.9%) may also be related to family deprivation in this population. In addition, these patients tend to require longer periods of stay in the ICU, which can amplify the effects of deprivation from social life. Since in our study visitation restrictions were due to the diagnosis of COVID-19, it was not possible to identify the degree of contribution of this restriction to the occurrence of delirium. A history of psychiatric disorders and advanced age are factors predisposing to delirium.^5,30 However, individuals with psychiatric disorders were excluded from the analysis. Regarding age, there was no difference between patients with and without delirium and the COVID-19 group was significantly younger.

In general, comparing the COVID patients with non-COVID patients in this cohort is quite difficult, because the patients are so different (as partly evidenced by the fact that a minority in the non-COVID group were ventilated for more than 2 days). In addition, numerous differences in depth of sedation and sedatives used are also significant confounders. We cannot disregard the fact that COVID-19 patients were more severe when compared to the others, which was confirmed by the higher SAPS 3 score observed in the COVID-19 group, as well as the higher proportion of patients needing mechanical ventilation for two days or more. Moreover, the fact that the duration of mechanical ventilation was the only independent variable associated with delirium reinforces the possibility of an effect related to the severity of the critical illness. In the subgroup of mechanically ventilated patients, which accounted for 49.3% of the total sample, the incidence of delirium was similar between COVID-19 and non-COVID-19 patients. This finding suggests that the occurrence of delirium may be more associated with the disease severity and duration of mechanical ventilation than with the diagnosis of COVID-19 itself. An international, multicenter study demonstrated the profound and independent association between the duration of delirium with important clinically significant outcomes, including ventilation time. They showed that the number of delirium days was the most significant predictor of time on mechanical ventilation in non-COVID-19 mechanically ventilated ICU patients.¹⁵ However, these data must be interpreted carefully considering the unequal distribution of mechanically ventilated patients in the two groups. A possible explanation for this finding is that the subgroup of mechanically ventilated patients is more likely to develop delirium because of the severity of the critical illness and systemic inflammation underlying the pulmonary impairment, as well as the more frequent and prolonged infusion of sedatives and opioids.^5,6

Prolonged sedation and the use of benzodiazepines are historically related to the development of delirium among critically ill patients^41,42 and, recently, also among severe COVID-19 patients.^5,6 However, adherence to management like assessment, prevention, and manage pain, spontaneous awakening and breathing trials, choice of analgesia and sedation, delirium assessment, prevention, and management, early mobility and exercise, and family engagement and empowerment can result in 15% improvement in survival and days without coma and delirium.^43,44 The high incidence of delirium observed in the COVID-19 group in our study also may be because of longer hospital and ICU stay, longer mechanical ventilation time, increased use of sedatives (especially midazolam and opioids) and invasive devices (orotracheal tube, urinary catheter, central venous catheter) in this group of patients.

Several reports suggest that level of consciousness, an important component of delirium assessment in the ICDSC (1 of 8 features) screening tool, may also be a critical confounder during delirium recognition efforts.^45–48 Some studies have shown that evaluation results using delirium screening tools, such as CAM-ICU or ICDSC, depend on the level of sedation, being able to measure the effect of sedation and overestimate the prevalence of delirium, especially hypoactive delirium, suggesting that delirium assessments must be standardized regarding precise timing and intensity of sedative administration.^49,50 In the most recent SCCM pain, agitation, delirium early mobility and sleep guidelines, they shows a total of 12,699 delirium assessments (97% involving the CAM-ICU) were evaluated in patients with a RASS between 0 and −2. The likelihood of a positive delirium assessment was significantly greater (77% vs 23%; p < 0.0001) when patients had a RASS −2 (vs a RASS of −1 to 0), which could suggest that level of arousal influences delirium assessments. However, because delirium can present with a decreased arousal level, no inferences can be made from these data. In our study, we evaluated patients without sedation or with low dose of sedatives with a RASS + 2 to −1 that would allow the evaluation and application of the ICDSC. Patients using deep sedation with RASS −2 to −4 or psychomotor agitation with RASS + 3 to + 5 were not analyzed due to the impossibility of evaluation and risk of false positive delirium results.⁵¹ However, the timing of delirium assessment, sedation discontinuation, or the dose of sedation in use at the time of delirium assessment were not recorded, factors that may have overestimated the incidence and prevalence of delirium in our patients.

Our study has others limitations. First, the non-randomized study design limits the interpretation of the results. Second, the data were collected at a single hospital center, a fact that may have biased the results and limited their generalization. Third, the study population was heterogenous and more homogenous subgroups were not analyzed. Finally, predictors of delirium can be influenced by different clinical and therapeutic factors that were not addressed in our study, such as specific comorbidities, sleep deprivation, nutritional deficiency, and drugs administered before the onset of delirium.

Conclusion

Although the diagnosis of COVID-19 can be associated with a higher incidence of delirium among critically ill patients, there was no difference in the occurrence of delirium between groups when mechanical ventilation lasted two days or more. The duration of mechanical ventilation was the only independent factor associated with delirium, but this finding must be confirmed in future studies.

Footnotes

Acknowledgments

Special thanks to all intensivists, intensive care psychologists, and intensive care nurses, to the system analysts Lucimeri Albino and Mauricio Gonçalves for their help in the data extraction from the EHR.

Authors Contributions

G. A. W., R. R. P., and A. R. R. G., had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis, including and especially any adverse effects. M. S. M. and M. R. P. contributed substantially to the investigation, data collection, analysis and interpretation, and the writing of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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