Delirium is a complex neuropsychiatric syndrome characterized by an acute onset and fluctuating course of reduced awareness, impairments in attention, and changes in cognition (1). The pathophysiology of this acute brain dysfunction can be conceptualized as a complex interplay between disease-related precipitating factors (e.g., inflammation, severity of illness), patient-specific predisposing factors (e.g., age, cognitive impairment), and environmental factors (e.g., restraints, noise, sleep deprivation) (2). Delirium is not a problem restricted to adults admitted to an ICU but also common in critically ill children. Besides PICU patients, certain populations of children outside the PICU are also at risk for developing PD (e.g., hospitalized children with cancer) (3). The estimated overall prevalence of delirium in critically ill children is 34%, with rates ranging from 17% to 66% depending on the subgroup studied (4). Further, evidence shows that delirium in critically ill children is associated with lengthier hospital stays, excess mortality, cognitive impairment after discharge, and increased costs (5–9). Delirium has been linked to postintensive care syndrome in the year after discharge, with physical problems, cognitive decline and mental health sequelae including anxiety, depression, and posttraumatic stress disorder (10).
Knowledge regarding specific factors associated with pediatric delirium (PD) may help us to better understand patients at highest risk. To date, the majority of PD studies have been single-center or focused on specific patient subgroups. In this systematic review of the literature, we have integrated information from PD studies into a meta-analysis to identify nonmodifiable (e.g., age, developmental delay) and potentially modifiable factors (e.g., mechanical ventilation [MV], use of benzodiazepines) associated with delirium in hospitalized children.
METHODS
We performed this systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis and Checklist for Meta-analyses of Observational Studies guidelines (Supplemental Digital Content 1, https://links.lww.com/PCC/C331) (11,12).
Search Strategy and Selection
We searched Embase, Ovid Medline, Web-of-Science, Cochrane, CIHNAL, and Google Scholar databases for relevant studies published from January 1990, until June 1, 2022. A biomedical information specialist from the medical library of the Erasmus MC—University Medical Centre Rotterdam guided these literature searches. Our search terms included “delirium,” ICU (“intensive care,” “critically ill,” “icu,” “critical care”), “pediatric,” and “risk factor” (for full search strategy, see Supplemental Digital Content 2, https://links.lww.com/PCC/C331). Reference lists were screened for additional studies.
We limited our search to factors associated with delirium occurring during a child’s admission to a PICU or pediatric hospital ward (e.g., pediatric surgery, pediatrics, pediatric oncology or PICU). We did not include studies about postanesthesia emergence delirium (PAED), which is a specific phenotype of delirium occurring within 30 minutes of anesthesia, and commonly treated in the postanesthesia recovery room. The studies used in the analysis had to meet all of the following criteria as well: 1) case-control or cohort design; 2) minimum of one factor associated with PD; 3) use of a validated delirium assessment tool (e.g. cornell assessment of PD [CAPD], pediatric confusion assessment method—ICU [pCAM-ICU], Sophia Observation Withdrawal Symptoms-PD [SOS-PD] scale) (13–15); and 4) age up to 18 years. We excluded reviews, editorials, congress abstracts, or studies that did not report factors associated with PD. No restrictions were imposed on language.
Quality Assessment
The quality of the studies included in the analysis was independently assessed by two reviewers (E.I., C.T.) who used the Newcastle-Ottawa Scale (NOS), and discrepancies were discussed with a third reviewer (M.d.N.). The NOS assesses the quality of design of nonrandomized studies and consists of eight items, divided into three broad criteria: selection, comparability, and either outcome (in cohort studies) or exposure (in case-control studies) (16). Studies were awarded a maximum of one star per item in the patient selection and assessment of outcome categories and a maximum of two stars per item in the comparability of the two-study arms category. Studies were graded on an ordinal star scoring scale. Studies with 7–9 points were considered of high quality, studies with 5–6 points were considered of moderate quality, and studies with 0–4 points were considered of poor quality.
Data Extraction
Data from the articles found by the searches were each independently evaluated by two reviewers (E.I., C.T.) who used a standard checklist to extract information about all factors as well as any additional data, such as geographic region of the study population, study design, sample size, delirium assessment tool, and numbers, frequencies, univariate, or multivariable odds ratios (ORs) with corresponding 95% CIs of the factors.
Statistical Analysis
A meta-analysis was conducted whenever three or more studies examined an associated factor using a consistent measure, and there was adequate information on numbers of cases and control subjects. Data were given as either crude numbers or frequencies for categorical variables. Mean (and sd) or median (and interquartile range [IQR]) were used for continuous data, as appropriate for the distribution. Heterogeneity among studies was tested using the Cochran Q-test of heterogeneity and Higgins and Thompson I2 (17). The degree of heterogeneity was defined as a value of I2: low (25–49%), moderate (50–74%), and high (> 75%) values (17). Based on the crude numbers of cases and controls, we calculated unadjusted ORs (95% CI) for dichotomous variables with the Mantel-Haenszel estimator, using either a fixed-effect or random effects model according to the results of the heterogeneity test (17). We also carried out subgroup analyses on patient populations (e.g., PICU, cardiac, or pediatric oncology patients). Publication bias was evaluated using Begg’s funnel and Egger’s tests only for variables if 10 or more studies of a particular variable were analyzed in the meta-analysis (18–20). A symmetrical funnel arises from a well-balanced dataset, whereas an asymmetrical funnel plot suggests publication bias (19,21).
RESULTS
The initial search retrieved 1,846 citations. After removal of duplicates, 1,051 articles remained. After review of abstracts, 1,020 were excluded because they were review articles, described PAED, or were otherwise irrelevant. Full text review of the remaining 31 articles led to subsequent exclusion of seven articles (Fig. 1). Thus, 24 articles (22–45) were included in our detailed analyses. The characteristics of the 24 pediatric cohort studies are summarized in Supplemental Digital Content 3 (https://links.lww.com/PCC/C331). Two of these 24 studies involved a secondary analysis of an original dataset (27,29). However, these studies were included in the qualitative evaluation of the systematic review, because different factors were analyzed in the secondary analysis than in the original study. In five studies, the numbers of cases and controls in the presentation of associated factors for PD were lacking, resulting in only 19 studies being available for meta-analysis (Fig. 1). In total, 10,465 individual patients were evaluated for factors associated with PD. The children were from eight different countries; 63% were from the United States, and the rest were from other countries (e.g., Germany, The Netherlands, Brazil, Argentina, Turkey, China). The settings varied with 17 studies evaluating general PICU patients (23–29,31–33,35–37,39,41,42,44,45), four studies evaluating pediatric cardiac ICU patients (22,30,34,43), two studies evaluating patients after major surgery (26,27), and three studies evaluating pediatric oncology patients (35,38,40). All 19 studies were of moderate to good methodological quality. The NOS score ranged from 6 to 9, with a median of 9 (IQR 8–9) (Supplemental Digital Content 4, https://links.lww.com/PCC/C331).
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Figure 1.: Preferred Reporting Items for Systematic Reviews and Meta-Analysis flow diagram.
Prevalence of PD
In 19 of the 24 studies (79%), the prevalence of PD was assessed using the CAPD (22–24,26–30,32,34–41,43–45). In one study, PD was assessed by a combination of CAPD and SOS-PD (25), and in two others, the pCAM-ICU was used (31,33). In the final study, PD was assessed by a neuropsychiatric evaluation (42). Overall, the prevalence of PD in the 19 studies was 28% (IQR 18–50%). Median PD rates varied by setting: PICU 23% (IQR 18–46%), n equals to 17; pediatric cardiac ICU (PCICU) 53% (IQR 42–65%), n equals to 4; and pediatric oncology 19% (IQR 13–45%), n equals to 3. Given the small number of studies using tools other than the CAPD, it was not possible to perform a subanalysis of prevalence based on type of screening tool used.
Factors Associated With PD
We identified 54 factors in the univariate analyses. Of these, 15 were studied in three or more studies. In multivariable analyses, 17 studies reported 27 factors (Supplemental Digital Contents 3 and 5, https://links.lww.com/PCC/C331). Seven of these independent factors were described in three or more studies. These were categorized as nonmodifiable factors (i.e., patient-related such as age and developmental delay and severity of illness [e.g., Paediatric Index of Mortality, Paediatric Risk of Mortality score]) and potentially modifiable factors (i.e., use of MV, benzodiazepines or opioids, and anticholinergic drugs [ACDs]). We were able to estimate the pooled OR for nine factors: age (below 2 yr old), developmental delay, MV, benzodiazepines, opioids, steroids, vasoactive drugs, ACD, and restraints (Table 1).
TABLE 1. -
Meta-Analysis of Risk Factors for
Delirium in
Pediatrics
| Risk Factor |
No. of Studies/Total No. of Patients (n/N) |
OR (95% CI) |
p
|
Heterogeneity I
2 (%) |
| Age < 2 yr |
8/4,573 |
2.01 (0.69–5.88) |
0.169 |
94.9 |
| Developmental delay |
5/2,898 |
3.98 (1.54–10.26) |
0.016 |
72.0 |
| Mechanical ventilation |
12/6,460 |
6.02 (4.43–8.19) |
< 0.0001 |
63.9 |
| Benzodiazepines |
14/7,519 |
4.10 (2.48–6.80) |
< 0.0001 |
89.7 |
| Opioids |
12/7,264 |
2.88 (.89–4.37) |
0.0006 |
79.8 |
| Steroids |
6/4,421 |
2.02 (1.47–2.77) |
0.0023 |
48.4 |
| Vasoactive drugs |
7/2,947 |
3.68 (1.17–11.60) |
0.0319 |
78.8 |
| Anticholinergic drug |
6/4,344 |
1.44 (0.58–3.56) |
0.3477 |
85.2 |
| Restraints |
8/3,885 |
4.67 (1.82–11.96) |
0.0061 |
93.1 |
OR = odds ratio.
Heterogeneity: low (25–50%), moderate (50–75%), or high (> 75%).
Patient-Related Factors
Twelve of 24 studies (50%) demonstrated an association between age and PD after multivariable analysis (22,24,26,30,32,35–37,40,41,43,44) (Supplemental Digital Contents 3 and 5, https://links.lww.com/PCC/C331). Nine of these 12 studies found that children under 2 years old had higher rates of PD. Two studies found that older age was associated with PD, that is, in children 2–5 years old (29) and in children greater than or equal to 12 years old (38). However, overall we did not find an association between age under 2 years and greater odds of PD in the pooled analysis (OR 2.01 [95% CI 0.69–5.88]) (24,25,30,32,36,37,41,43).
In five studies, multivariable analysis showed that developmental delay was associated with greater odds of PD (OR 3.31–17.54) (24,30–32,36). In the pooled analysis, premorbid presence of developmental delay was associated with greater odds of PD (OR 3.98 [95% CI 1.54–10.26]) (24,26,30,31,36).
Mechanical Ventilation
Use of MV was associated with great odds of PD in 11 multivariable analyses (Supplemental Digital Contents 3 and 5, https://links.lww.com/PCC/C331). Two studies reported an association between duration of MV and greater odds of PD (range 1.004–1.43) (26,41). Nine other studies described an association between use of MV during PICU admission and greater odds of PD (range 1.63–18.8) (22,24,28,31,32,36,37,43,44). Pooling the studies showed that MV was associated with greater odds of PD (OR 6.02 [95% CI 4.43–8.19]) (22,24,30–32,34,36,37,41–44).
Physical Restraints
In two multivariable analyses, use of physical restraints was associated with higher prevalence of PD (Supplemental Digital Contents 3 and 5, https://links.lww.com/PCC/C331) (24,37). In pooled analysis, physical restraints was associated with greater odds of PD (OR 4.67 [95% CI 1.82–11.96]) (Supplemental Digital Content 6, https://links.lww.com/PCC/C331) (24,30,31,34,37,39,43,44). We did not identify any differences in the overall effect between the PICU versus PCICU settings (Q = 2.96, p = 0.086).
Medication Exposures
In our initial univariate and multivariable analyses, we identified associations between use of various medications (e.g., benzodiazepines, opioids, and steroids) and greater odds of PD (Supplemental Digital Contents 3 and 5, https://links.lww.com/PCC/C331).
In 10 separate multivariable analyses, use of benzodiazepines was associated with greater odds of PD (range 1.06–5.25) (22,24,28,33,35–38,41,43). The pooled analysis also confirmed an overall association between use of benzodiazepines and greater odds of PD (OR 4.10 [95% CI 2.48–6.80]) (22,24,25,30,31,35–41,43,44). We did not identify an effect of patient subgroup (i.e., PICU, PCICU, and oncology) (Fig. 2).
Figure 2.: Forest plot benzodiazepines, comparing subgroups. df = degrees of freedom, OR = odds ratio, PCICU = pediatric cardiac ICU, Ped Onco = pediatric oncology.
Opiates were described as a factor associated with greater odds of PD in six multivariate analyses (OR 1.09–4.6) (Supplemental Digital Content 5, https://links.lww.com/PCC/C331) (28,35,37,38,41,43). This association was confirmed in the pooled analysis (OR 2.88 [95% CI 1.89–4.37]) (24,25,30,35–41,43,44). Again, as with benzodiazepines, we did not identify an effect of patient subgroup (Q = 0.02, p = 0.99) (Fig. 3).
Figure 3.: Forest plot Opioids, comparing subgroups. df = degrees of freedom, OR = odds ratio, PCICU = pediatric cardiac ICU, Ped Onco = pediatric oncology.
Individual studies did not show an association between the use of steroids and the development of PD in univariate analyses (Supplemental Digital Content 3 and Supplemental Digital Content 5, https://links.lww.com/PCC/C331). However, the pooled analysis showed an association between use of steroids and greater odds of developing PD (OR 2.02 [95% CI 1.47–2.77]) (Table 1) (Supplemental Digital Content 6, https://links.lww.com/PCC/C331) (35–40).
In two studies, multivariable analyses showed that exposure to vasoactive drugs was associated with greater odds of developing PD (37,41); this observation was consistent with the pooled analysis (OR 3.68 [95% CI 1.17–11.60]) (Supplemental Digital Content 6, https://links.lww.com/PCC/C331) (25,30,36,37,39,41).
In three studies, multivariable analyses showed that use of ACDs was associated with greater odds of developing PD (range 2.17–3.4) (Supplemental Digital Contents 3 and 5, https://links.lww.com/PCC/C331) (31,36,38). However, pooled analysis failed to identify a statistically significant association between use of ACDs and greater odds of developing PD (OR 1.44 [95% CI 0.58–3.56]) (Supplemental Digital Content 6, https://links.lww.com/PCC/C331) (25,35–38,40).
Publication Bias
Funnel plots failed to show any evidence of publication bias (Supplemental Digital Content 7, https://links.lww.com/PCC/C331).
DISCUSSION
In this systematic review and meta-analysis, which includes more than 10,000 pediatric patients across 24 selected studies, we note an overall prevalence of PD of 28%. Our pooled analyses shows that greater odds of developing PD is associated with the following factors: developmental delay, need for MV, use of physical restraints, and receipt of either benzodiazepines, opiates, steroids, or vasoactive medication.
Our findings are largely consistent with systematic reviews describing factors associated with delirium in adult patients (46). Similar to our pediatric findings, adults who require invasive MV and/or physical restraints have greater odds of developing delirium (46). Benzodiazepine exposure in adults is also associated with the development of delirium, like pediatrics (44).
Data regarding the association between opiate use and the development of delirium in adults are uncertain (46). In the current pediatric dataset, the pooled analysis shows that opiate exposure is associated with an almost three-fold greater odds of developing PD. However, there are many possible confounders (e.g., sample size, severity of illness, dosing) which we were not able to study. Similarly, although steroid use was associated with double the odds of developing PD, the data in adult patients are unclear and vary by population studied (46).
We know that preexisting dementia is linked to delirium in geriatric patients (39), and this observation is often linked to ACD load (47). We wondered whether this mechanism may be analogous to PD in children with baseline developmental disabilities (48). In the current dataset, we found that the presence of developmental delay was associated with a four-fold greater odds of PD. We were unable to tease out in our multivariable models whether there was an interaction between ACD use and developmental delay, and the association with developing PD. That said, more work is needed in this area, particularly as we have failed to exclude the possibility that ACD exposure is associated with 1.5-fold greater odds of developing PD (OR 1.44 [95% CI 0.58–3.56]). Perhaps, future research should focus on the risks of ACD exposure-load specifically in patients with developmental delay.
It is notable that most of the studies in the current systematic review did not define the subtypes of delirium, and none focused on the association between factor and delirium subtype. Three types of delirium are seen in children: hyperactive (“ICU psychosis”), hypoactive (“acute apathy syndrome”), and mixed delirium (49). These types differ in regard to four aspects: clinical presentation/phenotype, epidemiology, treatment response, and outcome (50). Although individual studies may not be powered for subtype analysis, it will be important for future studies to delineate delirium phenotype (51).
A major strength of the current review is that it incorporates the complete body of PD literature, thereby allowing for a large-scale meta-analysis of factors associated with PD. However, there are a number of limitations. First, most studies were single-center PICU studies, and most were undertaken in the United States, which may limit generalizability. Second, methodological and clinical heterogeneity among studies were present because of variation in hospital settings and sample sizes. A meta-regression would have been an ideal method to explore covariates, to account for heterogeneity and to determine associations between factors associated with PD (52,53) (e.g., it is well-known that there is an interaction between factors like MV and benzodiazepines, opioids, and restraint use in patients who are delirious). However, a meta-regression was not possible because of incomplete or missing information about covariates in the included studies. Therefore, this meta-analysis cannot account for confounding between factors. Third, as in all meta-analyses, the possibility of publication bias is a concern. However, publication bias was not detected for any of the pooled PD-associated factors. Fourth, variations in the frequency of assessment for PD across studies may have affected the results. In most of the studies, PD was assessed with the CAPD by caregiving nurses. However, assessments were not confirmed by child psychiatrists. Consequently, the prevalence of PD, as well as the strength of associations, may have been over- or underestimated. Finally—and most importantly—it is important to note that our pooled analyses describe associations between specific factors and odds of development of PD. Such observations do not establish causality (54).
In conclusion, this meta-analysis identified seven demographic and clinical factors associated with greater odds of developing PD. Further large-scale research is needed to account for interactions among these factors and explore delirium prevention strategies in at-risk children.
ACKNOWLEDGMENT
Members of the Dutch Multidisciplinary Pediatric Delirium Guideline Group are as follows: J. Strik, MD, PhD, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands; J.N. Schieveld, MD, PhD, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands; L. ’t Hart, MD, PhD, Emma Children’s Hospital, Amsterdam University Medical Centre, Amsterdam, the Netherlands; S.B. Oude Ophuis, MD, University Medical Centre Utrecht, Utrecht, the Netherlands; H. Knoester MD PhD, Emma Children’s Hospital, Amsterdam University Medical Centre, Amsterdam, the Netherlands; S. N. de Wildt, MD, PhD, Radboud University Medical Center, Nijmegen, the Netherlands; E Ista, RN, PhD, Erasmus MC-Sophia Children’s Hospital, University Medical Center Rotterdam, The Netherland; M. de Neef, RN, MSc, Emma Children’s Hospital, Amsterdam University Medical Centre, Amsterdam, the Netherlands; G. van der Weerden, MSc, University Medical Centre Utrecht, Utrecht, the Netherlands; M. Stevens, Emma Children’s Hospital, Amsterdam University Medical Centre, Amsterdam, the Netherlands; and E. Koomen, MD, University Medical Centre Utrecht, Utrecht, the Netherlands.
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