Geriatrics

Delirium prevalence in geriatric emergency department patients: A systematic review and meta-analysis

a b s t r a c t

Background: In the emergency department, delirium associated with serious adverse outcomes is common in ge- riatric patients. We performed a meta-analysis and estimated the prevalence of delirium and its related factors among Geriatric EMergency department patients.

Methods: PubMed, Embase, Web of Science, Cochrane Library, CINAHL, PsycINFO, and CBM databases were searched before November 7, 2021. The random-effects model was used to estimate the prevalence of delirium. In addition, subgroup analyses were performed based on continent or region, Publication year, age, sample size, and diagnostic criteria or assessment methods.

Results: 30 studies involving 19,534 geriatric patients in the emergency department were included. The overall pooled crude prevalence estimate of delirium was 15.2% [95% confidence interval (CI) 12.5-18.0%]. Subgroup analyses revealed that the region, publication year, age, sample size, and delirium assessment methods were sig- nificantly correlated with the prevalence of delirium. Meta-regression analysis showed that the publication year was positively, while the sample size was negatively associated with the pooled prevalence of delirium.

Conclusion: In the emergency department, delirium is common in geriatric patients. We should pay specific atten- tion to delirium screening, prevention, and treatment in geriatric patients. Overall appropriate interventions should be utilized to reduce the occurrence of delirium and the adverse outcomes.

(C) 2022

  1. Introduction

Delirium is an acute confusional state characterized by decreased attention, awareness, and cognitive performance [1,2]. It’s a common clinical syndrome in geriatric patients in the emergency department, and its incidence increases with patient age, disease severity, and as- sociated complications [3,4]. Studies have shown that delirium is as- sociated with extended hospital stays, decreased physical function and cognitive performance, risk of falls, and increased Medical costs in older patients [5-8]; delirium is also an independent risk fac- tor for death [9]. In addition, studies show that delirium costs pa- tients between $38-$152 billion a year in health care in the United

* Corresponding author at: Department of Nursing, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, 157 Jinbi Road, Xishan District, Kunming, Yunnan 650032, China.

E-mail address: [email protected] (J. Chi).

States [10]. Moreover, it puts enormous strain on the healthcare sys- tem [11], patients, and families [12,13].

Delirium is particularly prevalent in adults over 65 in emergency de- partments [14]. In addition, there is growing evidence that delirium is highly prevalent (7-20%) in emergency department patients, with a missed diagnosis rate of 57-83% [15]. However, no studies have analyzed the reported data together; thus, the prevalence of delirium in geriatric emergency patients remains unclear. Since 2014, several correlation studies on delirium in geriatric patients in the emergency department have been performed [16-19]. These latest research results need to be updated and analyzed to explore the latest evidence on the prevalence of delirium in geriatric patients in the emergency department.

Therefore, the present study was conducted to estimate the preva- lence of delirium in geriatric patients in the emergency department using a systematic review and meta-analysis of currently published studies. Furthermore, this study intends to provide a theoretical basis for the early identification and prevention of delirium by bringing the reference for the rational allocation of Health care resources in the emer- gency department.

https://doi.org/10.1016/j.ajem.2022.05.058

0735-6757/(C) 2022

  1. Methods

This meta-analysis was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (see Appendix 1) [20].

    1. Search strategy

Two investigators independently searched the literature in PubMed, Embase, Web of Science, Cochrane Library, CINAHL, PsycINFO, and CBM databases to collect the relevant studies on delirium in geriatric patients in the emergency department from database establishment to Novem- ber 7, 2021. Search medical subject headings (Mesh) and free words: delirium, deliri*, emergency department, emergency, emerg* et al. We also hand-searched the references of all the included studies and rele- vant systematic reviews to supplement the access the relevant studies. The literature search procedure is shown in Appendix 2.

    1. Inclusion and exclusion criteria

The inclusion criteria were made according to the PICOS acronym as follows: (1).

Participants (P): age 60 and over, patients with delirium according to standardized diagnostic criteria, such as CAM-ICU (the Confusion As- sessment Method for the ICU), CAM (the Confusion Assessment Method), bCAM (The modified Brief Confusion Assessment Method), physician diagnosis, the DSM-5 (Diagnostic and Statistical Manual of Mental disorders criteria), the 4AT (Abbreviated Mental Test-4), with no restriction on the cause and type of delirium, gender, race, or region. Intervention (I): not applicable. Comparison (C): not applicable. Out- comes (O): the prevalence of delirium, or data from which the preva- lence of delirium could be generated, and Study design (S): cross- sectional or cohort studies (only the baseline data of cohort studies were extracted).

Exclusion Criteria: (1) self-reported delirium; (2) meetings, ab- stracts; (3) studies that did not provide the definitive diagnostic criteria for delirium; (4) duplicate publications and unavailability of outcomes.

    1. Study selection

Two investigators independently screened the literature, extracted the data, and cross-checked them according to the inclusion and exclu- sion criteria. Any disagreements were resolved through discussion or consultation with the third investigator. The titles were first read during the literature screening. Then, after excluding irrelevant studies, the ab- stracts and full texts were read for inclusion. If required, the original study’s authors were contacted by mail or telephone to obtain informa- tion that was not reported but was crucial for this study.

    1. Data extraction

Two investigators extracted data independently using a standard- ized pre-defined data-collection spreadsheet in Microsoft Excel 2019. The extracted contents included: (1) study characteristics, including the first author and publication year; (2) participant characteristics, such as sample size, mean age, and proportion of females; (3) study de- sign, study setting, study location; (4) assessment methods for delirium and (6) outcomes: prevalence of delirium. Any disagreements were re- solved through discussion or consultation with the third investigator.

    1. Quality assessment

The methodological quality of the included studies was assessed using a modified version of the Newcastle-Ottawa scale. The scale in- cluded five items: sample representativeness and size, comparability between respondents and non-respondents, determination of delirium,

and statistical quality; studies were categorized to be at low risk of bias (>= 3 points) or high risk of bias (<3 points) (Appendix 2). Two investi- gators independently conducted the quality evaluation of each study. The third investigator resolved any disagreements or differences after discussion and adjudication.

    1. Statistical analysis

Meta-analysis was performed using Stata 12.0 (Version 15, Stata Corp., USA). The random-effects model was used to calculate the pooled prevalence of delirium with a 95% confidence interval (95% CI) [21-23]. The heterogeneity among the included study results was analyzed by the chi-square test (the test level was ? = 0.1). The heterogeneity was quantitatively determined in combination I 2 and I 2 > 50% was used as a criterion to validate inter-study heterogeneity. In the case of statistical heterogeneity among the study results (I 2 < 50%, P > 0.1), the fixed-effect model was used for meta-analysis; when there was sta- tistical heterogeneity among the study results (I 2 > 50%, P <= 0.1), the random-effects model was used for meta-analysis. The significance level of the meta-analysis was set at ? = 0.05.

To further explore Potential causes of heterogeneity, subgroup anal- yses were performed using the geographic locations (North-America/ Europe/Asia/Africa/Oceania), publication year, delirium assessment tool, age (60-75/>75), and sample size using the median splitting method (<=150/>150). In addition, meta-regression analysis was done for publication year, delirium assessment, regions, age, gender, and sample size. The p < 0.10 was used to determine whether covariates could explain the heterogeneity between studies. Sensitivity analyses were conducted using the leave-one-out method. In addition, publica- tion bias was investigated using funnel plots and Egger’s linear regres- sion test. Statistical tests were 2-sided, and significance was set at p < 0.05.

  1. Results
    1. Literature search

4536 relevant studies were initially detected, 609 duplicate studies were removed, and 3806 unrelated studies were excluded by reading the titles and abstracts. 121 studies that may meet the inclusion criteria were read in full, and finally, 30 studies were included [6,7,16-19,24- 47]. The literature screening process and results are shown in Fig. 1.

    1. Characteristics of the studies

Study and participant characteristics are summarized in Table 1.A total of 4536 articles were identified, and ultimately 30 articles covering 19,534 patients were included in this meta-analysis. Ten studies were cross-sectional, 19 were cohort, and 1 was a retrospective chart review study. The included samples were recruited from North America (n = 21), Europe (n = 4), South America (n = 2), Asia (n = 2), and Oceania (n = 1). The sample size ranged from 108 to 3383. The mean age ranged from 71.8 to 86.1 years. All included studies had clear diagnostic criteria for delirium, of which the diagnostic criteria used were CAM-ICU, CAM, b-CAM, physician diagnosis, the DSM-5, and the 4AT.

    1. Study quality

Based on the modified Newcastle-Ottawa Scale for quality assess- ment, the 30 included studies were scored from 2 to 5. Only three stud- ies were found to be of high risk (NOS score < 3), and 27 studies (NOS score >= 3) were of low risk. The details of NOS scores are shown in Ap- pendix 2.

Fig. 1. Literature Screening procedure and results (PRISMA Diagram).

Full-text articles assessed for eligibility (n = 121)

Records screened (n =3927)

Records after duplicates removal (n = 3927)

Additional records identified through other sources (n = 14)

4522 Total search hits:

2268 from PubMed; 1109 from web of science; 185 from the Cochrane Library; 822 from Embase, 66 from CINAHL, 48 from PsycINFO, CBM from 24

Studies included in qualitative synthesis (n = 30), the manual search of included articles (n=9)

Full-text articles assessed for eligibility (n = 30)

Full-text articles excluded (n = 91)

  • Study subjects do not meet (56)
  • Prevalence of delirium not reported (n = 23)

-Conference, comments, abstract, letters to the editor, protocol, guideline (n = 12)

Included

    1. Pooled prevalence of delirium in older adults in the emergency depart- ment

Records excluded (n =3806)

  • Not relevant to study topic (n = 2857)

-Case report (n = 99)

-Animal (n = 394)

-Conference, comments, abstract, letters to the editor, protocol, review (n = 203)

  • Study subjects and outcome do not meet (253)

Eligibility

Screening

Identification

In this meta-analysis, 30 studies reported delirium in geriatric pa- tients in the emergency department. The pooled prevalence of delirium in geriatric patients in the emergency department was 15.2% (95% CI 12.5-18.0%; I2 = 98.0%) (Fig. 2).

    1. Subgroup analysis

As shown in Table 2, the publication year, delirium assessment tool, geographic locations, age, and sample size were significantly associated with the pooled prevalence of delirium. Studies published from 2018 to 2020 also had a higher pooled prevalence of delirium. Geriatric patients from South America had a higher pooled prevalence of delirium than patients from other geographic locations. The combined prevalence of delirium was higher in geriatric patients >75 years of age in the emer- gency department than in those 60-75 years of age. Sample size <=300 had a higher pooled prevalence of delirium. Studies using the DSM-5 had a higher pooled prevalence of delirium (P < 0.001).

    1. Meta-regression analysis

Meta-regression analysis revealed that publication year (Coef. = 0.005, t = 2.20, P = 0. 038) were positively associated with the pooled prevalence of delirium. While the sample size (Coef. = 0.000,

t = -2.39, P = 0.026) was negatively associated with the pooled prev- alence of delirium (Table 3).

    1. Publication bias and sensitivity analysis

The Funnel plot and Egger’s test reveal publication bias in the pooled prevalence of delirium (Egger test: t = 7.01, P < 0.001) (Fig. 3). Sensitiv- ity analyses did not find outliers that could significantly change the pooled prevalence of delirium, indicating that the results of our meta- analysis were statistically stable (Appendix 2).

  1. Discussion
    1. Delirium is common in the emergency department in geriatric patients

Delirium is a common geriatric finding associated with serious adverse consequences. In this study (30 studies, total sample size of 19,534 cases), the prevalence of delirium in geriatric patients in the emergency department was investigated using quantitative analysis methods. The current study reported the 15.2% combined prevalence of delirium in geriatric patients in the emergency depart- ment.

Most geriatric patients in the emergency department are critically ill, with multiple co-morbidities and complex treatment options [48]. Cur- rent research evidence suggests that advanced age, Predisposing factors such as cognitive impairment, dementia, limb dysfunction, sensory

Table 1

Characteristics of the studies included in the meta-analysis.

Author (year) Country Setting Study design Assessment method

for delirium

Population (years)

N Mean age, yearsa

Males, n (%)

Female, n (%)

delirium, % (n/total)

Beland E et al. (2021)

Mailhot T et al. (2020)

Kennedy M et al. (2020)

Daoust R et al. (2020)

Canada Trauma centers, hospital

301

(49.2)

Canada

Trauma centers, hospital

Prospective cohort

CAM

>65

612

delirium: 80.6

(8.8)

Canada

Hospital

Prospective cohort

CAM

>70

108

80.3 (7.0)

USA

Hospital

Prospective cohort

CAM

>65

817

77.7 (8.2)

54

(50.0)

386

(47.0)

Prospective cohort CAM >65 338 77 (8.0) 165

(49.0)

311 (50.8) 11.1 (68/612)

54 (50.0) 27.8 (30/108)

431 (53.0) 27.7 (226/817)

173 (51.0) 12.1 (41/338)

Ohl ICB et al. (2019)

Brazil Hospital Cross-sectional CAM >60 200 71.8 (8.1) 104

(52.0)

96 (48.0) 28.0 (56/200)

Cirbus J et al. (2019)

USA Hospital Prospective cohort bCAM >65 3383 delirious: 75

(68, 83)b

NA delirious: 68

(64.8)

3.1 (105/3383)

Thompson C et al. (2018)

Ritter SRF et al. (2018)

Nguyen PV et al. (2018)

Gagne AJ et al. (2018)

Fallon A et al. (2018)

Evensen S et al. (2018)

Canada Hospital Retrospective chart review CAM >65 688 85 (75, 90)b 187

(27.2)

Brazil Hospital Cross-sectional CAM >60 110 72.2 (8.3) 62

(56.4)

455

Canada

Hospital

Retrospective cross-sectional chart review

Physician diagnosis

>75

1205

83.4 (5.7)

Canada

Hospital

Prospective cohort

CAM

>65

320

76.8 (7.4)

Ireland

Hospital

Prospective cohort

CAM-ICU

>70

198

78.8 (-)

Norway

Hospital

Prospective cohort

The DSM-5

>75

254

86.1 (5.2)

(37.8)

152

(47.7)

96

(48.5)

103

(41.6)

501 (72.8) 27.1 (181/688)

58 (43.6) 28.2 (31/110)

750 (62.2) 19.1 (230/1205)

168 (52.3) 15.3 (49/320)

102 (51.5) 8.6 (17/198)

151 (58.4) 19.3 (49/254)

Han JH et al.

(2017)

Han JH et al. (2017)

Emond M et al. (2017)

Aslaner MA et al. (2017)

Sri-on J et al. (2016)

Bo M et al. (2016)

Kennedy M et al. (2014)

Hare M et al. (2014)

Han JH et al. (2010)

Han JH et al. (2009) a

Han JH et al. (2009) b

Bo M et al. (2005)

Vida S et al. (2006)

Kakuma R et al. (2003)

Hustey FM et al. (2003)

Hustey FM et al. (2002)

Elie M et al. (2002)

Naughton BJ et al. (1995)

USA Hospital Prospective cohort bCAM >65 3383 delirium:75

(68, 83)b

USA

Canada

Hospital

Hospital

Prospective cohort

Retrospective cohort

CAM-ICU

CAM

>65

>65

1084

200

delirium:77 (71, 84)b

78.9 (7.3)

Turkey

Hospital

Prospective cohort

bCAM

>65

822

77 (70,83)

Thailand

Hospital

Prospective cohort

CAM-ICU

>65

232

76 (6.0)

Italy

Hospital

Prospective cohort

The 4AT

>75

330

83.2 (5.4)

USA

Hospital

Prospective cohort

CAM

>65

676

77 (8.0)

Australia

Hospital

Prospective cross-sectional

CAM

>65

320

80 (8.0)

USA Hospital, nursing Prospective cohort CAM-ICU >65 628 75 (69, 81)b

home

USA

Hospital

Prospective cross-sectional

CAM-ICU

>65

303

74 (69, 80)b

USA

Hospital

Prospective cross-sectional

CAM-ICU

>65

341

NA

Italia

Hospital

Prospective cohort

Physician diagnosis

>70

252

82.4 (4.1)

Canada

Hospital

Prospective cohort

CAM

>66

259

NA

Canada

Hospital

Prospective cohort

CAM

>66

1268

80.1 (7.9)

USA

Hospital

Prospective cross-sectional

CAM

>70

271

77.9 (5.8)

USA

Hospital

Prospective cross-sectional

CAM

>70

297

77.9 (6.0)

Canada

Hospital

Prospective cross-sectional

CAM

>65

447

NA

USA

Hospital

Prospective cross-sectional

CAM

>70

188

79.7 (6.5)

37 (35.2)c 493

(45.5)

91

(45.5)

392

(47.7)

98

(42.0)

171

(51.8)

328

(49.0)

142

(44.0)

263

(41.9)

134

(44.2)

146

(42.8)

118

(47.0)

108

(41.7)

440

(34.7)

119

(44.0)

133

(45.0)

203

(45.4)

72

(38.3)

68 (64.8)c 3.1

(105/3383)

591 (54.5) 14.3 (155/1084)

109 (54.5) 18.0 (36/200)

430 (52.3) 33.9 (279/822)

134 (58.0) 12.0 (27/232)

159 (48.2) 15.8 (52/330)

348 (51.0) 9.3 (63/676)

178 (56.0) 7.0 (23/320)

365 (58.1) 17.2 (108/628)

169 (55.8) 8.3 (25/303)

195 (57.2) 11.2 (38/341)

134 (53.0) 11.1 (28/252)

151 (58.3) 39.0 (101/259)

828 (65.3) 8.4 (107/1268)

152 (56.0) 7.0 (19/271)

164 (55.0) 6.0 (17/297)

244 (54.6) 9.6 (43/447)

116 (61.7) 9.6 (18/188)

CAM: the Confusion Assessment Method; CAM-ICU: the Confusion Assessment Method for the ICU; bCAM: The modified Brief Confusion Assessment Method; DSM-5: Diagnostic and Statistical Manual of Mental Disorders criteria; the 4AT: Abbreviated Mental Test-4; NA: not available.

a Mean age as reported by the authors. For the studies of Beland E et al. (2021), Cirbus J et al. (2019), Han JH et al. (2017), and Han JH et al. (2017), mean age refers to the age of delirium patients included as reported by the authors, not the total number of study subjects.

b Median (interquartile range [IQR]) age.

c Median (interquartile range [IQR]) number.

impairment such as visual and auditory, environmental changes and co- existence of multiple diseases [36,37], acute pain, infection, reduced limb movement, Urinary retention or catheterization, alcohol consump- tion, drug use, and psychosocial factors are the main confounding fac- tors [49]. Therefore, medical staff should pay special attention to the

predisposing factors of delirium in geriatric patients during clinical work evaluation in the emergency department for Timely intervention. The findings of Bo et al. showed that the duration of stay in the emer- gency department was associated with an increased risk of delirium in elderly patients [36]. However, due to the limited access to the original

Image of Fig. 2

Fig. 2. The prevalence of delirium in geriatric patients admitted to the Emergency Department.

data of the included studies, future studies should be conducted to fur- ther investigate the relationship between the length of hospital stay with the risk of delirium.

In summary, in the emergency department, delirium in geriatric pa- tients is mainly due to multiple factors. Therefore, the attention of clin- ical nursing staff to delirium should be improved further. Moreover,

Table 2

Subgroup analysis of the prevalence of delirium in geriatric patients admitted to the Emergency Department.

Subgroup

Meta-analysis

Heterogeneity

P-values across subgroups

No. of studies

Prevalence (95% Cl) (%)

I2 (%)

P value

Publication year

1995-2017

18

0.132 (0.096, 0.167)

97.6

< 0.001

< 0.001

2018-2021

12

0.187 (0.122, 0.252)

98.5

< 0.001

Delirium assessment tool

CAM

17

0.166 (0.128, 0.205)

95.9

< 0.001

< 0.001

BCAM

3

0.128 (0.066, 0.190)

99.4

< 0.001

Physician diagnosis

2

0.153 (0.074, 0.231)

91.9

< 0.001

CAM-ICU

6

0.121 (0.093, 0.149)

79.2

< 0.001

The DSM-5

1

0.193 (0.144, 0.242)

_

_

The 4AT

1

0.158 (0.119, 0.197)

_

_

Regions

< 0.001

North-America

21

0.141 (0.112, 0.170)

98.1

< 0.001

South America

2

0.281 (0.231, 0.331)

0

0.970

Europe

4

0.136 (0.091, 0.180)

78.9

0.003

Asia

2

0.230 (0.015, 0.445)

98.5

< 0.001

Oceania

1

0.070 (0.042, 0.098)

_

_

Age

< 0.001

60-75

27

0.149 (0.121, 0.177)

98.0

< 0.001

> 75

3

0.184 (0.164, 0.203)

8.60

0.335

Sample Size

< 0.001

Sample sizes <=300

12

0.174 (0.122, 0.226)

93.9

< 0.001

Sample sizes >300

18

0.140 (0.107, 0.173)

98.6

< 0.001

Note: CAM: the Confusion Assessment Method; bCAM: The modified Brief Confusion Assessment Method; CAM-ICU: the Confusion Assessment Method for the ICU; the DSM-5: the Diagnostic and Statistical Manual of Mental Disorders criteria; the 4AT: Abbreviated Mental Test-4.

Table 3

Multivariate meta-regression analysis demonstrates the strength of covariates in predicting the prevalence of delirium in geriatric patients in the Emergency Department.

Covariates

Coef.

Std. err.

t

P > | t |

95% Conf. interval

Publication year

0.005328

0.0024234

2.20

0.038

0.0003149

0.0103411

Regions

-0.0014041

0.0155846

-0.09

0.929

-0336433

0.0308351

Age

0.0379346

0.0652095

0.58

0.566

-0969615

0.1728306

Sample size

-0.0000498

0.0000208

-2.39

0.026

-0000929

-6.66e-06

Delirium assessment tool

-0.0154384

0.0131487

-1.17

0.252

-0426386

0.0117618

Gender

0.0000774

0.0000836

0.93

0.364

-0000954

0.0002503

Cons

-10.56629

4.860453

-2.17

0.04

-20.62091

-5116813

delirium-related risk factors should be recognized, assessed as early as possible, and personalized measures should be actively implemented to prevent the occurrence of delirium syndrome and improve the prog- nosis of these patients.

    1. The prevalence of delirium varies in geriatric patients in different emer- gency departments

A comprehensive understanding of the factors influencing delirium syndrome in geriatric patients is essential for preventing and treating delirium in the emergency department. However, due to the limitation of the included studies, it was impossible to conduct an analysis based on all factors associated with the prevalence of delirium in geriatric pa- tients and explore the source of inter-study heterogeneity. Our results suggest that age is one of the reasons for the high heterogeneity among the included studies. A recent systematic review study showed advanced age as a risk factor for delirium [50]. This study showed that the prevalence of delirium in geriatric patients in the emergency depart- ment increased with age. Consistent with Zaal et al., advanced age was one of the most important risk factors for delirium syndrome in geriatric patients in the emergency department [51]. This factor may be related to the degenerative changes in these patient’s organ structure, function, and tissues. Moreover, geriatric patients have reduced cerebral blood flow and are prone to hypoxia, leading to neurotransmitter synthesis blockage. In turn, reduced neurotransmitter synthesis causes transmis- sion dysfunction, thereby increasing the risk of delirium [52].

The DSM-5 and the International Statistical Classification of Diseases and Related Health Problems (ICD-10) are the gold standards for diag- nosing delirium. At present, various validated tools are used to help cli- nicians to screen patients for the presence of delirium. For example, CAM-ICU, CAM, physician diagnosis, the DSM-5, and other tools were used to assess delirium in this study. While the included studies had clear, validated methods or assessment tools for delirium, the diversity

Image of Fig. 3

Fig. 3. Funnel plot.

of methods or assessment tools might have contributed to the increased heterogeneity. Moreover, due to the heavy emergency department workload and tasks, health care providers often consider other life- threatening emergency medical problems more important than the de- lirium intervention [53,54]. Therefore, emergency department medical staff may not take delirium as a key focus in the treatment and nursing process. However, in geriatric patients with critical conditions, com- bined with a variety of underlying diseases, the delirium easily remains masked, which reduces the attention of medical staff towards it. As a re- sult, in the emergency department, delirium status is not effectively managed in up to 80% of geriatric patients, which loses the appropriate time for Delirium management [55]. In clinical practice, medical staff should pay more attention to delirium and achieve early evaluation, de- tection, diagnosis, and treatment of delirium in geriatric patients in the emergency department. Besides, the personalized intervention should be implemented to optimize the delirium management pathway and reduce the prevalence of delirium in geriatric patients in the emergency department.

Our study showed that the prevalence of delirium in geriatric pa- tients in emergency departments was higher in South America than in other regions; the finding was consistent with the observation of Ohl et al. [24]. The high prevalence may be due to differences in the socio- economic status of the different regions. The study by Vandelaar et al. showed a higher prevalence of delirium in geriatric patients in the emergency department in the Korean region than in the United States due to inconsistent delirium assessment tools. Future studies should use uniform criteria to assess the state of delirium in an older patient ad- mitted to the emergency department in different regions for early de- tection and prompt intervention.

  1. Strengths and limitations

This study is the first systematic review to investigate the prevalence of delirium in geriatric patients in the emergency department using a quantitative analysis method. First, the modified NOS score of the in- cluded studies ranged from 2 to 5, suggesting that the overall quality of the included studies was high and ensured the reliability and authen- ticity of the conclusions of this study. Second, multiple databases were searched in this study to ensure the broad representation of the articles and improve the stability of the findings.

This study had some limitations. First, diagnostic criteria for delirium were inconsistently defined, which might have resulted in large varia- tions in estimates of delirium in geriatric patients in the emergency de- partment. Due to the limitations of the data included in the study, the impact of delirium subtype, disease severity, and gender on the preva- lence of delirium in geriatric patients in the emergency department could not be explored. In addition, the sample size of the included stud- ies was relatively small. Moreover, given the apparent heterogeneity, the pooled results should be interpreted with caution.

  1. Conclusion

This study shows that delirium is common in geriatric patients in the emergency department. Therefore, it is vital to identify individuals with possible delirium in geriatric patients in the emergency department in

clinical practice. Future studies are required to investigate the preva- lence of delirium of different severities in geriatric patients in the emer- gency department, with the impact of gender, age, and other factors. The information will provide a basis for developing targeted preventive measures to manage delirium associated with geriatric patients in the emergency department.

Author contributions

CF and CJT contributed to study concept and design, acquisition of the data.

CJT, FLD, LY and CF contributed to analysis and interpretation of the data.

CF, LLB, and WYT contributed statistical expertise.

CF, LLB, WYT, LY, FLD and CJT contributed to drafting and critical revision of the manuscript.

CJT takes responsibility for the paper as a whole.

Financial support

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

CRediT authorship contribution statement

Fei Chen: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. Libo Liu: Methodology, Investigation, Formal analysis, Data curation. Yetong Wang: Methodology, Investiga- tion, Formal analysis, Data curation. Ying Liu: Validation, Supervision, Conceptualization. Luodan Fan: Validation, Supervision, Conceptualiza- tion. Junting Chi: Writing – review & editing, Writing – original draft, Validation, Supervision, Conceptualization.

Declaration of Competing Interest

None.

Acknowledgements

The authors would like to thank all the reviewers who participated in the review, as well as MJEditor (www.mjeditor.com) for providing English editing services during the preparation of this manuscript.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.ajem.2022.05.058.

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