A Systematic Review and Pooled Prevalence of Delirium in Critically Ill Children

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Diarmaid Semple ◽  
Moninne M. Howlett ◽  
Judith D. Strawbridge ◽  
Cormac V. Breatnach ◽  
John C. Hayden
BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e027666 ◽  
Author(s):  
Margarita Cariolou ◽  
Meghan A Cupp ◽  
Evangelos Evangelou ◽  
Ioanna Tzoulaki ◽  
Antonio J Berlanga-Taylor

ObjectivesTo estimate the prevalence of 25-hydroxyvitamin D (25(OH)D) deficiency and investigate its association with mortality in children with acute or critical conditions.DesignSystematic review and meta-analysis of observational studies.Data sourcesPubMed, OVID, Google Scholar and the Cochrane Library searched until 21 December 2018.Eligibility criteriaStudies of children hospitalised with acute or critical conditions who had blood 25(OH)D levels measured.Data extraction and synthesisWe obtained pooled prevalence estimates of 25(OH)D deficiency and ORs for mortality. We calculated 95% CI and prediction intervals and investigated heterogeneity and evidence of small-study effects.ResultsFifty-two studies were included. Of 7434 children, 3473 (47.0%) were 25(OH)D deficient (<50 nmol/L). The pooled prevalence estimate of 25(OH)D deficiency was 54.6% (95% CI 48.5% to 60.6%, I2=95.3%, p<0.0001). Prevalence was similar after excluding smaller studies (51.5%). In children with sepsis (18 studies, 889 total individuals) prevalence was 64.0% (95% CI 52.0% to 74.4%, I2=89.3%, p<0.0001) and 48.7% (95% CI 38.2% to 59.3%; I2=94.3%, p<0.0001) in those with respiratory tract infections (RTI) (25 studies, 2699 total individuals). Overall, meta-analysis of mortality (18 cohort studies, 2463 total individuals) showed increased risk of death in 25(OH)D deficient children (OR 1.81, 95% CI 1.24 to 2.64, p=0.002, I2=25.7%, p=0.153). Four (22.0%) of the 18 studies statistically adjusted for confounders. There were insufficient studies to meta-analyse sepsis and RTI-related mortality.ConclusionsOur results suggest that 25(OH)D deficiency in acute and critically ill children is high and associated with increased mortality. Small-study effects, reverse causation and other biases may have confounded results. Larger, carefully designed studies in homogeneous populations with confounder adjustment are needed to clarify the association between 25(OH)D levels with mortality and other outcomes.Prospero registration numberCRD42016050638.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sunny Singhal ◽  
Pramod Kumar ◽  
Sumitabh Singh ◽  
Srishti Saha ◽  
Aparajit Ballav Dey

Abstract Background Few studies have focused on exploring the clinical characteristics and outcomes of COVID-19 in older patients. We conducted this systematic review and meta-analysis to have a better understanding of the clinical characteristics of older COVID-19 patients. Methods A systematic search of PubMed and Scopus was performed from December 2019 to May 3rd, 2020. Observational studies including older adults (age ≥ 60 years) with COVID-19 infection and reporting clinical characteristics or outcome were included. Primary outcome was assessing weighted pooled prevalence (WPP) of severity and outcomes. Secondary outcomes were clinical features including comorbidities and need of respiratory support. Result Forty-six studies with 13,624 older patients were included. Severe infection was seen in 51% (95% CI– 36-65%, I2–95%) patients while 22% (95% CI– 16-28%, I2–88%) were critically ill. Overall, 11% (95% CI– 5-21%, I2–98%) patients died. The common comorbidities were hypertension (48, 95% CI– 36-60% I2–92%), diabetes mellitus (22, 95% CI– 13-32%, I2–86%) and cardiovascular disease (19, 95% CI – 11-28%, I2–85%). Common symptoms were fever (83, 95% CI– 66-97%, I2–91%), cough (60, 95% CI– 50-70%, I2–71%) and dyspnoea (42, 95% CI– 19-67%, I2–94%). Overall, 84% (95% CI– 60-100%, I2–81%) required oxygen support and 21% (95% CI– 0-49%, I2–91%) required mechanical ventilation. Majority of studies had medium to high risk of bias and overall quality of evidence was low for all outcomes. Conclusion Approximately half of older patients with COVID-19 have severe infection, one in five are critically ill and one in ten die. More high-quality evidence is needed to study outcomes in this vulnerable patient population and factors affecting these outcomes.


2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i31-i32
Author(s):  
D Semple ◽  
M M Howlett ◽  
J D Strawbridge ◽  
C V Breatnach ◽  
J C Hayden

Abstract Introduction Paediatric Delirium (PD) is a neuropsychiatric complication that occurs during the management of children in the critical care environment (Paediatric Intensive Care (PICU) and Neonatal Intensive Care (NICU). Delirium can be classified as hypoactive (decreased responsiveness and withdrawal), hyperactive (agitation and restlessness), and mixed (combined) (1). PD can be assessed using a number of assessment tools. PD has been historically underdiagnosed or misdiagnosed, having many overlapping symptoms with other syndrome such as pain and iatrogenic withdrawal syndrome (2). An appreciation of the extent of PD would help clinicians and policy makers drive interventions to improve recognition, prevention and management of PD in clinical practice. Aim To estimate the pooled prevalence of PD using validated assessment tools, and to identify risk factors including patient-related, critical-care related and pharmacological factors. Methods A systematic search of PubMed, EMBASE and CINAHL databases was undertaken. Eligible articles included observational studies or trials that estimated a prevalence of PD in a NICU/PICU population using a validated PD assessment tool. Validated tools are the paediatric Confusion Assessment Method-ICU (pCAM-ICU), the Cornell Assessment of Pediatric Delirium (CAPD), the PreSchool Confusion Assessment Method for the ICU (psCAM-ICU), pCAM-ICU severity scale (sspCAM-ICU), and the Sophia Observation Withdrawal Symptoms scale Paediatric Delirium scale (SOS-PD) (1). Only full text studies were included. No language restrictions were applied. Two reviewers independently screened records. Data was extracted using a pre-piloted form and independently verified by another reviewer. Quality was assessed using tools from the National Institutes of Health. A pooled prevalence was calculated from the studies that estimated PD prevalence using the most commonly applied tool, the CAPD (1). Results Data from 23 observational studies describing prevalence and risk factors for PD in critically ill children were included (Figure 1). Variability in study design and outcome reporting was found. Study quality was generally good. Using the validated tools prevalence ranged from 10–66% of patients. Hypoactive delirium was the most prevalent sub-class identified. Using the 13 studies that used the CAPD tool, a pooled prevalence of 35% (27%-43% 95%CI) was calculated. Younger ages, particularly less than two years old, sicker patients, particularly those undergoing mechanical and respiratory ventilatory support were more at risk for PD. Restraints, the number of sedative medications, including the cumulative use of benzodiazepines and opioids were identified as risk factors for the development of PD. PD was associated with longer durations of mechanical ventilation, longer stays and increased costs. Data on association with increased mortality risk is limited and conflicting. Conclusion PD affects one third of critical care admissions and is resource intense. Routine assessment in clinical practice may facilitate earlier detection and management strategies. Modifiable risk factors such as the class and number of sedative and analgesic medications used may contribute to the development of PD. Early mobility and lessening use of these medications present strategies to prevent PD occurrence. Longitudinal prospective multi-institutional studies to further investigate the presentations of the different delirium subtypes and modifiable risk factors that potentially contribute to the development of PD, are required. References 1. Semple D (2020) A systematic review and pooled prevalence of PD, including identification of the risk factors for the development of delirium in critically ill children. doi: 10.17605/OSF.IO/5KFZ8 2. Ista E, te Beest H, van Rosmalen J, de Hoog M, Tibboel D, van Beusekom B, et al. Sophia Observation withdrawal Symptoms-Paediatric Delirium scale: A tool for early screening of delirium in the PICU. Australian Critical Care. 2018;31(5):266–73


2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Anab Rebecca Lehr ◽  
Soha Rached-d’Astous ◽  
Melissa Parker ◽  
Lauralyn McIntyre ◽  
Margaret Sampson ◽  
...  

2021 ◽  
Vol 22 (Supplement 1 3S) ◽  
pp. 116-116
Author(s):  
A. Lehr ◽  
S. Rached-D’Astous ◽  
J. Hamid ◽  
M. Parker ◽  
L. Mcintyre ◽  
...  

Author(s):  
Emily Schapka ◽  
Jerica Gee ◽  
John W. Cyrus ◽  
Gregory Goldstein ◽  
Kara Greenfield ◽  
...  

AbstractFluid overload is a common complication of critical illness, associated with increased morbidity and mortality. Pulmonary fluid status is difficult to evaluate clinically and many clinicians utilize chest X-ray (CXR) to identify fluid overload. Adult data have shown lung ultrasound (LUS) to be a more sensitive modality. Our objective was to determine the performance of LUS for detecting fluid overload, with comparison to CXR, in critically ill children. We conducted a systematic review using multiple electronic databases and included studies from inception to November 15, 2020. The sensitivity and specificity of each test were evaluated. Out of 1,209 studies screened, 4 met eligibility criteria. Overall, CXR is reported to have low sensitivity (44–58%) and moderate specificity (52–94%) to detect fluid overload, while LUS is reported to have high sensitivity (90–100%) and specificity (94–100%). Overall, the quality of evidence was moderate, and the gold standard was different in each study. Our systematic review suggests LUS is more sensitive and specific than CXR to identify pulmonary fluid overload in critically ill children. Considering the clinical burden of fluid overload and the relative ease of obtaining LUS, further evaluation of LUS to diagnose volume overload is warranted.


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