Dataset Information

Risk of invasive candidiasis with prolonged duration of ICU stay: a systematic review and meta-analysis.

ABSTRACT:

Objective

This study aimed to evaluate the duration of intensive care unit (ICU) stay prior to onset of invasive candidiasis (IC)/candidaemia.

Design

Systematic review and meta-analysis.

Data sources

PubMed, Cochrane, Embase and Web of Science databases were searched through June 2019 to identify relevant studies.

Eligibility criteria

Adult patients who had been admitted to the ICU and developed an IC infection.

Data extraction and synthesis

The following data were extracted from each article: length of hospital stay, length of ICU stay, duration of ICU admission prior to candidaemia onset, percentage of patients who received antibiotics and duration of their antibiotic therapy prior to candidaemia onset, and overall mortality. In addition to the traditional meta-analyses, meta-regression was performed to explore possible mediators which might have contributed to the heterogeneity.

Results

The mean age of patients ranged from 28 to 76 years across selected studies. The pooled mean duration of ICU admission before onset of candidaemia was 12.9 days (95% CI 11.7 to 14.2). The pooled mean duration of hospital stay was 36.3±5.3 days (95% CI 25.8 to 46.7), and the pooled mean mortality rate was 49.3%±2.2% (95% CI 45.0% to 53.5%). There was no significant difference in duration of hospital stay (p=0.528) or overall mortality (p=0.111), but a significant difference was observed in the mean length of ICU stay (2.8 days, p<0.001), between patients with and without Candida albicans. Meta-regression analysis found that South American patients had longer duration of ICU admission prior to candidaemia onset than patients elsewhere, while those in Asia had the shortest duration.

Conclusions

Patients with IC are associated with longer ICU stay, with the shortest duration of ICU admission prior to the candidaemia onset in Asia. This shows a more proactive strategy in the diagnosis of IC should be considered in caring for ICU patients.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC7359383 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk of invasive candidiasis with prolonged duration of ICU stay: a systematic review and meta-analysis.

Zhang Zhidan Z Zhu Ran R Luan Zhenggang Z Ma Xiaochun X

BMJ open 20200712 7

<h4>Objective</h4>This study aimed to evaluate the duration of intensive care unit (ICU) stay prior to onset of invasive candidiasis (IC)/candidaemia.<h4>Design</h4>Systematic review and meta-analysis.<h4>Data sources</h4>PubMed, Cochrane, Embase and Web of Science databases were searched through June 2019 to identify relevant studies.<h4>Eligibility criteria</h4>Adult patients who had been admitted to the ICU and developed an IC infection.<h4>Data extraction and synthesis</h4>The following data ...[more]

PMID: 32660950

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Project description:BackgroundThe duration between a patient's arrival at the Emergency Department (ED) and their actual departure, known as the Emergency Department Length of Stay (EDLOS), can have significant implications for a patient's health. In Ethiopia, various studies have investigated EDLOS, but a comprehensive nationwide pooled prevalence of prolonged EDLOS, which varies across different locations, is currently lacking. Therefore, the objective of this systematic review and meta-analysis is to provide nationally representative pooled prevalence of prolonged EDLOS and identify associated factors.MethodsIn this study, we conducted a comprehensive systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist. We conducted a thorough search of numerous international databases, including PubMed/Medline, SCOPUS, Web of Science, and Google Scholar. The primary outcome was the prevalence of prolonged EDLOS. The secondary outcome was factors affecting the EDLOS. Random-effects model was used to since there was high heterogeneity. We also conducted subgroup analysis and meta-regression to investigate heterogeneity within the included studies. To assess publication bias, we used Egger's regression test and funnel plots. All statistical analyses were performed using STATA version 17.0 software to ensure accurate and reliable findings.ResultWe have identified eight articles that met our inclusion criteria with a total sample size of 8,612 participants. The findings of this systematic review and meta-analysis indicate that the pooled estimate for the prevalence of prolonged EDLOS is 63.67% (95% CI = 45.18, 82.16, I2 = 99.56%, P = 0.0001). The study identified several significant factors associated with prolonged EDLOS, including patients admitted to overcrowded emergency departments (OR = 5.25, 95% CI = 1.77, 15.58), delays in receiving laboratory findings (OR = 3.12, 95% CI = 2.16, 4.49), and delays in receiving radiological results (OR = 3.00, 95% CI = 2.16, 4.16).ConclusionIn this review, the EDLOS was found to be very high. Overcrowding, delays in laboratory test findings, and delays in radiology test results make up the factors that have a statistically significant association with prolonged EDLOS. Given the high prevalence of prolonged EDLOS in this review, stakeholders should work to increase the timeliness of ED services in Ethiopia by proper disposition of non-emergency palliative patients to the appropriate destination, and implementing point-of-care testing and imaging.

Project description:ObjectivesEarly prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization.DesignRetrospective cohort study with split sampling for model training and testing.SettingA single urban academic hospital.PatientsAll hospitalized patients who required ICU care at the Beth Israel Deaconess Medical Center in Boston, MA, from 2001 to 2012.InterventionsNone.Measurements and main resultsAmong eligible 25,947 hospital admissions, we observed 5,504 (21.2%) in which patients died or had ICU length of stay greater than or equal to 7 days. The gradient boosting machine model had the highest discrimination without (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.81-0.84) and with (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.88-0.90) text-derived variables. Both gradient boosting machines and random forests outperformed logistic regression without text data (p < 0.001), whereas all models outperformed logistic regression with text data (p < 0.02). The inclusion of text data increased the discrimination of all four model types (p < 0.001). Among those models using text data, the increasing presence of terms "intubated" and "poor prognosis" were positively associated with mortality and ICU length of stay, whereas the term "extubated" was inversely associated with them.ConclusionsVariables extracted from unstructured clinical text from the first 48 hours of hospital admission using natural language processing techniques significantly improved the abilities of logistic regression and other machine learning models to predict which patients died or had long ICU stays. Learning health systems may adapt such models using open-source approaches to capture local variation in care patterns.

Dataset Information

Risk of invasive candidiasis with prolonged duration of ICU stay: a systematic review and meta-analysis.

Objective

Design

Data sources

Eligibility criteria

Data extraction and synthesis

Results

Conclusions

Publications

Risk of invasive candidiasis with prolonged duration of ICU stay: a systematic review and meta-analysis.

Similar Datasets

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