| 1. |
- Lauque, Dominique, et al.
(författare)
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Length-of-Stay in the Emergency Department and In-Hospital Mortality : A Systematic Review and Meta-Analysis
- 2022
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 12:1
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Forskningsöversikt (refereegranskat)abstract
- The effect of emergency department (ED) length of stay (EDLOS) on in-hospital mortality (IHM) remains unclear. The aim of this systematic review and meta-analysis was to determine the association between EDLOS and IHM. We searched the PubMed, Medline, Embase, Web of Science, Cochrane Controlled Register of Trials, CINAHL, PsycInfo, and Scopus databases from their inception until 14-15 January 2022. We included studies reporting the association between EDLOS and IHM. A total of 11,337 references were identified, and 52 studies (total of 1,718,518 ED patients) were included in the systematic review and 33 in the meta-analysis. A statistically significant association between EDLOS and IHM was observed for EDLOS over 24 h in patients admitted to an intensive care unit (ICU) (OR = 1.396, 95% confidence interval [CI]: 1.147 to 1.701; p < 0.001, I2 = 0%) and for low EDLOS in non-ICU-admitted patients (OR = 0.583, 95% CI: 0.453 to 0.745; p < 0.001, I2 = 0%). No associations were detected for the other cut-offs. Our findings suggest that there is an association between IHM low EDLOS and EDLOS exceeding 24 h and IHM. Long stays in the ED should not be allowed and special attention should be given to patients admitted after a short stay in the ED.
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| 2. |
- Wu, Lijuan, et al.
(författare)
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The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning : An Observational Cohort Study
- 2023
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 12:14
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Tidskriftsartikel (refereegranskat)abstract
- The association between emergency department (ED) length of stay (EDLOS) with in-hospital mortality (IHM) in older patients remains unclear. This retrospective study aims to delineate the relationship between EDLOS and IHM in elderly patients. From the ED patients (n = 383,586) who visited an urban academic tertiary care medical center from January 2010 to December 2016, 78,478 older patients (age ≥60 years) were identified and stratified into three age subgroups: 60-74 (early elderly), 75-89 (late elderly), and ≥90 years (longevous elderly). We applied multiple machine learning approaches to identify the risk correlation trends between EDLOS and IHM, as well as boarding time (BT) and IHM. The incidence of IHM increased with age: 60-74 (2.7%), 75-89 (4.5%), and ≥90 years (6.3%). The best area under the receiver operating characteristic curve was obtained by Light Gradient Boosting Machine model for age groups 60-74, 75-89, and ≥90 years, which were 0.892 (95% CI, 0.870-0.916), 0.886 (95% CI, 0.861-0.911), and 0.838 (95% CI, 0.782-0.887), respectively. Our study showed that EDLOS and BT were statistically correlated with IHM (p < 0.001), and a significantly higher risk of IHM was found in low EDLOS and high BT. The flagged rate of quality assurance issues was higher in lower EDLOS ≤1 h (9.96%) vs. higher EDLOS 7 h
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| 3. |
- Wärnberg Gerdin, Ludvig, et al.
(författare)
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Comparison of emergency department trauma triage performance of clinicians and clinical prediction models : a cohort study in India
- 2020
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Ingår i: BMJ Open. - : BMJ. - 2044-6055. ; 10:2
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Tidskriftsartikel (refereegranskat)abstract
- Objective The aim of this study was to evaluate and compare the abilities of clinicians and clinical prediction models to accurately triage emergency department (ED) trauma patients. We compared the decisions made by clinicians with the Revised Trauma Score (RTS), the Glasgow Coma Scale, Age and Systolic Blood Pressure (GAP) score, the Kampala Trauma Score (KTS) and the Gerdin et al model. Design Prospective cohort study. Setting Three hospitals in urban India. Participants In total, 7697 adult patients who presented to participating hospitals with a history of trauma were approached for enrolment. The final study sample included 5155 patients. The majority (4023, 78.0%) were male. Main outcome measure The patient outcome was mortality within 30 days of arrival at the participating hospital. A grid search was used to identify model cut-off values. Clinicians and categorised models were evaluated and compared using the area under the receiver operating characteristics curve (AUROCC) and net reclassification improvement in non-survivors (NRI+) and survivors (NRI-) separately. Results The differences in AUROCC between each categorised model and the clinicians were 0.016 (95% CI-0.014 to 0.045) for RTS, 0.019 (95% CI-0.007 to 0.058) for GAP, 0.054 (95% CI 0.033 to 0.077) for KTS and-0.007 (95% CI-0.035 to 0.03) for Gerdin et al. The NRI+ for each model were-0.235 (-0.37 to-0.116), 0.17 (-0.042 to 0.405), 0.55 (0.47 to 0.65) and 0.22 (0.11 to 0.717), respectively. The NRI-were 0.385 (0.348 to 0.4), -0.059 (-0.476 to-0.005),-0.162 (-0.18 to-0.146) and 0.039 (-0.229 to 0.06), respectively. Conclusion The findings of this study suggest that there are no substantial differences in discrimination and net reclassification improvement between clinicians and all four clinical prediction models when using 30-day mortality as the outcome of ED trauma triage in adult patients.
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