The association between out of hours burn centre admission and in-hospital outcomes in patients with severe burns

Singer, Yvonne; Gabbe, Belinda; Cleland, Heather; Holden, Dane; Schnekenburger, Marc; Tracy, Lincoln M.

doi:10.1016/j.burns.2022.08.002

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…Indeed, admission time following a burn injury is an important risk factor, and timely treatment of a burn injury, including effective fluid resuscitation, anti-shock therapy, early burn wound management, infection control, and organ protection, is important in preventing sepsis. 22,23…”

Section: Discussionmentioning

confidence: 99%

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns

Liu,

Li,

Zhang

et al. 2024

J Int Med Res

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Objective To compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns. Methods For this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve. Results Of the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns. Conclusions The XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.

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Section: Discussionmentioning

confidence: 99%

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns

Liu,

Li,

Zhang

et al. 2024

J Int Med Res

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“…The challenge of delivering hospital care to achieve equitable outcomes irrespective of when patients arrive at hospital has been the focus of a growing body of research. 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 The issue has become politicized in some jurisdictions, and policy makers, health system managers and clinicians have been increasingly interested in the potential risks involved for patients admitted to hospital after‐hours. 1 …”

Section: Introductionmentioning

confidence: 99%

“…The challenge of delivering hospital care to achieve equitable outcomes irrespective of when patients arrive at hospital has been the focus of a growing body of research. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] The issue has become politicized in some jurisdictions, and policy makers, health system managers and clinicians have been increasingly interested in the potential risks involved for patients admitted to hospital after-hours. 1 The growing body of evidence suggesting outcome differences for patients admitted after-hours remains largely observational, has been described as low in quality, 9 and has matured to include improved risk adjustment methodology, and consideration of patient acuity measures to better understand the often contradictory findings.…”

Section: Introductionmentioning

confidence: 99%

Mortality and readmission differences associated with after‐hours hospital admission: A population‐based cohort study in Queensland Australia

Bell

Boyle

Rolls

et al. 2023

Health Science Reports

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Background and Aims Policy makers and health system managers are seeking evidence on the risks involved for patients associated with after‐hours care. This study of approximately 1 million patients who were admitted to the 25 largest public hospitals in Queensland Australia sought to quantify mortality and readmission differences associated with after‐hours hospital admission. Methods Logistic regression was used to assess whether there were any differences in mortality and readmissions based on the time inpatients were admitted to hospital (after‐hours versus within hours). Patient and staffing data, including the variation in physician and nursing staff numbers and seniority were included as explicit predictors within patient outcome models. Results After adjusting for case‐mix confounding, statistically significant higher mortality was observed for patients admitted on weekends via the hospital's emergency department compared to within hours. This finding of elevated mortality risk after‐hours held true in sensitivity analyses which explored broader definitions of after‐hours care: an “Extended” definition comprising a weekend extending into Friday night and early Monday morning; and a “Twilight” definition comprising weekends and weeknights. There were no significant differences in 30‐day readmissions for emergency or elective patients admitted after‐hours. Increased mortality risks for elective patients was found to be an evening/weekend effect rather than a day‐of‐week effect. Workforce metrics that played a role in observed outcome differences within hours/after‐hours were more a time of day rather than day of week effect, i.e. staffing impacts differ more between day and night than the weekday versus weekend. Conclusion Patients admitted after‐hours have significantly higher mortality than patients admitted within hours. This study confirms an association between mortality differences and the time patients were admitted to hospital, and identifies characteristics of patients and staffing that affect those outcomes.

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The association between out of hours burn centre admission and in-hospital outcomes in patients with severe burns

Cited by 2 publications

References 32 publications

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns

Mortality and readmission differences associated with after‐hours hospital admission: A population‐based cohort study in Queensland Australia

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