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Multicentre, prospective observational study of the correlation between the Glasgow Admission Prediction Score and
Dominic Jones1, Allan Cameron2, David J Lowe3
1School of Health and Related Research, University of Sheffield, Sheffield, UK jones.dom17@gmail.com.
Insights
The Glasgow Admission Prediction Score (GAPS) correlates with longer hospital stays, increased 6-month readmissions, and higher mortality. This score can aid in predicting patient outcomes and optimizing hospital resource allocation.
Area of Science:
- Emergency Medicine
- Health Services Research
Background:
- The Glasgow Admission Prediction Score (GAPS) was developed to predict hospital admission at triage.
- External validation is crucial to confirm the score's predictive capabilities in diverse settings.
Purpose of the Study:
- To evaluate the correlation between the Glasgow Admission Prediction Score (GAPS) and hospital length of stay.
- To assess the association of GAPS with 6-month hospital readmission rates.
- To determine the relationship between GAPS and 6-month all-cause mortality.
Main Methods:
- Prospective data collection from 1420 adult patients in two emergency departments.
- GAPS calculated at triage without influencing patient management.
- Survival analysis used to model outcomes at 6-month follow-up.
Main Results:
- Higher GAPS scores were associated with increased hospital length of stay.
- A significant increase in 6-month hospital readmission risk (9.2% per GAPS point) was observed.
- A notable increase in 6-month all-cause mortality risk (9.0% per GAPS point) was demonstrated.
Conclusions:
- The GAPS score is a significant predictor of hospital length of stay, 6-month readmission, and 6-month mortality.
- Beyond predicting admission, GAPS offers valuable insights for inpatient resource allocation and bed planning.
- The findings support the utility of GAPS in clinical decision-making and healthcare management.
Objectives:
To assess whether the Glasgow Admission Prediction Score (GAPS) is correlated with hospital length of stay, 6-month hospital readmission and 6-month all-cause mortality. This study represents a 6-month follow-up of patients who were included in an external validation of the GAPS' ability to predict admission at the point of triage.
Setting:
Sampling was conducted between February and May 2016 at two separate emergency departments (EDs) in Sheffield and Glasgow.
Participants:
Data were collected prospectively at triage for consecutive adult patients who presented to the ED within sampling times. Any patients who avoided formal triage were excluded from the study. In total, 1420 patients were recruited.
Primary Outcomes:
GAPS was calculated following triage and did not influence patient management. Length of hospital stay, hospital readmission and mortality against GAPS were modelled using survival analysis at 6 months.
Results:
Of the 1420 patients recruited, 39.6% of these patients were initially admitted to hospital. At 6 months, 30.6% of patients had been readmitted and 5.6% of patients had died. For those admitted at first presentation, the chance of being discharged fell by 4.3% (95% CI 3.2% to 5.3%) per GAPS point increase. Cox regression indicated a 9.2% (95% CI 7.3% to 11.1%) increase in the chance of 6-month hospital readmission per point increase in GAPS. An association between GAPS and 6-month mortality was demonstrated, with a hazard increase of 9.0% (95% CI 6.9% to 11.2%) for every point increase in GAPS.
Conclusion:
A higher GAPS is associated with increased hospital length of stay, 6-month hospital readmission and 6-month all-cause mortality. While GAPS's primary application may be to predict admission and support clinical decision making, GAPS may provide valuable insight into inpatient resource allocation and bed planning.
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