Prediction admission in the older population in the Emergency Department: the CLEARED tool

Brink, A.; Alsma, J.; Brink, H.S.; Gelder, J. de; Lucke, J.A.; Mooijaart, S.P.; Zietse, R.; Schuit, S.C.E.; Lingsma, H.F.

Background: Length of stay (LOS) in the Emergency Department (ED) is correlated with an extended in-hospital LOS and may even increase 30-day mortality. Older patients represent a growing population in the ED and they are especially at risk of adverse outcomes. Screening tools that adequately predict admission could help reduce waiting times in the ED and reduce time to treatment. We aimed to develop and validate a clinical prediction tool for admission, applicable to the aged patient population in the ED.Methods: Data from 7,606 ED visits of patients aged 70 years and older between 2012 and 2014 were used to develop the CLEARED tool. Model performance was assessed with discrimination using logistic regression and calibration. The model was internally validated by bootstrap resampling in Erasmus Medical Center and externally validated at two other hospitals, Medisch Spectrum Twente (MST) and Leiden University Medical Centre (LUMC).Results: CLEARED contains 10 predictors: body...Show moreBackground: Length of stay (LOS) in the Emergency Department (ED) is correlated with an extended in-hospital LOS and may even increase 30-day mortality. Older patients represent a growing population in the ED and they are especially at risk of adverse outcomes. Screening tools that adequately predict admission could help reduce waiting times in the ED and reduce time to treatment. We aimed to develop and validate a clinical prediction tool for admission, applicable to the aged patient population in the ED.Methods: Data from 7,606 ED visits of patients aged 70 years and older between 2012 and 2014 were used to develop the CLEARED tool. Model performance was assessed with discrimination using logistic regression and calibration. The model was internally validated by bootstrap resampling in Erasmus Medical Center and externally validated at two other hospitals, Medisch Spectrum Twente (MST) and Leiden University Medical Centre (LUMC).Results: CLEARED contains 10 predictors: body temperature, heart rate, diastolic blood pressure, systolic blood pressure, oxygen saturation, respiratory rate, referral status, the Manchester Triage System category, and the need for laboratory or radiology testing. The internally validated area under the curve (AUC) was 0.766 (95% CI [0759; 0.780. External validation in MST showed an AUC of o.797 and in LUMC, an AUC of 0.725.Conclusions: The developed CLEARED tool reliably predicts admission in elderly patients visiting the ED. It is a promising prompt, although further research is needed to implement the tool and to investigate the benefits in terms of reduction of crowding and LOS in the ED.Show less

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Prediction admission in the older population in the Emergency Department: the CLEARED tool