A simple tool to predict admission at the time of triage
| Autor: | Allan Cameron, Gerard A. McKay, Kenneth Rodgers, Alastair J Ireland, Ravi Jamdar |
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| Rok vydání: | 2014 |
| Předmět: |
Adult
Male medicine.medical_specialty Cross-sectional study Critical Care and Intensive Care Medicine Logistic regression Risk Assessment Severity of Illness Index Odds Young Adult Patient Admission Severity of illness clinical management Humans Medicine Aged Retrospective Studies business.industry Retrospective cohort study clinical assessment General Medicine Middle Aged Models Theoretical Early warning score medicine.disease Triage clinical assessment effectiveness emergency care systems efficiency Cross-Sectional Studies Logistic Models Emergency medicine Emergency Medicine Female Original Article Medical emergency Emergency Service Hospital business Risk assessment |
| Zdroj: | Emergency Medicine Journal : EMJ |
| ISSN: | 1472-0213 1472-0205 |
| DOI: | 10.1136/emermed-2013-203200 |
| Popis: | Aim To create and validate a simple clinical score to estimate the probability of admission at the time of triage.\ud \ud Methods This was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests.\ud \ud Results 215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p\ud \ud Conclusions This simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments. |
| Databáze: | OpenAIRE |
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