Aim We compared two methods of predicting hospital admission from ED triage: probabilities estimated by triage nurses and probabilities calculated by the Glasgow Admission Prediction Score (GAPS).
Methods In this single-centre prospective study, triage nurses estimated the probability of admission using a 100 mm visual analogue scale (VAS), and GAPS was generated automatically from triage data. We compared calibration using rank sum tests, discrimination using area under receiver operating characteristic curves (AUC) and accuracy with McNemar's test.
Results Of 1829 attendances, 745 (40.7%) were admitted, not significantly different from GAPS’ prediction of 750 (41.0%, p=0.678). In contrast, the nurses’ mean VAS predicted 865 admissions (47.3%), overestimating by 6.6% (p<0.0001). GAPS discriminated between admission and discharge as well as nurses, its AUC 0.876 compared with 0.875 for VAS (p=0.93). As a binary predictor, its accuracy was 80.6%, again comparable with VAS (79.0%), p=0.18. In the minority of attendances, when nurses felt at least 95% certain of the outcome, VAS’ accuracy was excellent, at 92.4%. However, in the remaining majority, GAPS significantly outperformed VAS on calibration (+1.2% vs +9.2%, p<0.0001), discrimination (AUC 0.810 vs 0.759, p=0.001) and accuracy (75.1% vs 68.9%, p=0.0009). When we used GAPS, but ‘over-ruled’ it when clinical certainty was ≥95%, this significantly outperformed either method, with AUC 0.891 (0.877–0.907) and accuracy 82.5% (80.7%–84.2%).
Conclusions GAPS, a simple clinical score, is a better predictor of admission than triage nurses, unless the nurse is sure about the outcome, in which case their clinical judgement should be respected.
Statistics from Altmetric.com
Contributors All authors contributed to the design of the study. DJL and AS performed the study. DJL and AC prepared the original draft of the paper revised by GAM and approved by AI and. GAM is the guarantor.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement All information available on request.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.