Performance of externally validated enhanced computer-aided versions of the National Early Warning Score in predicting mortality following an emergency admission to hospital in England: a cross-sectional study
| Autor: | Andy J. Scally, Muhammad Faisal, Robin Howes, Donald Richardson, Kevin Beatson, Mohammed A Mohammed |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Predicting mortality medicine.medical_specialty Cross-sectional study Performance statistics & research methods Vital signs National Early Warning Score Logistic regression Health informatics decision making health & safety 03 medical and health sciences Patient Admission 0302 clinical medicine Humans Medicine Hospital Mortality 030212 general & internal medicine health informatics Original Research Aged Aged 80 and over Data collection Computers business.industry General Medicine Middle Aged Models Theoretical Early warning score Cross-Sectional Studies Blood pressure England Computer-aided Early Warning Score Emergency medicine Female Health Services Research Emergency Service Hospital business 030217 neurology & neurosurgery |
| Zdroj: | BMJ Open |
| Popis: | ObjectivesIn the English National Health Service, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient’s risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS).DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (YH—York Hospital for model development; NH—Northern Lincolnshire and Goole Hospital for external model validation).ParticipantsAdult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2).ResultsThe risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups.ConclusionsAn externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems. |
| Databáze: | OpenAIRE |
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