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dc.contributor.authorFaisal, Muhammad
dc.contributor.authorRichardson, D.
dc.contributor.authorScally, Andy J.
dc.contributor.authorHowes, R.
dc.contributor.authorBeatson, K.
dc.contributor.authorMohammed, Mohammed A.
dc.date.accessioned2020-08-25T12:12:27Z
dc.date.accessioned2020-09-14T09:18:16Z
dc.date.available2020-08-25T12:12:27Z
dc.date.available2020-09-14T09:18:16Z
dc.date.issued2019-11
dc.identifier.citationFaisal M, Richardson, D, Scally A et al (2019) 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. BMJ Open. 9(11): e031596.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18010
dc.descriptionYesen_US
dc.description.abstractOBJECTIVES: In 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). DESIGN: Logistic regression model development and external validation study. SETTING: Two acute hospitals (YH-York Hospital for model development; NH-Northern Lincolnshire and Goole Hospital for external model validation). PARTICIPANTS: Adult (≥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). RESULTS: The 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. CONCLUSIONS: An 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.en_US
dc.description.sponsorshipThis research was supported by the Health Foundation. The Health Foundation is an independent charity working to improve the quality of healthcare in the UK. This research was also supported by the National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre (YHPSTRC).en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1136/bmjopen-2019-031596en_US
dc.rights© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.en_US
dc.subjectDecision makingen_US
dc.subjectHealth informaticsen_US
dc.subjectHealth and safetyen_US
dc.subjectStatistics and research methodsen_US
dc.titlePerformance 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 studyen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-09-18
dc.date.application2019-11-02
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.date.updated2020-08-25T11:12:33Z
refterms.dateFOA2020-09-14T09:18:58Z


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