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
Objectives 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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2019-11-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/11/e031596.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850066054556942336 |
|---|---|
| author | Muhammad Faisal Donald Richardson Kevin Beatson Robin Howes Mohammed Mohammed Andy Scally |
| author_facet | Muhammad Faisal Donald Richardson Kevin Beatson Robin Howes Mohammed Mohammed Andy Scally |
| author_sort | Muhammad Faisal |
| collection | DOAJ |
| description | Objectives 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. |
| format | Article |
| id | doaj-art-625b043f7a48419e81d73539b85631bc |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-11-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-625b043f7a48419e81d73539b85631bc2025-08-20T02:48:51ZengBMJ Publishing GroupBMJ Open2044-60552019-11-0191110.1136/bmjopen-2019-031596Performance 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 studyMuhammad Faisal0Donald Richardson1Kevin Beatson2Robin Howes3Mohammed Mohammed4Andy Scally5Wolfson Centre for Applied Health Research, Bradford Royal Infirmary, Bradford, UKRenal Medicine, York Teaching Hospital NHS Foundation Trust, York, UKDepartment of Information Technology, York Teaching Hospitals NHS Foundation Trust, York, UK5 Department of Strategy & Planning, Northern Lincolnshire and Goole Hospitals NHS Foundation Trust, Grimsby, UKFaculty of Health Studies, University of Bradford, Bradford, UK4 School of Clinical Therapies, University College Cork National University of Ireland, Cork, IrelandObjectives 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.https://bmjopen.bmj.com/content/9/11/e031596.full |
| spellingShingle | Muhammad Faisal Donald Richardson Kevin Beatson Robin Howes Mohammed Mohammed Andy Scally 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopen.bmj.com/content/9/11/e031596.full |
| work_keys_str_mv | AT muhammadfaisal performanceofexternallyvalidatedenhancedcomputeraidedversionsofthenationalearlywarningscoreinpredictingmortalityfollowinganemergencyadmissiontohospitalinenglandacrosssectionalstudy AT donaldrichardson performanceofexternallyvalidatedenhancedcomputeraidedversionsofthenationalearlywarningscoreinpredictingmortalityfollowinganemergencyadmissiontohospitalinenglandacrosssectionalstudy AT kevinbeatson performanceofexternallyvalidatedenhancedcomputeraidedversionsofthenationalearlywarningscoreinpredictingmortalityfollowinganemergencyadmissiontohospitalinenglandacrosssectionalstudy AT robinhowes performanceofexternallyvalidatedenhancedcomputeraidedversionsofthenationalearlywarningscoreinpredictingmortalityfollowinganemergencyadmissiontohospitalinenglandacrosssectionalstudy AT mohammedmohammed performanceofexternallyvalidatedenhancedcomputeraidedversionsofthenationalearlywarningscoreinpredictingmortalityfollowinganemergencyadmissiontohospitalinenglandacrosssectionalstudy AT andyscally performanceofexternallyvalidatedenhancedcomputeraidedversionsofthenationalearlywarningscoreinpredictingmortalityfollowinganemergencyadmissiontohospitalinenglandacrosssectionalstudy |