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CMAJ. 2019 Apr 8;191(14):E382-E389. doi: 10.1503/cmaj.181418.

Computer-aided National Early Warning Score to predict the risk of sepsis following emergency medical admission to hospital: a model development and external validation study.

Author information

1
Faculty of Health Studies (Faisal, Mohammed), University of Bradford, Bradford, UK; Bradford Institute for Health Research (Faisal), Bradford, UK; Department of Renal Medicine (Richardson), York Teaching Hospital NHS Foundation Trust Hospital, York, UK; School of Clinical Therapies (Scally), University College Cork, Cork, Ireland; Department of Strategy & Planning (Howes), Northern Lincolnshire and Goole Hospitals, Scunthorpe, UK; York Teaching Hospital NHS Foundation Trust (Beatson), York, UK; Northern Lincolnshire and Goole Hospitals (Speed), Scunthorpe, UK; The Strategy Unit (Mohammed), NHS Midlands and Lancashire Commissioning Support Unit, West Bromwich, UK.
2
Faculty of Health Studies (Faisal, Mohammed), University of Bradford, Bradford, UK; Bradford Institute for Health Research (Faisal), Bradford, UK; Department of Renal Medicine (Richardson), York Teaching Hospital NHS Foundation Trust Hospital, York, UK; School of Clinical Therapies (Scally), University College Cork, Cork, Ireland; Department of Strategy & Planning (Howes), Northern Lincolnshire and Goole Hospitals, Scunthorpe, UK; York Teaching Hospital NHS Foundation Trust (Beatson), York, UK; Northern Lincolnshire and Goole Hospitals (Speed), Scunthorpe, UK; The Strategy Unit (Mohammed), NHS Midlands and Lancashire Commissioning Support Unit, West Bromwich, UK M.A.Mohammed5@Bradford.ac.uk.

Abstract

BACKGROUND:

In hospitals in England, patients' vital signs are monitored and summarized into the National Early Warning Score (NEWS); this score is more accurate than the Quick Sepsis-related Organ Failure Assessment (qSOFA) score at identifying patients with sepsis. We investigated the extent to which the accuracy of the NEWS is enhanced by developing and comparing 3 computer-aided NEWS (cNEWS) models (M0 = NEWS alone, M1 = M0 + age + sex, M2 = M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis.

METHODS:

We included all emergency medical admissions of patients 16 years of age and older discharged over 24 months from 2 acute care hospital centres (York Hospital [YH] for model development and a combined data set from 2 hospitals [Diana, Princess of Wales Hospital and Scunthorpe General Hospital] in the Northern Lincolnshire and Goole National Health Service Foundation Trust [NH] for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data.

RESULTS:

The prevalence of sepsis was lower in YH (4.5%, 1596/35 807) than in NH (8.5%, 2983/35 161). The C statistic increased across models (YH: M0 0.705, M1 0.763, M2 0.777; NH: M0 0.708, M1 0.777, M2 0.791). For NEWS of 5 or higher, sensitivity increased (YH: 47.24% v. 50.56% v. 52.69%; NH: 37.91% v. 43.35% v. 48.07%), the positive likelihood ratio increased (YH: 2.77 v. 2.99 v. 3.06; NH: 3.18 v. 3.32 v. 3.45) and the positive predictive value increased (YH: 11.44% v. 12.24% v. 12.49%; NH: 22.75% v. 23.55% v. 24.21%).

INTERPRETATION:

From the 3 cNEWS models, model M2 is the most accurate. Given that it places no additional burden of data collection on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

PMID:
30962196
DOI:
10.1503/cmaj.181418

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