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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
Richardson, D. Scally, Andy J. Howes, R. Beatson, K. Speed, K. Mohammad, Mohammad A.
Richardson, D.
Scally, Andy J.
Howes, R.
Beatson, K.
Speed, K.
Mohammad, Mohammad A.
Publication Date
2019-04-08
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(c) 2019 The Authors. Full-text reproduced in accordance with the CMA self-archiving policy.
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Yes
Open Access status
openAccess
Accepted for publication
2019-03-20
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Department
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Embargo end date
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Abstract
In English hospitals, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS). NEWS is more accurate than the quick sepsis related organ failure assessment (qSOFA) score at identifying patients with sepsis. We investigate the extent to which the accuracy of the NEWS is enhanced by developing computer-aided NEWS (cNEWS) models. We compared three cNEWS models (M0=NEWS alone; M1=M0 + age + sex; M2=M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis.
Methods: All adult emergency medical admissions discharged over 24-months from two acute hospitals (YH–York Hospital for model development; NH–Northern Lincolnshire and Goole Hospital for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data.
Findings: The prevalence of sepsis was lower in YH (4.5%=1596/35807) than NH (8.5%=2983/35161). 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). At NEWS 5+, sensitivity increased (YH: 47.24% vs 50.56% vs 52.69%; NH: 37.91% vs 43.35% vs 48.07%)., the positive likelihood ratio increased (YH: 2.77 vs 2.99 vs 3.06; NH: 3.18 vs 3.32 vs 3.45) and the positive predictive value increased (YH: 11.44% vs 12.24% vs 12.49%; NH: 22.75% vs 23.55% vs 24.21%).
Interpretation: From the three cNEWS models, Model M2 is the most accurate. Since it places no additional data collection burden on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.
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Accepted manuscript
Citation
Faisal M, Richardson D, Scally AJ et al (2019) 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. CMAJ. 191(14): E382-E389.
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Notes
Research Development Fund Publication Prize Award winner, April 2019.