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Dynamic individual vital sign trajectory early warning score (DyniEWS) versus snapshot national early warning score (NEWS) for predicting postoperative deterioration.
Zhu, Yajing; Chiu, Yi-Da; Villar, Sofia S; Brand, Jonathan W; Patteril, Mathew V; Morrice, David J; Clayton, James; Mackay, Jonathan H.
Afiliação
  • Zhu Y; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK. Electronic address: yajing.zhu@roche.com.
  • Chiu YD; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Research and Development, Royal Papworth Hospital, Cambridge, UK. Electronic address: yi-da.chiu@nhs.net.
  • Villar SS; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Research and Development, Royal Papworth Hospital, Cambridge, UK. Electronic address: sofia.villar@mrc-bsu.cam.ac.uk.
  • Brand JW; Department of Anaesthesia and Critical Care, James Cook University Hospital, Middlesbrough, UK. Electronic address: Jonathan.brand@nhs.net.
  • Patteril MV; Department of Anaesthesia and Critical Care, University Hospitals Coventry and Warwickshire, Coventry, UK. Electronic address: Mathew.Patteril@uhcw.nhs.uk.
  • Morrice DJ; Department of Anaesthesia and Critical Care, New Cross Hospital, Wolverhampton, UK. Electronic address: D.Morrice@nhs.net.
  • Clayton J; Clinical Governance, Royal Papworth Hospital, Cambridge, UK. Electronic address: james.clayton1@nhs.net.
  • Mackay JH; Department of Anaesthesia and Critical Care, Royal Papworth Hospital, Cambridge, UK. Electronic address: jonmackay@doctors.org.uk.
Resuscitation ; 157: 176-184, 2020 12.
Article em En | MEDLINE | ID: mdl-33181231
ABSTRACT

AIMS:

International early warning scores (EWS) including the additive National Early Warning Score (NEWS) and logistic EWS currently utilise physiological snapshots to predict clinical deterioration. We hypothesised that a dynamic score including vital sign trajectory would improve discriminatory power.

METHODS:

Multicentre retrospective analysis of electronic health record data from postoperative patients admitted to cardiac surgical wards in four UK hospitals. Least absolute shrinkage and selection operator-type regression (LASSO) was used to develop a dynamic model (DyniEWS) to predict a composite adverse event of cardiac arrest, unplanned intensive care re-admission or in-hospital death within 24 h.

RESULTS:

A total of 13,319 postoperative adult cardiac patients contributed 442,461 observations of which 4234 (0.96%) adverse events in 24 h were recorded. The new dynamic model (AUC = 0.80 [95% CI 0.78-0.83], AUPRC = 0.12 [0.10-0.14]) outperforms both an updated snapshot logistic model (AUC = 0.76 [0.73-0.79], AUPRC = 0.08 [0.60-0.10]) and the additive National Early Warning Score (AUC = 0.73 [0.70-0.76], AUPRC = 0.05 [0.02-0.08]). Controlling for the false alarm rates to be at current levels using NEWS cut-offs of 5 and 7, DyniEWS delivers a 7% improvement in balanced accuracy and increased sensitivities from 41% to 54% at NEWS 5 and 18% to -30% at NEWS 7.

CONCLUSIONS:

Using an advanced statistical approach, we created a model that can detect dynamic changes in risk of unplanned readmission to intensive care, cardiac arrest or in-hospital mortality and can be used in real time to risk-prioritise clinical workload.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Escore de Alerta Precoce Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Escore de Alerta Precoce Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article