ViEWS—Towards a national early warning score for detecting adult inpatient deterioration

Prytherch, David; Smith, Gary B.; Schmidt, Paul E.; Featherstone, Peter I.

doi:10.1016/j.resuscitation.2010.04.014

Cited by 465 publications

(421 citation statements)

References 11 publications

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“…NEWS was designed by a multiprofessional working group using evidence arising from the development of ViEWS. 11 The modifications to ViEWS resulting in NEWS reflect clinical considerations based on professional opinion, the paucity of extreme physiological values in the ViEWS database, and the recommendation that all extreme values in all physiological parameters in NEWS should score 3 points. 12 NEWS was evaluated against a range of outcomes that are of major importance to patients and staff.…”

Section: Discussionmentioning

confidence: 99%

“…This large study has several strengths that are described in the original ViEWS paper. 11 Of particular note are that all vital signs variables were collected simultaneously in a standardised manner as part of the clinical process and that each vital signs observation set contained all of the necessary vital signs variables. The study also has several weaknesses that are also described elsewhere.…”

Section: Discussionmentioning

confidence: 99%

“…ViEWS was developed using an iterative, pragmatic, 'trial and error' approach, with the cut-offs for its scoring bands being deliberately adjusted to maximise its ability to predict in-hospital death within 24 hours of a vital signs dataset. 11 No attempt was made to modify ViEWS with respect to maximise its ability to discriminate any other outcome.…”

Section: Rcpl) Recommended That "…Physiological Assessment Of All Patmentioning

confidence: 99%

“…As part of the development of NEWS, our group undertook an evaluation of NEWS versus other existing EWS systems for the RCPL. This article shows the application of NEWS to the same large vital signs database used in the ViEWS publication, 11 but now also applied to additional clinical outcomes, i.e., cardiac arrest and unanticipated intensive care unit (ICU) admission. developed.…”

Section: Rcpl) Recommended That "…Physiological Assessment Of All Patmentioning

confidence: 99%

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The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

et al. 2013

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Introduction: Early warning scores (EWS) are recommended as part of the early recognition and response to patient deterioration. The Royal College of Physicians recommends the use of a National Early Warning Score (NEWS) for the routine clinical assessment of all adult patients. Methods:We tested the ability of NEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit (ICU) admission or death within 24 hours of a NEWS value and compared its performance to that of 33 other EWSs currently in use, using the area under the receiver-operating characteristics (AUROC) curve and a large vital signs database (n=198,755 observation sets) collected from 35,585 consecutive, completed acute medical admissions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Rcpl) Recommended That "…Physiological Assessment Of All Patmentioning

confidence: 99%

Section: Rcpl) Recommended That "…Physiological Assessment Of All Patmentioning

confidence: 99%

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The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

et al. 2013

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“…The prediction time window was defined as the interval from 0.5 to 24 hours before the outcomes 28, 29. It is important to note that the prediction unit was not the patient but the input vector.…”

Section: Methodsmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

221

205

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BackgroundIn‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems.Methods and ResultsThis retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.ConclusionsAn algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

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Implementation of a Value-Driven Outcomes Program to Identify High Variability in Clinical Costs and Outcomes and Association With Reduced Cost and Improved Quality

Lee

Kawamoto

Hess

et al. 2016

JAMA

247

207

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ViEWS—Towards a national early warning score for detecting adult inpatient deterioration

Cited by 465 publications

References 11 publications

The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Implementation of a Value-Driven Outcomes Program to Identify High Variability in Clinical Costs and Outcomes and Association With Reduced Cost and Improved Quality

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