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

By D. Prytherch, Gary B. Smith, P. Schmidt, P. Featherstone. 2010

553 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Surgery

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Citations in the corpus

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, methodology): “…have previously created several scoring models for predicting mortality, such as the Modified Early Warning Score [46], the VitalPAC Early Warning Score [47], and the Acute Physiology And Chronic Health Evaluation [48], mainly utilizing vital signs to predict mortality for hospitalized patients.”
Scalable and accurate deep learning with electronic health records (2018, neutral): “unexpected readmission (0.75-0.76 vs 0.69)[71] and increased length of stay (0.85-0.86 vs 0.77) [62]. Direct comparisons to other studies are diﬃcult [98] because of diﬀerent underlying study designs [52, 29, 97, 103, 77, 21, 80, 89, 53, 14, 84, 31, 104, 87, 96] incomplete deﬁnitions of cohorts and ”
An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest (2018, background): “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.”
Multitask learning and benchmarking with clinical time series data (2017, background): “ummarize patient state with a composite score and trigger alerts based on abnormally low values. Examples include the Modiﬁed Early Warning Score (MEWS) [57], the VitalPAC Early Warning Score (ViEWS) [58], and the National Early Warning Score (NEWS) [5] being deployed throughout the United Kingdom. ”
Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model (2018, methodology): “Where neurological status had been assessed using the GCS, we converted the GCS value to the AVPU scale, as previously described [22].”
Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence (2017, background): “chronic obstructive pulmonary disease (COPD); palliative care patients [23]), and human factors issues such as staff attitudes, poor communication of deterioration or confidence in the PTTS [24].”
Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study (2019, background): “Warning Scores (MEWS)(15) and VitalPAC Early Warning Scores (ViEWS).(16) The adoption of EWS in the hospital was found to correlate with reduced mortality rates and improved overall patient outcomes in a systematic review.”
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. (2013, methodology): “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 neces”
Coarse race data conceals disparities in clinical risk score performance (2023, background): “Several early warning scores have been developed to identify deteriorating patients (Prytherch et al., 2010; Churpek et al., 2012; Alam et al., 2014), with recent ML-based approaches (Muralitharan et al., 2021; Romero-Brufau et al., 2021), and emerging evidence suggests that warning systems reduce…”