Validation of a modified Early Warning Score in medical admissions.

By C. Subbe, M. Kruger, P. Rutherford, L. Gemmel. 2001

1,761 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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): “Researchers 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…”
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards (2016, methodology): “In order to put the accuracy results in perspective with prior studies, the Modified Early Warning Score (MEWS), a commonly utilized rapid response team activation tool, was also calculated (13).”
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection (2014, methodology): “The modified early warning score (MEWS) is another popular tool in Commonwealth countries to identify patients at risk of deterioration [8,9].”
Predicting accurate probabilities with a ranking loss (2012, background): “Accurate probabilities are also essential for medical screening tools to trigger early assessment and admission to an ICU (Subbe et al., 2001).”
Coarse race data conceals disparities in clinical risk score performance (2023, background): “There were many possible clinical risk scores to study: NEWS, CART, NEWS2, MEWS (Subbe et al., 2001), and REMS (Olsson et al., 2004), for example.”
A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain (2016, background): “Vital signs and 12-lead ECG variables are well-established and frequently employed during clinical risk prediction on the ED [11, 32].”
An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest (2018, background): “These vital signs are associated with adverse clinical outcomes and are measured periodically and frequently.(6,16,26) Furthermore, they are objective values that are barely affected by medical staff measuring them.”
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics (2016, methodology): “There is a 11.3% increase in the PPV value for the Rothman index, and 19.4% increase in the PPV for MEWS compared to the PPV of Algorithm 1.”
BTS guideline for emergency oxygen use in adult patients (2008, background): “73 138 The respiratory rate is the single best predictor of severe illness.(73)”
A targeted real-time early warning score (TREWScore) for septic shock (2015, methodology): “MEWS assigns points based on RR, heart rate, SBP, temperature, and the Alert, Voice, Pain, Unresponsive (AVPU) score (43).”
The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise (2020, background): “The NEWS assigns a score to f R values outside of the 12–20 breaths/min range, with the highest score attributed to f R values≤8 and ≥25 breaths/min [112], while the highest score for MEWS is attributed to f R values≥30 breaths/min [113].”
Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence (2017, background): “Physiological track and trigger systems (PTTSs) are increasingly being used as an aid to help healthcare workers identify deteriorating patients so that immediate, appropriate intervention can occur [9].”