ViEWS--Towards a national early warning score for detecting adult inpatient deterioration.
TL;DR: A validated, paper-based, aggregate weighted track and trigger system (AWTTS) that could serve as a template for a national early warning score (EWS) for the detection of patient deterioration is developed and demonstrated that its performance for predicting mortality (within a range of timescales) is superior to all other published AWTTSs.
About: This article is published in Resuscitation.The article was published on 2010-08-01. It has received 495 citations till now. The article focuses on the topics: Early warning score.
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TL;DR: Cardiothoracic anesthetic, Southampton General Hospital, Southampton, UK Anesthesia and Intensive Care Medicine, Royal United Hospital, Bath, UK Anaesthesia and intensive care medicine, Southmead Hospital, Bristol, UK Surgical ICU, Oslo University Hospital Ulleval, Oslo, Norway Department of Cardiology, Academic Medical Center, Amsterdam, The Netherlands Critical Care and Resuscitation, University of Warwick, Warwick Medical School, Warwick, UK
2,561 citations
Heart of England NHS Foundation Trust1, University of Warwick2, University of Helsinki3, Oslo University Hospital4, University of Antwerp5, Ghent University6, Innsbruck Medical University7, Mario Negri Institute for Pharmacological Research8, Southmead Hospital9, The Catholic University of America10, Imperial College Healthcare11, Royal United Hospital12, Imperial College London13, University of Bern14
TL;DR: This chapter contains guidance on the techniques used during the initial resuscitation of an adult cardiac arrest victim and the use of an automated external defibrillator (AED).
1,104 citations
TL;DR: In this paper, the authors proposed a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format and demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Abstract: Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.
958 citations
TL;DR: Anaesthesia and Intensive Care Medicine, Southmead Hospital, Bristol, UK Anaesthesia and intensive care Medicine, Royal United Hospital, Bath, UK School of Clinical Sciences, University of Bristol, United Kingdom, UK Department of Anesthesiology, and intensive care medicine, The National Institute for Mental Health (NIMH), London, UK NHS Foundation Trust, Coventry, UK The National Health Service (NHS), Coventry and Birmingham, UK Heart of England (HSE), Birmingham, Birmingham and The Royal National Institute of Neurological and Women's Health Service
919 citations
TL;DR: News has a greater ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated ICU admission or death within 24h of a NEWS value than 33 other EWSs.
749 citations
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TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Abstract: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect difference...
19,398 citations
TL;DR: In this article, the authors present methods of measuring the concentration of wealth in the United States, and present a method for measuring the distribution of wealth among individuals in the USA, in terms of wealth concentration.
Abstract: (1905) Methods of Measuring the Concentration of Wealth Publications of the American Statistical Association: Vol 9, No 70, pp 209-219
1,941 citations
TL;DR: The ability of a modified Early Warning Score (MEWS) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated and could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.
Abstract: The Early Warning Score (EWS) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score (MEWS) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied MEWS to patients admitted to the 56-bed acute Medical Admissions Unit (MAU) of a District General Hospital (DGH). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit (ICU) admission, high dependency unit (HDU) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death (OR 5.4, 95%CI 2.8-10.7), ICU admission (OR 10.9, 95%CI 2.2-55.6) and HDU admission (OR 3.3, 95%CI 1.2-9.2). MEWS can be applied easily in a DGH medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the HDU or ICU. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.
1,423 citations
TL;DR: The NRCPR is described as the first comprehensive, Utstein-based, standardized characterization of in-hospital resuscitation in the United States, with results that suggest that discharged survivors were generally good and neurological outcome in discharged survivors was generally good.
1,168 citations
TL;DR: A wide variety of TTs were in use, with little evidence of reliability, validity and utility, and Sensitivity was poor, which might be due in part to the nature of the physiology monitored or to the choice of trigger threshold.
Abstract: Physiological track and trigger warning systems (TTs) are used to identify patients outside critical care areas at risk of deterioration and to alert a senior clinician, Critical Care Outreach Service, or equivalent. The aims of this work were: to describe published TTs and the extent to which each has been developed according to established procedures; to review the published evidence and available data on the reliability, validity and utility of existing systems; and to identify the best TT for timely recognition of critically ill patients. Systematic review of studies identified from electronic, citation and hand searching, and expert informants. Cohort study of data from 31 acute hospitals in England and Wales. Thirty-six papers were identified describing 25 distinct TTs. Thirty-one papers described the use of a TT, and five were studies examining the development or testing of TTs. None of the studies met all methodological quality standards. For the cohort study, outcome was measured by a composite of death, admission to critical care, ‘do not attempt resuscitation’ or cardiopulmonary resuscitation. Fifteen datasets met pre-defined quality criteria. Sensitivities and positive predictive values were low, with median (quartiles) of 43.3 (25.4–69.2) and 36.7 (29.3–43.8), respectively. A wide variety of TTs were in use, with little evidence of reliability, validity and utility. Sensitivity was poor, which might be due in part to the nature of the physiology monitored or to the choice of trigger threshold. Available data were insufficient to identify the best TT.
436 citations