The EMS Modified Early Warning Score (EMEWS): A simple count of vital signs as a predictor of out-of-hospital cardiac arrests

2021 ◽  
pp. 1-10
Author(s):  
Brian M Clemency ◽  
William Murk ◽  
Alexander Moore ◽  
Lawrence H. Brown
Keyword(s):  
Early Warning ◽  
Vital Signs ◽  
Early Warning Score ◽  
Simple Count

Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Sliding Window ◽  
Early Warning Score ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

scholarly journals Impact of the level of sickness on higher mortality in emergency medical admissions to hospital at weekends

2017 ◽  
Vol 22 (4) ◽  
pp. 236-242 ◽  
Author(s):  
Mohammed Mohammed ◽  
Muhammad Faisal ◽  
Donald Richardson ◽  
Robin Howes ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Early Warning ◽  
Odds Ratio ◽  
Vital Signs ◽  
Early Warning Score ◽  
Emergency Admissions ◽  
Clinical Assessments ◽  
Admission Time ◽  
Emergency Medical ◽  
Acute Hospitals ◽  
National Early Warning Score

Objective Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p < 0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio = 1.10, 95% CI 1.01 to 1.20, p = 0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio = 0.99, 95% CI 0.90 to 1.09, p = 0.87). Index National Early Warning Score was recorded sooner (−0.45 h, 95% CI −0.52 to −0.38, p < 0.001) for weekend admissions. Conclusions Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed.

scholarly journals Early warning score using electronically-held data for patients discharged from intensive care units

2020 ◽  
Author(s):  
Marco Pimentel ◽  
Alistair Johnson ◽  
Julie Darbyshire ◽  
Lionel Tarassenko ◽  
David Clifton ◽  
...  
Keyword(s):  
Adverse Event ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Early Warning ◽  
Vital Sign ◽  
Scoring System ◽  
Vital Signs ◽  
Early Warning Score ◽  
Hospital Death ◽  
Icu Discharge

Abstract Rationale. Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with early warning score (EWS) systems being used to identify those at risk of deterioration. Objectives. We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWSs) with a risk score of a future adverse event calculated on discharge from ICU.Methods. A modified Delphi process identified common, and candidate variables frequently collected and stored in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital-sign data from the day of hospital discharge, which is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay. Data from two NHS Foundation Trusts (UK) were used to develop and (externally) validate the model.Measurements and Main Results. A total of 12,394 vital-sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4,831 from 136 patients in the validation cohort. Outcome validation of our model yielded an area under the receiver operating characteristic curve (AUROC) of 0.724 for predicting ICU re-admission or in-hospital death within 24h. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (NEWS, 0.653). Conclusion. We showed that a scoring system incorporating data from a patient’s stay in ICU has better performance than commonly-used EWS systems based on vital signs alone.

scholarly journals Comparison of the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS) for predicting admission and in-hospital mortality in elderly patients in the pre-hospital setting and in the emergency department

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6947 ◽  
Author(s):  
Toshiya Mitsunaga ◽  
Izumu Hasegawa ◽  
Masahiko Uzura ◽  
Kenji Okuno ◽  
Kei Otani ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Hospital Mortality ◽  
Early Warning ◽  
Pressure Pulse ◽  
Vital Signs ◽  
Early Warning Score ◽  
Single Centre ◽  
Peripheral Oxygen Saturation ◽  
Significant Difference ◽  
National Early Warning Score

The aim of this study is to evaluate the usefulness of the pre-hospital National Early Warning Score (pNEWS) and the pre-hospital Modified Early Warning Score (pMEWS) for predicting admission and in-hospital mortality in elderly patients presenting to the emergency department (ED). We also compare the value of the pNEWS with that of the ED NEWS (eNEWS) and ED MEWS (eMEWS) for predicting admission and in-hospital mortality. This retrospective, single-centre observational study was carried out in the ED of Jikei University Kashiwa Hospital, in Chiba, Japan, from 1st April 2017 to 31st March 2018. All patients aged 65 years or older were included in this study. The pNEWS/eNEWS were derived from seven common physiological vital signs: respiratory rate, peripheral oxygen saturation, the presence of inhaled oxygen parameters, body temperature, systolic blood pressure, pulse rate and Alert, responds to Voice, responds to Pain, Unresponsive (AVPU) score, whereas the pMEWS/eMEWS were derived from six common physiological vital signs: respiratory rate, peripheral oxygen saturation, body temperature, systolic blood pressure, pulse rate and AVPU score. Discrimination was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC). The median pNEWS, pMEWS, eNEWS and eMEWS were significantly higher at admission than at discharge (p < 0.001). The median pNEWS, pMEWS, eNEWS and eMEWS of non-survivors were significantly higher than those of the survivors (p < 0.001). The AUC for predicting admission was 0.559 for the pNEWS and 0.547 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting admission (p = 0.102). The AUCs for predicting in-hospital mortality were 0.678 for the pNEWS and 0.652 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting in-hospital mortality (p = 0.081). The AUC for predicting admission was 0.628 for the eNEWS and 0.591 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting admission (p < 0.001). The AUC for predicting in-hospital mortality was 0.789 for the eNEWS and 0.720 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting in-hospital mortality (p < 0.001). For admission and in-hospital mortality, the AUC of the eNEWS was significantly greater than that of the pNEWS (p < 0.001, p < 0.001), and the AUC of the eMEWS was significantly greater than that of the pMEWS (p < 0.01, p < 0.05). Our single-centre study has demonstrated the low utility of the pNEWS and the pMEWS as predictors of admission and in-hospital mortality in elderly patients, whereas the eNEWS and the eMEWS predicted admission and in-hospital mortality more accurately. Evidence from multicentre studies is needed before introducing pre-hospital versions of risk-scoring systems.

scholarly journals Using modified early warning score to predict need of lifesaving intervention in adult non-trauma patients in a tertiary state hospital

2018 ◽  
Vol 25 (3) ◽  
pp. 146-151 ◽  
Author(s):  
Leong Shian Peng ◽  
Azhana Hassan ◽  
Aida Bustam ◽  
Muhaimin Noor Azhar ◽  
Rashidi Ahmad
Keyword(s):  
Emergency Department ◽  
Critically Ill ◽  
Early Warning ◽  
Demographic Data ◽  
Vital Signs ◽  
Multivariate Logistic Regression Analysis ◽  
Early Warning Score ◽  
Trauma Patients ◽  
Multivariate Logistic Regression ◽  
Level Of Consciousness

Background: Modified early warning score has been validated in many uses in the emergency department. We propose that the modified early warning score performs well in predicting the need of lifesaving interventions in the emergency department, as a predictor of patients who are critically ill. Objective: The study aims to evaluate the use of modified early warning score in sorting out critically ill patients in the emergency department. Methods: The patients’ demographic data and first vital signs (blood pressure, heart rate, temperature, respiratory rate, and level of consciousness) were collected prospectively. Individual modified early warning score was calculated. The outcome was a patient received one or more lifesaving interventions toward the end of stay in emergency department. Multivariate logistic regression analysis was utilized to assess the association between modified early warning score and other potential predictors with outcome. Results: There are a total of 259 patients enrolled into the study. The optimal modified early warning score in predicting lifesaving intervention was ≥4 with a sensitivity of 95% and specificity of 81%. Modified early warning score ≥4 (odds ratio = 96.97, 95% confidence interval = 11.82–795.23, p < 0.001) was found to significantly increase the risk of receiving lifesaving intervention in the emergency department. Conclusion: Modified early warning score is found to be a good predictor for patients in need of lifesaving intervention in the emergency department.

scholarly journals Pregnancy physiology pattern prediction study (4P study): protocol of an observational cohort study collecting vital sign information to inform the development of an accurate centile-based obstetric early warning score

BMJ Open ◽  
2017 ◽  
Vol 7 (9) ◽  
pp. e016034 ◽  
Author(s):  
Fiona Kumar ◽  
Jude Kemp ◽  
Clare Edwards ◽  
Rebecca M Pullon ◽  
Lise Loerup ◽  
...  
Keyword(s):  
Cohort Study ◽  
Pregnant Women ◽  
Early Warning ◽  
Vital Signs ◽  
Observational Cohort Study ◽  
Mobile Internet ◽  
Early Warning Score ◽  
Evidence Based ◽  
Reference Ranges ◽  
Observational Cohort

IntroductionSuccessive confidential enquiries into maternal deaths in the UK have identified an urgent need to develop a national early warning score (EWS) specifically for pregnant or recently pregnant women to aid more timely recognition, referral and treatment of women who are developing life-threatening complications in pregnancy or the puerperium. Although many local EWS are in use in obstetrics, most have been developed heuristically. No current obstetric EWS has defined the thresholds at which an alert should be triggered using evidence-based normal ranges, nor do they reflect the changing physiology that occurs with gestation during pregnancy.Methods and analysisAn observational cohort study involving 1000 participants across three UK sites in Oxford, London and Newcastle. Pregnant women will be recruited at approximately 14 weeks’ gestation and have their vital signs (heart rate, blood pressure, respiratory rate, oxygen saturation and temperature) measured at 4 to 6-week intervals during pregnancy. Vital signs recorded during labour and delivery will be extracted from hospital records. After delivery, participants will measure and record their own vital signs daily for 2 weeks. During the antenatal and postnatal periods, vital signs will be recorded on an Android tablet computer through a custom software application and transferred via mobile internet connection to a secure database. The data collected will be used to define reference ranges of vital signs across normal pregnancy, labour and the immediate postnatal period. This will inform the design of an evidence-based obstetric EWS.Ethics and disseminationThe study has been approved by the NRES committee South East Coast–Brighton and Sussex (14/LO/1312) and is registered with the ISRCTN (10838017). All participants will provide written informed consent and can withdraw from the study at any point. All data collected will be managed anonymously. The findings will be disseminated in international peer-reviewed journals and through research conferences.

scholarly journals Examining the utility of the Hamilton early warning scores (HEWS) at triage: Retrospective pilot study in a Canadian emergency department

CJEM ◽  
2017 ◽  
Vol 20 (2) ◽  
pp. 266-274 ◽  
Author(s):  
Steven Skitch ◽  
Benjamin Tam ◽  
Michael Xu ◽  
Laura McInnis ◽  
Anthony Vu ◽  
...  
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Early Warning ◽  
Vital Signs ◽  
Roc Curves ◽  
Early Warning Score ◽  
Critical Event ◽  
Discriminative Ability ◽  
Early Warning Scores ◽  
Patients At Risk

ABSTRACTObjectivesEarly warning scores use vital signs to identify patients at risk of critical illness. The current study examines the Hamilton Early Warning Score (HEWS) at emergency department (ED) triage among patients who experienced a critical event during their hospitalization. HEWS was also evaluated as a predictor of sepsis.MethodsThe study population included admissions to two hospitals over a 6-month period. Cases experienced a critical event defined by unplanned intensive care unit admission, cardiopulmonary resuscitation, or death. Controls were randomly selected from the database in a 2-to-1 ratio to match cases on the burden of comorbid illness. Receiver operating characteristic (ROC) curves were used to evaluate HEWS as a predictor of the likelihood of critical deterioration and sepsis.ResultsThe sample included 845 patients, of whom 270 experienced a critical event; 89 patients were excluded because of missing vitals. An ROC analysis indicated that HEWS at ED triage had poor discriminative ability for predicting the likelihood of experiencing a critical event 0.62 (95% CI 0.58-0.66). HEWS had a fair discriminative ability for meeting criteria for sepsis 0.77 (95% CI 0.72-0.82) and good discriminative ability for predicting the occurrence of a critical event among septic patients 0.82 (95% CI 0.75-0.90).ConclusionThis study indicates that HEWS at ED triage has limited utility for identifying patients at risk of experiencing a critical event. However, HEWS may allow earlier identification of septic patients. Prospective studies are needed to further delineate the utility of the HEWS to identify septic patients in the ED.

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