Prediction of in-hospital mortality and admission to ICU using vital signs - a study of Early Warning Score as an alternative to traditional triage

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.

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Abbreviated National Early Warning Score predicts the need for hospital admission and in-hospital mortality in elderly patients

Emergency Care Journal ◽

10.4081/ecj.2018.7771 ◽

2018 ◽

Vol 14 (3) ◽

Cited By ~ 1

Author(s):

Toshiya Mitsunaga ◽

Masahiko Hujita ◽

Izumu Hasegawa ◽

Kei Otani ◽

Kenji Okuno ◽

...

Keyword(s):

Elderly Patients ◽

Hospital Mortality ◽

Early Warning ◽

Roc Analysis ◽

Pressure Pulse ◽

Vital Signs ◽

Early Warning Score ◽

Operating Characteristics ◽

Peripheral Oxygen Saturation ◽

National Early Warning Score

The aim of this study was to evaluate the value of the Abbreviated National Early Warning Score (aNEWS) for predicting admissions and in-hospital mortality in elderly patients present to Emergency Department (ED). This retrospective, single-centred observational study was carried out in the ED of Minamitama Hospital, in Tokyo, Japan from 1 April 2018 to 30 April 2018. All of the patients aged 65 and older were included in this study. The aNEWS is based on six common physiological vital signs, including peripheral oxygen saturation, the presence of inhaled oxygen parameters, body temperature, systolic blood pressure, pulse rate, and the Alert, responds to Voice, responds to Pain, Unresponsive score. The scores range from 0 and 3 for each parameter. The aNEWS ranged from a score of 0 to a maximum of 17. The receiver operating characteristics (ROC) analysis was used to evaluate the predictive value of the aNEWS for admission and in-hospital mortality. The median aNEWS of patients admitted to the hospital was significantly higher than that of patients discharged from the ED (P<0.001). The median aNEWS of survivors was significantly higher than that of non-survivors (P<0.001). The Areas under the ROC Curve (AUC) for predicting admission was 0.773 [95% CI 0.7142 to 0.8317, P<0.001] for the aNEWS. The AUC for predicting in-hospital mortality was 0.791 [95% CI 0.604 to 0.978, P<0.001] for the aNEWS. Our single-centred study has demonstrated the utility of the aNEWS as a predictor of patient admission and in-hospital mortality in elderly patients.

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

10.2196/preprints.21720 ◽

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.

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

Prehospital Emergency Care ◽

10.1080/10903127.2021.1908464 ◽

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

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Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study

BMJ Open ◽

10.1136/bmjopen-2020-043721 ◽

2021 ◽

Vol 11 (2) ◽

pp. e043721

Author(s):

Donald Richardson ◽

Muhammad Faisal ◽

Massimo Fiori ◽

Kevin Beatson ◽

Mohammed Mohammed

Keyword(s):

Hospital Mortality ◽

Early Warning ◽

Absolute Risk ◽

Cross Sectional Study ◽

Early Warning Score ◽

Cross Sectional ◽

Risk Of Mortality ◽

Baseline Mortality ◽

Acute Hospitals ◽

National Early Warning Score

ObjectivesAlthough the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic.DesignA retrospective cross-sectional study.SettingTwo acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively.ParticipantsAdult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions.ResultsOut of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%).ConclusionsThe index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.

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

Journal of Health Services Research & Policy ◽

10.1177/1355819617720955 ◽

2017 ◽

Vol 22 (4) ◽

pp. 236-242 ◽

Cited By ~ 8

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.

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