Assessing prognosis with modified early warning score, rapid emergency medicine score and worthing physiological scoring system in patients admitted to intensive care unit from emergency department

2019 ◽  
Vol 43 ◽  
pp. 9-14 ◽  
Author(s):  
Raziye Gizem Yüksel Gök ◽  
Alper Gök ◽  
Mehtap Bulut
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Emergency Medicine ◽  
Intensive Care ◽  
Early Warning ◽  
Scoring System ◽  
Early Warning Score

scholarly journals Prognostic value of Modified Early Warning Score generated in a Chinese emergency department: a prospective cohort study

BMJ Open ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e024120 ◽  
Author(s):  
Xiaohua Xie ◽  
Wenlong Huang ◽  
Qiongling Liu ◽  
Wei Tan ◽  
Lu Pan ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Cohort Study ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Early Warning ◽  
Predictive Value ◽  
Mortality Prediction ◽  
Early Warning Score ◽  
Secondary Outcome

ObjectivesThis study aimed to validate the performance of the Modified Early Warning Score (MEWS) in a Chinese emergency department and to determine the best cut-off value for in-hospital mortality prediction.DesignA prospective, single-centred observational cohort study.SettingThis study was conducted at a tertiary hospital in South China.ParticipantsA total of 383 patients aged 18 years or older who presented to the emergency department from 17 May 2017 through 27 September 2017, triaged as category 1, 2 or 3, were enrolled.OutcomesThe primary outcome was a composite of in-hospital mortality and admission to the intensive care unit. The secondary outcome was using MEWS to predict hospitalised and discharged patients.ResultsA total of 383 patients were included in this study. In-hospital mortality was 13.6% (52/383), and transfer to the intensive care unit was 21.7% (83/383). The area under the receiver operating characteristic curve of MEWS for in-hospital mortality prediction was 0.83 (95% CI 0.786 to 0.881). When predicting in-hospital mortality with the cut-off point defined as 3.5, 158 patients had MEWS >3.5, with a specificity of 66%, a sensitivity of 87%, an accuracy of 69%, a positive predictive value of 28% and a negative predictive value of 97%, respectively.ConclusionOur findings support the use of MEWS for in-hospital mortality prediction in patients who were triaged category 1, 2 or 3 in a Chinese emergency department. The cut-off value for in-hospital mortality prediction defined in this study was different from that seen in many other studies.

scholarly journals The significance of National Early Warning Score for predicting prognosis and evaluating conditions of patients in resuscitation room

2018 ◽  
Vol 25 (6) ◽  
pp. 324-330 ◽  
Author(s):  
Wang Chang Yuan ◽  
Cao Tao ◽  
Zhu Dan Dan ◽  
Sun Chang Yi ◽  
Wang Jing ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Warning ◽  
Chronic Health Evaluation ◽  
Early Warning Score ◽  
Health Evaluation ◽  
Chronic Health ◽  
Resuscitation Room ◽  
Receiver Operating Characteristic Curves ◽  
National Early Warning Score

Background: For critical patients in resuscitation room, the early prediction of potential risk and rapid evaluation of disease progression would help physicians with timely treatment, leading to improved outcome. In this study, it focused on the application of National Early Warning Score on predicting prognosis and conditions of patients in resuscitation room. The National Early Warning Score was compared with the Modified Early Warning Score) and the Acute Physiology and Chronic Health Evaluation II. Objectives: To assess the significance of NEWS for predicting prognosis and evaluating conditions of patients in resuscitation rooms. Methods: A total of 621 consecutive cases from resuscitation room of Xuanwu Hospital, Capital Medical University were included during June 2015 to January 2016. All cases were prospectively evaluated with Modified Early Warning Score, National Early Warning Score, and Acute Physiology and Chronic Health Evaluation II and then followed up for 28 days. For the prognosis prediction, the cases were divided into death group and survival group. The Modified Early Warning Score, National Early Warning Score, and Acute Physiology and Chronic Health Evaluation II results of the two groups were compared. In addition, receiver operating characteristic curves were plotted. The areas under the receiver operating characteristic curves were calculated for assessing and predicting intensive care unit admission and 28-day mortality. Results: For the prognosis prediction, in death group, the National Early Warning Score (9.50 ± 3.08), Modified Early Warning Score (4.87 ± 2.49), and Acute Physiology and Chronic Health Evaluation II score (23.29 ± 5.31) were significantly higher than National Early Warning Score (5.29 ± 3.13), Modified Early Warning Score (3.02 ± 1.93), and Acute Physiology and Chronic Health Evaluation II score (13.22 ± 6.39) in survival group ( p < 0.01). For the disease progression evaluation, the areas under the receiver operating characteristic curves of National Early Warning Score, Modified Early Warning Score, and Acute Physiology and Chronic Health Evaluation II were 0.760, 0.729, and 0.817 ( p < 0.05), respectively, for predicting intensive care unit admission; they were 0.827, 0.723, and 0.883, respectively, for predicting 28-day mortality. The comparison of the three systems was significant ( p < 0.05). Conclusion: The performance of National Early Warning Score for predicting intensive care unit admission and 28-day mortality was inferior than Acute Physiology and Chronic Health Evaluation II but superior than Modified Early Warning Score. It was able to rapidly predict prognosis and evaluate disease progression of critical patients in resuscitation room.

Early Warning Score Communication Bundle: A Pilot Study

2018 ◽  
Vol 27 (3) ◽  
pp. 238-242
Author(s):  
Cheryl Gagne ◽  
Susan Fetzer
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Warning ◽  
Rapid Response Team ◽  
Rapid Response ◽  
Early Warning Score ◽  
Early Warning Scores ◽  
Response Team ◽  
Intensive Care Unit Nurses ◽  
The Impact

Background Unplanned admissions of patients to intensive care units from medical-surgical units often result from failure to recognize clinical deterioration. The early warning score is a clinical decision support tool for nurse surveillance but must be communicated to nurses and implemented appropriately. A communication process including collaboration with experienced intensive care unit nurses may reduce unplanned transfers. Objective To determine the impact of an early warning score communication bundle on medical-surgical transfers to the intensive care unit, rapid response team calls, and morbidity of patients upon intensive care unit transfer. Methods After an early warning score was electronically embedded into medical records, a communication bundle including notification of and telephone collaboration between medical-surgical and intensive care unit nurses was implemented. Data were collected 3 months before and 21 months after implementation. Results Rapid response team calls increased nonsignificantly during the study period (from 6.47 to 8.29 per 1000 patient-days). Rapid response team calls for patients with early warning scores greater than 4 declined (from 2.04 to 1.77 per 1000 patient-days). Intensive care unit admissions of patients after rapid response team calls significantly declined (P = .03), as did admissions of patients with early warning scores greater than 4 (P = .01), suggesting that earlier intervention for patient deterioration occurred. Documented reassessment response time declined significantly to 28 minutes (P = .002). Conclusion Electronic surveillance and collaboration with experienced intensive care unit nurses may improve care, control costs, and save lives. Critical care nurses have a role in coaching and guiding less experienced nurses.

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.

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