scholarly journals Early Warning Scores and Their Application in the Inpatient Oncology Settings

2022 ◽  
Author(s):  
Sonieya Nagarajah ◽  
Monika K. Krzyzanowska ◽  
Tracy Murphy
Keyword(s):  
Intensive Care ◽  
Early Warning ◽  
Small Sample Size ◽  
Vital Signs ◽  
Organ Damage ◽  
Small Sample ◽  
Early Warning Score ◽  
Inpatient Ward ◽  
Retrospective Assessment ◽  
Long Term Prognosis

Early Warning Score (EWS) systems are tools that use alterations in vital signs to rapidly identify clinically deteriorating patients and escalate care accordingly. Since its conception in 1997, EWSs have been used in several settings, including the general inpatient ward, intensive care units, and the emergency department. Several iterations of EWSs have been developed with varying levels of sensitivity and specificity for use in different populations. There are multiple strengths of these tools, including their simplicity and their ability to standardize communication and to reduce inappropriate or delayed referrals to the intensive care unit. Although early identification of deteriorating patients in the oncology population is vital to reduce morbidity and mortality and to improve long-term prognosis, the application in the oncology setting has been limited. Patients with an oncological diagnosis are usually older, medically complex, and can have increased susceptibility to infections, end-organ damage, and death. A search using PubMed and Scopus was conducted for articles published between January 1997 and November 2020 pertaining to EWSs in the oncology setting. Seven relevant studies were identified and analyzed. The most commonly used EWS in this setting was the Modified Early Warning Score. Of the seven studies, only two included prospective validation of the EWS in the oncology population and the other five only included a retrospective assessment of the data. The majority of studies were limited by their small sample size, single-institution analysis, and retrospective nature. Future studies should assess dynamic changes in scores over time and evaluate balance measures to identify use of health care resources.

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.

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.

Abstract 9778: Establishing Inter Rater Reliability of the National Early Warning Score (NEWS)

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Donna M Miller
Keyword(s):  
Intensive Care ◽  
Acute Care ◽  
Early Warning ◽  
Assessment Tools ◽  
Early Warning Score ◽  
Care Hospital ◽  
Convenience Sample ◽  
Rater Reliability ◽  
National Early Warning Score ◽  
Level Of Agreement

A change in patient condition is a dynamic process which can go unrecognized and result in a failure to rescue. Changes in patients’ vital signs can precede adverse events many hours before critical events such as cardiac arrest or emergent transfer to the Intensive Care Unit occurs. Quantitative assessment tools are used to predict risk and need for additional resources at the bedside. These tools are referred to as Early Warning Scoring Systems. The Royal College of Physicians developed a standardized tool called the National Early Warning Score (NEWS, 2012) that uses a variety of physiologic parameters to assess risk and establish a trigger threshold for summoning additional resources. Purpose: Early warning scoring tools have been found to be reliable and accurate in predicting patient decompensation. However, data from instruments are only as reliable and accurate as the caregiver who obtains and documents the parameters. The purpose of this study was to establish inter rater reliability between the RN and PCA using NEWS. Design, Sample, Setting, Procedures: This study was conducted on the clinical units of a 104 bed Long Term Acute Care Hospital (LTCH) system. These units accept patients directly from Intensive Care Units who require intense services to maintain their trajectory toward recovery. The NEWS provides a way for early detection of patient decompensation which can prevent readmission to acute care and the subsequent financial implications The convenience sample consisted of 22 RNs and 6 PCAs. Consented subjects reviewed an unfolding case study that portrayed a typical patient on the LTCH unit. Subjects were asked to circle the parameter ranges on the NEWS tool that corresponded to physiologic values in the scenario. Findings: Krippendorff’s alpha was utilized to determine the level of agreement among the raters examining the three scenarios. An alpha value of 0.94 was obtained indicating a high level of agreement among the raters. Conclusion: The NEWS can serve as a reliable adjunct to the provision of safe patient care. While it is not the sole source for determining

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.

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