scholarly journals CRITICAL APPRAISAL: ADEQUACY OF REPORTING STUDIES ON EARLY WARNING SCORE SYSTEMS

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Una Kyriacos
Keyword(s):  
Early Warning ◽  
Critical Appraisal ◽  
Early Warning Score ◽  
Score Systems

scholarly journals Development and validation of early warning score systems for COVID‐19 patients

2021 ◽  
Author(s):  
Alexey Youssef ◽  
Samaneh Kouchaki ◽  
Farah Shamout ◽  
Jacob Armstrong ◽  
Rasheed El‐Bouri ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning Score ◽  
Development And Validation ◽  
Score Systems

Pediatric Early Warning Score Systems, Nurses Perspective – A Focus Group Study

2018 ◽  
Vol 41 ◽  
pp. e16-e22 ◽  
Author(s):  
Claus Sixtus Jensen ◽  
Pia Bonde Nielsen ◽  
Hanne Vebert Olesen ◽  
Hans Kirkegaard ◽  
Hanne Aagaard
Keyword(s):  
Focus Group ◽  
Early Warning ◽  
Early Warning Score ◽  
Focus Group Study ◽  
Score Systems

scholarly journals Development and Validation of Early Warning Score Systems for COVID-19 Patients

2020 ◽  
Author(s):  
Alexey Youssef ◽  
Samaneh Kouchaki ◽  
Farah Shamout ◽  
Jacob Armstrong ◽  
Rasheed El-Bouri ◽  
...  
Keyword(s):  
Early Warning ◽  
Vital Signs ◽  
Early Warning Score ◽  
Respiratory Support ◽  
Patient Treatment ◽  
Gradient Boosting ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Development And Validation ◽  
Score Systems

AbstractCOVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of October 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. We evaluated the ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 hours. We show these scores perform sub-optimally at this specific task. Therefore, we develop an alternative Early Warning Score based on a Gradient Boosting Trees (GBT) algorithm that is able to predict deterioration within the next 24 hours with high AUROC 94% and an accuracy, sensitivity and specificity of 70%, 96%, 70%, respectively. Our GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.

scholarly journals Nurses' Experiences and Perceptions of two Early Warning Score systems to Identify Patient Deterioration—A Focus Group Study

Nursing Open ◽  
2021 ◽  
Author(s):  
Caroline S. Langkjaer ◽  
Dorthe G. Bove ◽  
Pernille B. Nielsen ◽  
Kasper K. Iversen ◽  
Morten H. Bestle ◽  
...  
Keyword(s):  
Focus Group ◽  
Early Warning ◽  
Early Warning Score ◽  
Focus Group Study ◽  
Patient Deterioration ◽  
Score Systems

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 Experiences of a National Early Warning Score (NEWS) intervention in care homes during the COVID-19 pandemic: a qualitative interview study

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e045469
Author(s):  
Rachel Stocker ◽  
Siân Russell ◽  
Jennifer Liddle ◽  
Robert O Barker ◽  
Adam Remmer ◽  
...  
Keyword(s):  
Early Warning ◽  
Healthcare Professionals ◽  
Care Home ◽  
Qualitative Interview ◽  
Interview Study ◽  
Care Homes ◽  
Early Warning Score ◽  
Service Utilisation ◽  
Qualitative Interview Study ◽  
National Early Warning Score

BackgroundThe COVID-19 pandemic has taken a heavy toll on the care home sector, with residents accounting for up to half of all deaths in Europe. The response to acute illness in care homes plays a particularly important role in the care of residents during a pandemic. Digital recording of a National Early Warning Score (NEWS), which involves the measurement of physical observations, started in care homes in one area of England in 2016. Implementation of a NEWS intervention (including equipment, training and support) was accelerated early in the pandemic, despite limited evidence for its use in the care home setting.ObjectivesTo understand how a NEWS intervention has been used in care homes in one area of North-East England during the COVID-19 pandemic, and how it has influenced resident care, from the perspective of stakeholders involved in care delivery and commissioning.MethodsA qualitative interview study with care home (n=10) and National Health Service (n=7) staff. Data were analysed using thematic analysis.ResultsUse of the NEWS intervention in care homes in this area accelerated during the COVID-19 pandemic. Stakeholders felt that NEWS, and its associated education and support package, improved the response of care homes and healthcare professionals to deterioration in residents’ health during the pandemic. Healthcare professionals valued the ability to remotely monitor resident observations, which facilitated triage and treatment decisions. Care home staff felt empowered by NEWS, providing a common clinical language to communicate concerns with external services, acting as an adjunct to staff intuition of resident deterioration.ConclusionsThe NEWS intervention formed an important part of the care home response to COVID-19 in the study area. Positive staff perceptions now need to be supplemented with data on the impact on resident health and well-being, workload, and service utilisation, during the pandemic and beyond.

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