Short National Early Warning Score - Developing a Modified Early Warning Score

Abstract

Introduction: Early Warning Score (EWS) systems have been developed for detecting hospital patients clinical deterioration. Many studies show that a National Early Warning Score (NEWS) performs well in discriminating survival from death in acute medical and surgical hospital wards. NEWS is validated for Portugal and is available for use. A simpler EWS system may help to reduce the risk of error, as well as increase clinician compliance with the tool.

Objectives: The aim of the study was to evaluate whether a simplified NEWS model will improve use and data collection.

Methods: We evaluated the ability of single and aggregated parameters from the NEWS model to detect patients' clinical deterioration in the 24h prior to an outcome. There were 2 possible outcomes: Survival vs Unanticipated intensive care unit admission or death. We used binary logistic regression models and Receiver Operating Characteristic Curves (ROC) to evaluate the parameters' performance in discriminating among the outcomes for a sample of patients from 6 Portuguese hospital wards.

Results: NEWS presented an excellent discriminating capability (Area under the Curve of ROC (AUCROC)=0.944). Temperature and systolic blood pressure (SBP) parameters did not contribute significantly to the model. We developed two different models, one without temperature, and the other by removing temperature and SBP (M2). Both models had an excellent discriminating capability (AUCROC: 0.965; 0.903, respectively) and a good predictive power in the optimum threshold of the ROC curve.

Conclusions: The 3 models revealed similar discriminant capabilities. Although the use of SBP is not clearly evident in the identification of clinical deterioration, it is recognized as an important vital sign. We recommend the use of the first new model, as its simplicity may help to improve adherence and use by health care workers.

Keywords: Clinical derangement; Hospital; NEWS; Patient safety; Statistic models; Vital signs.