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This work aimed to investigate the adoption value of blood lactic acid (BLA) combined with the National Early Warning Score (NEWS) in the early screening of sepsis patients and assessing their severity. The data and materials utilized in this work were obtained from the electronic medical record system of 537 anonymized sepsis patients who received emergency rescue in the emergency rescue area of Liuzhou People's Hospital, Guangxi, from July 1, 2020, to December 26, 2020. Based on the 28-day outcomes of sepsis patients, the medical records were rolled into Group S (407 survival cases) and Group D (130 dead cases). Basic information such as the mode of hospital admission, initial management, use of emergency ventilator within 24 h of admission, NEWS score, arterial oxygen pressure/alveolar oxygen pressure ratio (PaO2/PAO2), alveolar-arterial oxygen difference (A-aDO2), serum creatinine (SCr), blood urea nitrogen (BUN), oxygenation index (OI), Glasgow Coma Scale (GCS), D-dimer, use of vasoactive drugs within 24 h of admission, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), quick Sequential Organ Failure Assessment (qSOFA) score, SOFA score, BLA level, NEWS with lactate (NEWS-L) score, SOFA score including lactate level (SOFA-L) score, Intensive Care Unit (ICU) length of stay, total hospital stay, ICU stay/total hospital stay, and septic shock condition were compared between groups. Logistic regression analysis was performed to assess the impact of various predictive factors on prognosis and to plot the receiver operating characteristic (ROC) curve. The results suggested marked differences between Group S and Group D in terms of mean age (t = −5.620; OR = −9.96, 95 % CI: −13.44∼-6.47; P

Background: Extensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients., Methods: A retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of Changhai Hospital, the First Affiliated Hospital of Naval Medical University. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling., Results: The results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, inhalation tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923(95 % CI 0.892-0.953), compared to NEWS which had a C-index of 0.699 (95 % CI 0.628-0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group., Conclusion: This study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems., Competing Interests: Declaration of Competing Interest The authors affirm that there are no conflicts of interest. We confirm that we do not possess any financial or personal affiliations with individuals or organizations that could potentially exert undue influence on our work. Furthermore, we have no professional or personal interests whatsoever in any product, service, and/or company that may be perceived as impacting the stance presented in, or the evaluation of, this manuscript., (Copyright © 2024. Published by Elsevier Ltd.)

Aim: To investigate whether trends in the NEWS values are associated with patient mortality in general ward patients. Methods: A one-year prospective observational study in three hospitals in Finland. All data on patients’ NEWS values during the first three days of general ward admissions were collected. The linear regression model was used to investigate the association of the NEWS trajectories with subsequent mortality. We used three outcome measures: 4–7-day, 4–14-day and 4–21-day mortality rates after the 0–3 days of initial hospitalization, respectively. Results: The study cohort consisted of 11,331 general ward patients. The non-survivors had higher initial NEWS score values in all outcome categories (all p

Aim: Early identification of patients at risk of serious adverse events (SAEs) is of vital importance, yet it remains a challenging task. We investigated the predictive power of National Early Warning Score (NEWS) 2, as compared to NEWS, among patients assessed by a Rapid response team (RRT). Methods: Prospective, observational cohort study on 898 consecutive patients assessed by the RRTs in 26 Swedish hospitals. For each patient, NEWS and NEWS 2 scores were uniformly calculated by the study team. The associations of NEWS and NEWS 2 scores with unanticipated admissions to Intensive care unit (ICU), mortality and in-hospital cardiac arrests (IHCA) within 24 h, and the composite of these three events were investigated using logistic regression. The predictive power of NEWS and NEWS 2 was assessed using the area under the receiver operating characteristic (AUROC) curves. Results: The prognostic accuracy of NEWS/NEWS 2 in predicting mortality was acceptable (AUROC 0.69/0.67). In discriminating the composite outcome and unanticipated ICU admission, both NEWS and NEWS 2 were relatively weak (AUROC 0.62/0.62 and AUROC 0.59/0.60 respectively); for IHCA the performance was poor. There were no differences between NEWS and NEWS 2 as to the predictive power. Conclusion: The prognostic accuracy of NEWS 2 to predict mortality within 24 h was acceptable. However, the prognostic accuracy of NEWS 2 to predict IHCA was poor. NEWS and NEWS 2 performed similar in predicting the risk of SAEs but their performances were not sufficient for use as a risk stratification tool in patients assessed by a RRT.

Aim of the study: The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality. Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs. Methods: In this retrospective study, all electronic ambulance mission reports between 2008 and 2015 in a single EMS system were collected. Adult patients (≥ 18 years) were included in the analysis. Random forest models with and without blood glucose were compared to the traditional NEWS for predicting one-day mortality. A ten-fold cross-validation method was applied to train and validate the random forest models. Results: A total of 26,458 patients were included in the study of whom 278 (1.0%) died within one day of ambulance mission. The area under the receiver operating characteristic curve for one-day mortality was 0.836 (95% CI, 0.810−0.860) for NEWS, 0.858 (95% CI, 0.832−0.883) for a random forest trained with NEWS variables only and 0.868 (0.843−0.892) for a random forest trained with NEWS variables and blood glucose. Conclusion: A random forest algorithm trained with NEWS variables was superior to traditional NEWS for predicting one-day mortality in adult prehospital patients, although the risk of selection bias must be acknowledged. The inclusion of blood glucose in the model further improved its predictive performance.

Objectives: The aim of this study was to assess the value of the National Early Warning Score and Worthing Physiological Scoring System for predicting changes in the condition of critical cases during transfer from the emergency department to the intensive care unit., Methods: This prospective single-centre study was conducted at a 1759-bed hospital in Beijing. We recorded the vital signs in the cases before leaving the emergency department and their changes in condition during transit., Results: A total of 258 critically ill cases were included. Forty-four cases (17.05%) exhibited changes in their condition during transit. Compared with cases with NEWS ≤ 5, cases with NEWS > 5 were more likely to experience changes with an OR of 5.744 (95% CI 2.888-11.426). Compared with cases with WPS ≤ 2, cases with WPS > 2 were more likely to experience changes with an OR of 7.217 (95% CI 3.575-14.569). The difference between the areas under the curve of the NEWS (0.751 ± 0.045) and the WPS (0.736 ± 0.045) was not statistically significant (P = 0.4518)., Conclusion: In our study, the Worthing Physiological Scoring System and National Early Warning Score both exhibited good discriminatory power, but the Worthing Physiological Scoring System is simpler to use and more suitable for use in a busy emergency department., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Limited research exists on the effectiveness of the New Zealand Early Warning Score (NZEWS)., Aim: To determine the impact of a modified NZEWS (M-NZEWS) and NZEWS on ward patients' medical emergency team activation triggers., Research Design: Mixed methods sequential design., Methods: Three phases included: 1) review of M-NZEWS electronic data to determine the effect of a M-NZEWS and NZEWS on ward patients; 2) an in-depth review of 20 Māori patients allocated to lower escalation zones if the NZEWS were adopted and 3) the number of electronic medical emergency team activation triggers compared to the number of actual medical emergency team activations., Results: 1255 patients and 3505 vital sign data sets were analysed. Adopting the NZEWS would result in 396 (26.8%) fewer patients triggering a medical emergency team activation. The biggest impact would be on Māori, with 38.6% of Māori allocated to a lower escalation zone. Only 51.2% of patients with a medical emergency team activation had vital signs triggering the response electronically documented., Conclusion: Changing from the M-NZEWS to NZEWS will reduce the number of medical emergency team activation triggers, with the biggest impact on Māori. Electronic vital sign data does not accurately reflect the number of ward medical emergency team triggers or activations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The National Early Warning Score - NEWS is a "track and trigger" scale designed to assess in-hospital patients' vital signs and detect clinical deterioration. In this study the NEWS was translated into Swedish and its association with the need of intensive care was investigated. A total of 868 patient charts, recorded by the medical emergency team at a university hospital, containing the parameters needed to calculate the NEWS were audited. The NEWS was translated into Swedish and tested for inter-rater reliability with a perfect agreement (weighted κ=1.0) among the raters. The median score for patients admitted to the ICU were higher than for those who were not (10 vs. 8, p<0.0001). AUROC for discriminating admittance to the ICU was 0.68 (95% CI: 0.622-0.739, p<0.0001). A regression analysis showed that lower oxygen saturation and a lower level of consciousness were significantly associated with ICU admission (OR 1.27 [1.06-1.52], p=0.01 and OR 1.77 [1.12-2.82], p=0.02) and may predict admission to the ICU better than the other parameters. The Swedish translated NEWS seems to have excellent inter-rater reliability and can be used without risk of linguistic misinterpretation. High scores for the parameters oxygen saturation and level of consciousness in the NEWS may predict admission to the ICU., (Copyright © 2016 Elsevier Ltd. All rights reserved.)