Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Mahmoodpoor, Ata; Sanaie, Sarvin; Saghaleini, Seied Hadi; Ostadi, Zohreh; Hosseini, Mohammad-Salar; Sheshgelani, Naeeme; Vahedian‐Azimi, Amir; Samim, Abbas; Rahimi–Bashar, Farshid

doi:10.3389/fmed.2022.938005

Cited by 16 publications

(7 citation statements)

References 38 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MEWS were considerably higher (P 0.001) in the ICU readmission, time to readmission, mechanical ventilation after readmission, mechanical ventilation duration, and multiple organ failure. According to the study's findings, the MEWS performed better in predicting other outcomes than the NEWS score 12…”

Section: Resultsmentioning

confidence: 96%

Assess the Modified Early Warning Score (MEWS) in Predicting Critical Care Unit Admission Among Patient in Emergency Department at Saveetha Medical College and Hospital, Thandalam

Govindaraj¹,

Karpagam²,

KalaBarathi³

2023

View full text Add to dashboard Cite

Background: In the hospital, sickness severity and worsening were predicted using the Modified Early Warning Score (MEWS). The MEWS makes it possible to detect patient deterioration early, take prompt treatment, and consistently gauge the seriousness of a disease. Objectives: To assess the Modified Early Warning Score (MEWS) in predicting critical care unit admission among emergency department patients. Methodology: Research approach: quantitative approach, descriptive research design was used for 60 samples by convenience sampling techniques. Modified Early Warning Score (MEWS) tool was used to predict critical care admission in emergency department. Result: out of 60 samples, 30(50%) had medium score which infers that key threshold urgent response in critical care unit admission, 16(26.7%) had low score which interprets ward based admission and 14(23.3%) had high score which interprets urgent (or) emergency response admission. Regarding critical care admission in emergency department patients the demographic variables age, education and clinical variable in mechanical ventilator support had shown statistically significant association with Modified Early Warning Score in predicting critical care unit admission among patients in emergency department at p

show abstract

Section: Resultsmentioning

confidence: 96%

Assess the Modified Early Warning Score (MEWS) in Predicting Critical Care Unit Admission Among Patient in Emergency Department at Saveetha Medical College and Hospital, Thandalam

Govindaraj¹,

Karpagam²,

KalaBarathi³

2023

View full text Add to dashboard Cite

show abstract

“…As MEWS includes cardiac and pulmonary parameters, such as HR, RR, and blood pressure, which sensitively reflect patients’ circulation and fluid status, MEWS is able to assist in the earlier detection of patients who are becoming unstable and guides important interventions. Studies have demonstrated the reliable performance of MEWS for risk stratification in sepsis, trauma, and critical patients with ICU readmission [ 31 , 32 , 33 ]. In elderly patients presenting to the ED, MEWS has also been proven to be an effective tool for predicting in-hospital mortality [ 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Performance of Six Clinical Physiological Scoring Systems in Predicting In-Hospital Mortality in Elderly and Very Elderly Patients with Acute Upper Gastrointestinal Bleeding in Emergency Department

Hung

et al. 2023

Medicina

View full text Add to dashboard Cite

Background and Objectives: The aim of this study is to compare the performance of six clinical physiological-based scores, including the pre-endoscopy Rockall score, shock index (SI), age shock index (age SI), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and Modified Early Warning Score (MEWS), in predicting in-hospital mortality in elderly and very elderly patients in the emergency department (ED) with acute upper gastrointestinal bleeding (AUGIB). Materials and Methods: Patients older than 65 years who visited the ED with a clinical diagnosis of AUGIB were enrolled prospectively from July 2016 to July 2021. The six scores were calculated and compared with in-hospital mortality. Results: A total of 336 patients were recruited, of whom 40 died. There is a significant difference between the patients in the mortality group and survival group in terms of the six scoring systems. MEWS had the highest area under the curve (AUC) value (0.82). A subgroup analysis was performed for a total of 180 very elderly patients (i.e., older than 75 years), of whom 27 died. MEWS also had the best predictive performance in this subgroup (AUC, 0.82). Conclusions: This simple, rapid, and obtainable-by-the-bed parameter could assist emergency physicians in risk stratification and decision making for this vulnerable group.

show abstract

“…In clinical practice, predicting all-cause mortality and assessing risk have consistently been crucial outcomes for clinicians [4][5][6][7][8][9] . Various scores and indices based on simple linear relationship have been proposed and used to predict mortality; however, their accuracy is only moderate 4,8,10 .…”

Section: Introductionmentioning

confidence: 99%

Prospective and External Validation of Prognostic Machine Learning Models for Short- and Long-Term Mortality Among Acutely Admitted Patients Based on Blood Tests.

Jawad,

Altintas,

Eugen-Olsen

et al. 2024

Preprint

View full text Add to dashboard Cite

The application of machine learning (ML) models in emergency departments (EDs) to predict short- and long-term mortality encounters challenges, particularly in balancing simplicity with performance. This study addresses this gap by developing models that uses a minimal set of biomarkers, derived from a single blood sample at admission, to predict both short-term and long-term mortality. Our approach utilizes biomarkers representing vital organs and the immune system, offering a comprehensive view of both acute and chronic disease states. Moreover, by integrating explainable machine learning methods, we ensured that clinicians can easily interpret the model's outputs. Our Analysis included 65,484 admissions from three cohorts at two large Danish university hospitals, demonstrating the models' efficacy with high accuracy, with AUC values between 0·87 and 0·93. These results underscore that a single assessment of routine clinical biochemistry upon admission can serve as a powerful tool for both short-term and long-term mortality prediction in ED admissions.

show abstract

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Cited by 16 publications

References 38 publications

Assess the Modified Early Warning Score (MEWS) in Predicting Critical Care Unit Admission Among Patient in Emergency Department at Saveetha Medical College and Hospital, Thandalam

Assess the Modified Early Warning Score (MEWS) in Predicting Critical Care Unit Admission Among Patient in Emergency Department at Saveetha Medical College and Hospital, Thandalam

Performance of Six Clinical Physiological Scoring Systems in Predicting In-Hospital Mortality in Elderly and Very Elderly Patients with Acute Upper Gastrointestinal Bleeding in Emergency Department

Prospective and External Validation of Prognostic Machine Learning Models for Short- and Long-Term Mortality Among Acutely Admitted Patients Based on Blood Tests.

Contact Info

Product

Resources

About