2021
DOI: 10.1186/s12873-021-00459-7
|View full text |Cite
|
Sign up to set email alerts
|

Internal validation and comparison of the prognostic performance of models based on six emergency scoring systems to predict in-hospital mortality in the emergency department

Abstract: Background Medical scoring systems are potentially useful to make optimal use of available resources. A variety of models have been developed for illness measurement and stratification of patients in Emergency Departments (EDs). This study was aimed to compare the predictive performance of the following six scoring systems: Simple Clinical Score (SCS), Worthing physiological Score (WPS), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), Modified Early Warning Score (ME… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
10
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 14 publications
(10 citation statements)
references
References 30 publications
0
10
0
Order By: Relevance
“…It is also widely used to predict the mortality risk for patients with community-acquired bacteremia in Taiwan. In a recent study, the REMS score was applied to patients with COVID-19 and influenced its risk stratification [ 34 , 35 , 36 , 37 , 38 ]. Olsson et al created the REMS score in 2004 and the parameters were listed in Table 7 .…”
Section: Discussionmentioning
confidence: 99%
“…It is also widely used to predict the mortality risk for patients with community-acquired bacteremia in Taiwan. In a recent study, the REMS score was applied to patients with COVID-19 and influenced its risk stratification [ 34 , 35 , 36 , 37 , 38 ]. Olsson et al created the REMS score in 2004 and the parameters were listed in Table 7 .…”
Section: Discussionmentioning
confidence: 99%
“…The variables showing significance in the multivariable regression analysis were used to develop a nomogram-based model for predicting apCR status after neoadjuvant treatment in breast cancer patients without distant metastasis (M0). The area under the curve (AUC) of ROC is a discrimination measure which represents the ability of the model to assign higher probability of apCR patients than non-apCR patients [16]. The yield values were from 0.5 (no predictive power) to 1.0 (perfect prediction).…”
Section: Discussionmentioning
confidence: 99%
“…Trauma and injury scoring systems can be crucial for injury characterization, especially in terms of assessing and providing prognosis for trauma incidents [ 116 ]. While current scoring systems are substandard, triaging departments often utilize them to evaluate patients efficiently by separating them on the degree of injury and threat of mortality and/or morbidity [ 117 ]. This presents an inherent standard for the measurement of trauma and/or injury as well as for making accurate prognoses.…”
Section: Application Of ML Algorithms For Hemorrhagic Traumamentioning
confidence: 99%