THERM: the Resuscitation Management score. A prognostic tool to identify critically ill patients in the emergency department

Cattermole, Giles N.; Liow, Elizabeth; Graham, Colin A.; Rainer, TH

doi:10.1136/emermed-2013-202772

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…81 The same findings have been reported for the REMS (Rapid Emergency Medicine Score), 82 MEDS (modified mortality in emergency department sepsis) 83 and THERM (The Resuscitation Management Score) 84 scores in the ED, the PREEMPT-2 (PRE-critical Emergency Medical Patient Triage) and PREAMBLE-2 (PRE-Admission Medical Blue-Light Emergency) scores in acute medical admissions 85 and CREWS (Chronic Respiratory Early Warning Score) for respiratory disease. 86 Care must be taken when using early warning scores in certain subspecialties.…”

Section: Limitations Of Early Warning Scoressupporting

confidence: 70%

Strengths and limitations of early warning scores: A systematic review and narrative synthesis

Downey

Tahir

Randell

et al. 2017

International Journal of Nursing Studies

212

153

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Section: Limitations Of Early Warning Scoressupporting

confidence: 70%

Strengths and limitations of early warning scores: A systematic review and narrative synthesis

Downey

Tahir

Randell

et al. 2017

International Journal of Nursing Studies

212

153

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“…Several studies have reported that REMS was superior to MEWS (2011 to 2019 in Israel, Taiwan, China, and Turkey) [ 20 , 23 , 24 , 31 ], which is in contrast to the results of the current study and other evidence presented in Table 5 [ 3 , 22 , 27 ]. Consistent with our findings, researches from the United States and Turkey indicate that the performance of these two models is similar to each other [ 2 , 3 ].…”

Section: Discussioncontrasting

confidence: 99%

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

et al. 2021

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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 (MEWS), and Routine Laboratory Data (RLD) to predict in-hospital mortality. Methods A prospective single-center observational study was conducted from March 2016 to March 2017 in Edalatian ED in Emam Reza Hospital, located in the northeast of Iran. All variables needed to calculate the models were recorded at the time of admission and logistic regression was used to develop the models’ prediction probabilities. The Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models’ performance. Internal validation was obtained by 1000 bootstrap samples. Pairwise comparison of AUC-ROC was based on the DeLong test. Results A total of 2205 patients participated in this study with a mean age of 61.8 ± 18.5 years. About 19% of the patients died in the hospital. Approximately 53% of the participants were male. The discrimination ability of SCS, WPS, RAPS, REMS, MEWS, and RLD methods were 0.714, 0.727, 0.661, 0.678, 0.698, and 0.656, respectively. Additionally, the AUC-PR of SCS, WPS, RAPS, REMS, EWS, and RLD were 0.39, 0.42, 0.35, 0.34, 0.36, and 0.33 respectively. Moreover, BS was 0.1459 for SCS, 0.1713 for WPS, 0.0908 for RAPS, 0.1044 for REMS, 0.1158 for MEWS, and 0.073 for RLD. Results of pairwise comparison which was performed for all models revealed that there was no significant difference between the SCS and WPS. The calibration plots demonstrated a relatively good concordance between the actual and predicted probability of non-survival for the SCS and WPS models. Conclusion Both SCS and WPS demonstrated fair discrimination and good calibration, which were superior to the other models. Further recalibration is however still required to improve the predictive performance of all available models and their use in clinical practice is still unwarranted.

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“…Some authors have argued that disease‐specific or condition‐specific EWSs might be required when EWS systems are used outside the patient population in which they were developed. The findings of the present study suggest that, for admissions to surgical specialties, a change in trigger level could be all that is required as a NEWS value of 4 for non‐elective admissions to surgical specialties has equivalent efficiency to a NEWS value of 5 for medicine.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the National Early Warning Score in non-elective medical and surgical patients

Kovacs

Jarvis

Prytherch

et al. 2016

British Journal of Surgery

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THERM: the Resuscitation Management score. A prognostic tool to identify critically ill patients in the emergency department

Cited by 10 publications

References 17 publications

Strengths and limitations of early warning scores: A systematic review and narrative synthesis

Strengths and limitations of early warning scores: A systematic review and narrative synthesis

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

Comparison of the National Early Warning Score in non-elective medical and surgical patients

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