Rapid Emergency Medicine Score as a main predictor of mortality in Vibrio vulnificus–related patients

Evaluation of patients with decreased general condition and comparing HOTEL, WPS, and REMS for mortality prediction

The emergency department (ED) plays a crucial role in evaluating patients with decreased general condition (DGC). Prompt diagnosis and precise risk stratification enhances clinical care outcomes. This study aimed to reveal which scoring system is better for predicting mortality in patients with DGC. This single-center prospective study was conducted between 1 March 2021 and 1 June 2021 in a tertiary university hospital's ED. All patients presented with DGC were included. The scores assessed upon ED admission for patients include the Hypotension, Oxygen saturation, low Temperature, ECG changes, and Loss of independence, abbreviated as HOTEL score, Rapid Emergency Medicine Score (REMS), and the Worthing Physiological Scoring (WPS). The study aimed to compare the scoring systems to identify which is the best mortality predictor. Independent risk factors for 30-day mortality were analyzed using binary logistic regression and ROC curves to assess the effectiveness of HOTEL, REMS, and WPS scores in predicting mortality. The study was conducted with 137 DGC patients. The median age of patients presented to the ED with DGC was 77 (66.5-87), and 50.4% were male. 52.6% of patients died within one month. Analysis of mortality risk factors revealed that gender, age, day of admission, visit type, home oxygen, urinary catheter use, and speech status were not independent risk factors. For one-month mortality prediction, AUCs were: HOTEL 0.644 (cut-off 1), REMS 0.635 (cut-off 8), WPS 0.547 (cut-off 5). For mechanical ventilation: HOTEL 0.689 (10), REMS 0.790 (10), WPS 0.777 (4). For ICU admission: HOTEL 0.697 (2), REMS 0.770 (9), WPS 0.728 (4). Both the HOTEL and REMS scoring systems have moderate prognostic value in predicting mortality in patients with a DGC. The REMS, and WPS scores are also more helpful in determining the need for intensive care unit admission and mechanical ventilation.

Evaluation of prognostic scoring systems in patients hospitalized from the emergency department in a low-income region: northern Syria after internal turmoil as a different universe

BACKGROUND

In low-income or underdeveloped countries with conflict and internal unrest, healthcare facilities and staff are limited. For these reasons, it is necessary to use the most straightforward scoring systems to ensure that health facilities and staff are used effectively and to expedite processes through early and effective interventions for patients. In this study, we evaluate and compare the scoring systems used to predict patient prognosis for Emergency Department (ED) patients in northern Syria, which is an area marred by conflict and internal unrest.

METHODS

In this study, patients hospitalized in the Afrin, Azez Vatan, Jarablus, Tel Abyad, Rasulayn, El Bab, and Çobanbey hospitals in northern Syria were investigated. Only patients that were hospitalized in the emergency departments of these hospitals, including wards and intensive care units, were included in the study. Patients that were hospitalized from 03/01/2021 to 08/31/2021, the study period, were prospectively analyzed. Vital signs, medical histories and demographic data of the patients were recorded by calculating National Early Warning Score 2 (NEWS2), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and HOTEL Score (hypotension, oxygen saturation, low temperature, electrocardiogram, loss of independence). Acceptance parameters and scores were analyzed using statistical methods and by comparing groups.

RESULTS

: All four scoring systems were found to be effective in predicting mortality regarding ROC curve analysis. However, the statistical significance of the RAPS was slightly stronger than that of the other scores and REMS had the highest sensitivity and specificity amongst the four systems, at 86.2% and 84.1%, respectively. Regarding the risk of hospitalization in the ICU (p < 0.05), the sensitivity values of the cut-off values offered by the scoring systems remained below 0.70 regarding ROC curve analysis. RAPS had the highest sensitivity (65.2%) of the four systems with a cut-off value of 1.5.

DISCUSSION

This study in northern Syria has shown that although RAPS had stronger statistical power, REMS had better sensitivity and specificity for the prediction of mortality. Additionally, RAPS had better sensitivity for ICU risk. This study will contribute to the evaluation of healthcare in similar regions and to cost-effective healthcare delivery by using scoring systems for ED patients' admission.

Investigation of the usability of the REMS, RAPS, and MPM II0 scoring systems in the prediction of short-term and long-term mortality in patients presenting to the emergency department triage

BACKGROUND

Appropriate triage is an important component of patient management in emergency departments. The risk scoring system used for triage purposes in emergency departments should be obtained quickly and based on parameters directly related to prognosis.

AIMS

To investigate the success of the Rapid Emergency Medicine Score (REMS) and Rapid Acute Physiology Score (RAPS) as triage scoring systems and the Mortality Probability Model (MPM II₀) as an intensive care scoring system in identifying critical patients visited to the emergency department (ED) triage and predicting mortality, and to evaluate their superiority over each other, if any.

METHODS

This research was planned as a single-center and prospectively. The data of the study were obtained by screening the medical records of all patients who presented to the ED triage between January 1, 2020 and January 31, 2020. Patients under the age of 18 years, those with missing information in their files and pregnant women were not included in the study. Only the patients for whom the REMS, RAPS, and MPM II₀ scores could be calculated were included in the sample.

RESULTS

After excluding the patients who did not meet the inclusion criteria, the study was completed with 12,210 patients. The mean age of these patients was 44.7 ± 18.7 years, and 47.3% were male. The area under the receiver operating characteristics curve values for the prediction of 24-h, 30-day, 90-day, and 180-day mortality were determined as 0.979, 0.921, 0.904, and 0.897, respectively, for REMS; 0.929, 0.778, 0.75, and 0.725, respectively, for RAPS; and 0.925, 0.888, 0.866, and 0.861, respectively, for MPM II₀.

CONCLUSIONS

In this study, it was concluded that the REMS score was superior to the MPM II₀ and RAPS scores in predicting the short-term and long-term mortality status of patients and determining the discharge and hospitalization status of the patients.

The Accuracy of Rapid Emergency Medicine Score in Predicting Mortality in Non-Surgical Patients: A Systematic Review and Meta-Analysis

BACKGROUND

Emergency department (ED) physicians often need to quickly assess patients and determine vital signs to prioritize them by the severity of their condition and make optimal treatment decisions. Effective triage requires optimal scoring systems to accelerate and positively influence the treatment of trauma cases. To this end, a variety of scoring systems have been developed to enable rapid assessment of ED patients. The present systematic review and meta-analysis aimed to investigate the accuracy of the rapid emergency medicine score (REMS) system in predicting the mortality rate in non-surgical ED patients.

METHODS

A systematic search of articles published between 1990 and 2020 was conducted using various scientific databases (Medline, Embase, Scopus, Web of Science, ProQuest, Cochrane Library, IranDOC, Magiran, and Scientific Information Database). Both cross-sectional and cohort studies assessing the REMS system to predict mortality in ED settings were considered. Two reviewers appraised the selected articles independently using the National Institutes of Health (NIH) quality assessment tool. The random-effects model was used for meta-analysis. I² index and Q statistic were used to examine heterogeneity between the articles.

RESULTS

The search resulted in 1,310 hits from which, 29 articles were eventually selected. Out of these, for 25 articles, the area under the curve value of REMS ranged from 0.52 to 0.986. The predictive power of REMS for the in-hospital mortality rate was high in 19 articles (67.85%) and low in nine articles (32.15%).

CONCLUSION

The results showed that the REMS system is an effective tool to predict mortality in non-surgical patients presented to the ED. However, further evidence using high-quality design studies is required to substantiate our findings.

The Value of Physiological Scoring Criteria in Predicting the In-Hospital Mortality of Acute Patients; a Systematic Review and Meta-Analysis

INTRODUCTION

There is no comprehensive meta-analysis on the value of physiological scoring systems in predicting the mortality of critically ill patients. Therefore, the present study intended to conduct a systematic review and meta-analysis to collect the available clinical evidence on the value of physiological scoring systems in predicting the in-hospital mortality of acute patients.

METHOD

An extensive search was performed on Medline, Embase, Scopus, and Web of Science databases until the end of year 2020. Physiological models included Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), modified REMS (mREMS), and Worthing Physiological Score (WPS). Finally, the data were summarized and the findings were presented as summary receiver operating characteristics (SROC), sensitivity, specificity and diagnostic odds ratio (DOR).

RESULTS

Data from 25 articles were included. The overall analysis showed that the area under the SROC curve of REMS, RAPS, mREMS, and WPS criteria were 0.83 (95% CI: 0.79-0.86), 0.89 (95% CI: 0.86-0.92), 0.64 (95% CI: 0.60-0.68) and 0.86 (95% CI: 0.83-0.89), respectively. DOR for REMS, RAPS, mREMS and WPS models were 11 (95% CI: 8-16), 13 (95% CI: 4-41), 2 (95% CI: 2-4) and 17 (95% CI: 5-59) respectively. When analyses were limited to trauma patients, the DOR of the REMS and RAPS models were 112 and 431, respectively. Due to the lack of sufficient studies, it was not possible to limit the analyses for mREMS and WPS.

CONCLUSION

The findings of the present study showed that three models of RAPS, REMS and WPS have a high predictive value for in-hospital mortality. In addition, the value of these models in trauma patients is much higher than other patient settings.

Prognostic Factors of Mortality in Vibrio vulnificus Sepsis and Soft Tissue Infections: Meta-Analysis

Background: Vibrio vulnificus is a rare but life-threatening infection that effects the population near warm coastal areas. This infection could be fulminant and rapidly progress to severe sepsis and necrotizing soft tissue infection. Early diagnosis and treatment are critical to saving patients' lives. With multiple studies reporting discrepancies in prognostic factors and different treatment protocols, we aimed through this meta-analysis to assess these factors and protocols and the impact on the outcome of the infection. Materials and Methods: In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of PubMed, Embase, and Cochrane Library databases by two independent reviewers was reported. Studies evaluating prognostic factors and treatment outcomes of Vibrio vulnificus infections were included. Comprehensive Meta-Analysis, version 3.0 was used. Results: Two hundred eleven studies were identified. Of those, eight studies met our inclusion criteria. The following factors on presentation were associated with higher mortality rates; concomitant liver disease (odds ratio [OR], 4.38; 95% confidence interval [CI], 2.43-7.87; p < 0.001), renal disease (OR, 3.90; 95% CI, 1.37-11.12; p = 0.011), septic shock (OR, 2.82; 95% CI, 1.84-4.31; p < 0.001), higher Acute Physiology and Chronic Health Evaluation (APACHE) II score (OR, 3.40; 95% CI, 2.26-5.12; p < 0.001), elevated band cells count (OR, 2.61; 95% CI, 1.13-6.0; p = 0.024), hypoalbuminemia (OR, 3.41; 95% CI, 1.58-7.35; p = 0.002), and infection involving multiple limbs (OR, 4.36; 95% CI, 1.72-11.07; p = 0.002). Interestingly, different antibiotic regimens did not have any impact on outcomes, however, delayed surgical intervention after the first 12 or 24 hours was associated with higher mortality rates (OR, 2.64; 95% CI, 1.39-5.0; p = 0.003 and OR, 2.99; 95% CI, 1.54-5.78; p = 0.001, respectively). Conclusion: The presence of liver or renal disease, higher APACHE II scores, septic shock, hypoalbuminemia, or elevated band cell on presentation should alert the physician to the higher risk of mortality. Different antibiotic regimens did not impact the outcomes in these patients and delayed surgical intervention is associated with worsening of mortality.

Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease

Objectives

Rapid and early severity‐of‐illness assessment appears to be important for critically ill patients with novel coronavirus disease (COVID‐19). This study aimed to evaluate the performance of the rapid scoring system on admission of these patients.

Methods

A total of 138 medical records of critically ill patients with COVID‐19 were included in the study. Demographic and clinical characteristics on admission used for calculating Modified Early Warning Score (MEWS) and Rapid Emergency Medicine Score (REMS) and outcomes (survival or death) were collected for each case and extracted for analysis. All patients were divided into two age subgroups (<65 years and ≥65 years). The receiver operating characteristic (ROC) curve analyses were performed for overall patients and both subgroups.

Results

The median [25th quartile, 75th quartile] of MEWS of survivors versus nonsurvivors were 1 [1, 2] and 2 [1, 3] and those of REMS were 5 [2, 6] and 7 [6, 10], respectively. In overall analysis, the area under the ROC curve for the REMS in predicting mortality was 0.833 (95% confidence interval [CI] = 0.737 to 0.928), higher than that of MEWS (0.677, 95% CI = 0.541 to 0.813). An optimal cutoff of REMS (≥6) had a sensitivity of 89.5%, a specificity of 69.8%, a positive predictive value of 39.5%, and a negative predictive value of 96.8%. In the analysis of subgroup of patients aged <65 years, the area under the ROC curve for the REMS in predicting mortality was 0.863 (95% CI = 0.743 to 0.941), higher than that of MEWS (0.603, 95% CI = 0.462 to 0.732).

Conclusion

To our knowledge, this study was the first exploration on rapid scoring systems for critically ill patients with COVID‐19. The REMS could provide emergency clinicians with an effective adjunct risk stratification tool for critically ill patients with COVID‐19, especially for the patients aged <65 years. The effectiveness of REMS for screening these patients is attributed to its high negative predictive value.

The Rapid Emergency Medicine Score: A Critical Appraisal of Its Measurement Properties and Applicability to the Air Retrieval Environment

OBJECTIVE

The Rapid Emergency Medicine Score (REMS) was designed to predict in-hospital mortality using variables that are available in the prehospital setting. The objective of this article is to critically appraise the development and summarize the evidence regarding the measurement properties (sensitivity, reliability and validity) of the REMS.

METHODS

A literature search was performed identifying all studies describing the REMS. The original validation study was critically appraised for its development. All other studies that reported any measurement properties of the REMS were also appraised for evidence of calibration, reliability, and validity.

RESULTS

In total, 26 studies reported on the measurement properties of the REMS. Overall, the REMS was developed with robust methodology and has good sensibility with adequate content and face validity. It is easy to understand and feasible to be calculated within minutes of patient assessment. The REMS has the necessary measurement properties to be both a predictive and evaluative clinical index to measure prehospital severity of illness; however, no studies have adequately addressed the intra or inter-rater reliability of the score.

CONCLUSIONS

There is evidence to support the use of the REMS as a predictive or evaluative instrument. In most studies, it performed as well or better than other illness severity scores in predicting mortality.

Development of a tool for defining and identifying the dying patient in hospital: Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL)

OBJECTIVE

To develop a screening tool to identify elderly patients at the end of life and quantify the risk of death in hospital or soon after discharge for to minimise prognostic uncertainty and avoid potentially harmful and futile treatments.

DESIGN

Narrative literature review of definitions, tools and measurements that could be combined into a screening tool based on routinely available or obtainable data at the point of care to identify elderly patients who are unavoidably dying at the time of admission or at risk of dying during hospitalisation.

MAIN MEASUREMENTS

Variables and thresholds proposed for the Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL screening tool) were adopted from existing scales and published research findings showing association with either in-hospital, 30-day or 3-month mortality.

RESULTS

Eighteen predictor instruments and their variants were examined. The final items for the new CriSTAL screening tool included: age ≥65; meeting ≥2 deterioration criteria; an index of frailty with ≥2 criteria; early warning score >4; presence of ≥1 selected comorbidities; nursing home placement; evidence of cognitive impairment; prior emergency hospitalisation or intensive care unit readmission in the past year; abnormal ECG; and proteinuria.

CONCLUSIONS

An unambiguous checklist may assist clinicians in reducing uncertainty patients who are likely to die within the next 3 months and help initiate transparent conversations with families and patients about end-of-life care. Retrospective chart review and prospective validation will be undertaken to optimise the number of prognostic items for easy administration and enhanced generalisability. Development of an evidence-based tool for defining and identifying the dying patient in hospital: CriSTAL.