Table 1

Physiologic scoring systems developed and implemented in the emergency department setting

ED scoring system

Reference

Objectives and method

Summary results

Application

MEDS

[61]

Prospective cohort study in ED patients at risk for infection, using multivariate analysis to identify independent predictors of death

Development and internal validation of a prediction rule to risk stratify ED patients at risk for infection and predict their mortality. The areas under the ROC curve were 0.82 for the derivation set (n = 2070) and 0.78 for the validation set (n = 1109)

MEDS accurately identifies correlates of death in ED patients at risk for infection and is useful in stratification of patients according to mortality risk

RAPS

[82]

Prospective multi-institutional study of diverse group of transported patients to define the predictive power of RAPS

Predictive power of RAPS for mortality using the most deranged physiologic parameters pre- and post-transport was high (n = 1881), with ROC curves exhibiting predictive power similar to that of APACHE II

RAPS is a strong predictor of mortality and is highly reliable in predicting severity of physiologic instability before and after transport

REMS

[67]

Prospective cohort study to evaluate the accuracy of RAPS in predicting mortality and length of stay in nonsurgical ED patients. Age and SaO₂ were added to RAPS to derive REMS

REMS was superior to RAPS in predicting inpatient mortality, with area under the ROC curve of 0.85 for REMS and 0.65 for RAPS (n = 12,006)

REMS is an excellent predictor of inpatient mortality and length of stay for a wide range of nonsurgical ED patients

MEES

[69]

Prospective study to develop a rapid, simple scoring system to evaluate prehospital intervention based on objective parameters

Development and evaluation of MEES as a scoring system to evaluate prehospital clinical treatment. MEES was found to be an efficient and effective method for determining the impact of ED intervention (n = 356)

MEES is a reliable method for assessing prehospital intervention

SARS

[71]

Prospective study to validate SARS (four-item symptom and six-item clinical) screening scores in predicting SARS in febrile ED patients in endemic areas

Previously developed SARS screening scores (n = 70) were examined in 239 patients with fever. Eighty-two patients had SARS. The scores exhibited a combined sensitivity of 90.2% and specificity of 80.1% for SARS

SARS screening scores are potential screening methods for SARS in mass outbreaks

PRISA

[74]

Prospective study of pediatric severity of illness assessment, using univariate and multivariate logistic regression analyses to develop a model predicting hospital admission

Development of PRISA as an assessment tool to predict pediatric hospital admission from the ED. Areas under the ROC curve were 0.86 and 0.83 for the development (n = 2146) and validation (n = 537) samples, respectively, in predicting pediatric ED admission

PRISA can reliably predict pediatric hospital admission using data during the ED stay

APACHE, Acute Physiology and Chronic Health Evaluation; ED, emergency department; MEDS, Mortality in Emergency Department Sepsis; MEES, Mainz Emergency Evaluation Systems; PRISA, Pediatric Risk of Admission; RAPS, Rapid Acute Physiology Score; REMS, Rapid Emergency Medicine Score; ROC, receiver operating characteristic; SaO₂, oxygen saturation; SARS, Severe Acute Respiratory Syndrome.

Hargrove and Nguyen Critical Care 2005 9:376   doi:10.1186/cc3518