Table 1 Physiologic scoring systems developed and implemented in the emergency department setting
From: Bench-to-bedside review: Outcome predictions for critically ill patients in the emergency department
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 |