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Initial prehospital Rapid Emergency Medicine Score (REMS) to predict outcomes for COVID‐19 patients

Abstract Objective The Rapid Emergency Medicine Score (REMS) has not been widely studied for use in predicting outcomes of COVID‐19 patients encountered in the prehospital setting. This study aimed to determine whether the first prehospital REMS could predict emergency department and hospital dispositions for COVID‐19 patients transported by emergency medical services. Methods This retrospective study used linked prehospital and hospital records from the ESO Data Collaborative for all 911‐initiated transports of patients with hospital COVID‐19 diagnoses from July 1 to December 31, 2020. We calculated REMS with the first recorded prehospital values for each component. We calculated area under the receiver operating curve (AUROC) for emergency department (ED) mortality, ED discharge, hospital mortality, and hospital length of stay (LOS). We determined optimal REMS cut‐points using test characteristic curves. Results Among 13,830 included COVID‐19 patients, median REMS was 6 (interquartile range [IQR]: 5‐9). ED mortality was <1% (n = 80). REMS ≥9 predicted ED death (AUROC 0.79). One‐quarter of patients (n = 3,419) were discharged from the ED with an optimal REMS cut‐point of ≤5 (AUROC 0.72). Eighteen percent (n = 1,742) of admitted patients died. REMS ≥8 optimally predicted hospital mortality (AUROC 0.72). Median hospital LOS was 8.3 days (IQR: 4.1‐14.8 days). REMS ≥7 predicted hospitalizations ≥3 days (AUROC 0.62). Conclusion Initial prehospital REMS was modestly predictive of ED and hospital dispositions for patients with COVID‐19. Prediction was stronger for outcomes more proximate to the first set of emergency medical services (EMS) vital signs. These findings highlight the potential value of first prehospital REMS for risk stratification of individual patients and system surveillance for resource planning related to COVID‐19.


Background
Periodic surges of COVID-19 patients seeking care in the hospital environment overwhelm systems, reduce the availability of resources for treatment of non-COVID-19 cases, place unpredictable demands on inpatient resources and create increased risk for transmission of the infection to staff or other patients. 1,2 COVID-19 patient acuity has remained fairly consistent with the majority of patients having somewhat mild symptoms, about 25% experiencing hospitalization, and 5%-6% requiring critical care intervention. 3 This suggests that a significant proportion of patients seeking care for COVID-19 at the emergency department (ED) may have less severe presentations of the disease that could potentially be evaluated and managed in the community or in an outpatient setting. [3][4][5] Identifying this patient population could reduce the impact of patient surges on ED capacity and inpatient resources. 6 Hospital flow and resource management could be greatly enhanced if emergency medical services (EMS) notification included information that could reliably differentiate patients who will likely require admission with a prolonged length of stay (LOS) from those who could safely be discharged after evaluation and managed outside of the hospital setting.

Importance
The Bottom Line The Rapid Emergency Medicine Score (REMS) uses vital signs collected in the prehospital environment to predict outcomes. In this study the authors tested the ability of the REMS to predict hospital mortality among prehospital patients ultimately diagnosed with COVID-19. When applied to over 13,000 prehospital patients from across the United States, REMS was modestly predictive of emergency department and hospital dispositions. REMS may potentially play a role in community-wide COVID-19 management.
Identification of COVID-19 patients who are unlikely to require inpatient care could also improve hospital resource use and reduce risk of disease transmission. Multiple studies have explored the potential for EMS systems to perform initial screening on suspected COVID-19 patients for the purpose of identifying patients who are stable enough to be managed at home or in outpatient settings. 4,9,10,[18][19][20] Recently validated tools that predict COVID-19 prognosis in the hospital 21

Goals of this investigation
This study aimed to determine whether the first prehospital REMS could predict ED mortality, ED discharge, hospital mortality, and hospital LOS for COVID-19 patients transported by EMS.

Study design and setting
We conducted a retrospective observational study using de-identified

Selection of subjects
Given the advancements in treatment that led to measurable improvements in mortality for COVID-19 patients admitted between March and July 2020, 23

Rapid Emergency Medicine Score
Scoring for the REMS is calculated by assigning point values to categories of mean arterial pressure, pulse rate, respiratory rate, oxygen saturation, Glasgow Coma Scale, and patient age (Table 1). Total REMS values range from 0 to 26 and higher values are associated with increased mortality. 24 We used the first set of prehospital vital signs as entered by the EMS clinician in the REMS calculation, consistent with prior studies of EMS obtained REMS and hospital disposition. 17

Outcome measures
We examined 4 outcome measures: ED mortality, ED discharge, hospital mortality, and hospital LOS.
We created a dichotomous variable for ED mortality by categorizing dispositions of patients who died in the ED versus patients who did not die in the ED (discharged from the ED, admitted, transferred to another facility).
For ED discharge, we created a 2-level variable by grouping all patients discharged from the ED to locations other than hospice versus those who were admitted to the hospital, transferred from the ED to another facility, or discharged to hospice. Patients discharged to hospice were not included in the discharged from ED group because they were likely to be substantially different from discharged patients due to their age (61% > 75) and likelihood of imminent death (54% die within 30 days of admission to hospice). 25 We excluded patients who died in the ED from the ED discharge variable, because characteristics of this patient group would likely differ from patients discharged alive or patients who were admitted, transferred, or discharged to hospice.
For hospital mortality, we created categories for patients who died after admission versus patients who were discharged from the hospital. We excluded patients who were transferred to another facility and those who were still patients at the end of the study period as the definitive outcome for the encounter was unknown. We also excluded patients discharged to hospice as this disposition is distinct from other hospital discharge dispositions.
For hospital LOS, we used 3 days as a cut-point based on prior literature. 26 We created a dichotomous variable for admitted patients with a hospital LOS <3 days versus those who had an inpatient LOS of 3 or more days. For LOS analysis, we excluded patients who were transferred to another facility or who were still a patient at the end of the follow-up period because total LOS could not be determined. We also excluded patients who died within 3 days of admission as these patients were likely distinct from patients who were discharged alive within 3 days of admission. Similarly, we excluded patients who were discharged to hospice as being discharged to hospice likely represents a distinct cohort compared with patients with other discharge dispositions.

Analysis
We calculated prehospital REMS using the first set of vital signs obtained by EMS from the patient care record. We excluded records that were missing 1 or more elements needed to calculate a REMS value. In the descriptive analysis, we summarize continuous variables using median and interquartile range (IQR) and categorical variables with frequencies and percentages.
To determine the overall predictive ability of REMS for each outcome variable, we calculated the area under the receiver operating curve (AUROC) and 95% confidence interval (95% CI). In general, AUROC values between 0.7 and 0.8 are considered acceptable and values greater than 0.8 represent excellent predictive ability. 27 We used sensitivity and specificity curves to determine the optimal statistical prediction cut-point for each outcome by examining the intersection of these curves. We used univariable logistic regression models to assess the relationship between the dichotomized REMS variable and each outcome.

REMS as a predictor of ED mortality
Out of the 13,830 included patients, <1% (80) died in the ED ( Figure 2).
The median age among those who died in the ED was 79 (IQR: 67.5-88). The median first prehospital REMS among patients who died in the ED was 10 (IQR: [8][9][10][11][12][13][14]. The AUROC for REMS as a predictor of ED mortality versus ED discharge, hospital admission, or transfer to another facility or hospice was 0.79 (95% CI: 0.74-0.84). A cut-point of 9 or higher was statistically optimal for predicting ED death and resulted in a sensitivity of 72% and specificity of 74% (

REMS as a predictor of hospital mortality
Out of 9801 admitted EMS patients diagnosed with COVID-19, 18% (1742) died ( Figure 2). The median first prehospital REMS among patients who died in the hospital was 9 (IQR: 7-11) compared to a median of 6 (IQR: 5-8) among patients discharged alive from the hospital. AUROC was 0.72 (95% CI: 0.71-0.73) for REMS prediction of hospital death versus hospital discharge (Table 3). Sensitivity and specificity for hospital death were 68% and 63% respectively at an optimal REMS cut-point of 8 or higher. Patients whose initial prehospital REMS was 8 or higher had over 3 times the odds of dying after hospital admission

LIMITATIONS
This was a large retrospective study of data obtained directly from prehospital electronic health records. As a convenience sample of records from participating agencies in a data collaborative using a single software provider, these findings are not intended to represent a quantifi-