A modified simple triage and rapid treatment algorithm from the New York City (USA) Fire Department.

INTRODUCTION: The objective of this study was to determine if modification of the Simple Triage and Rapid Treatment (START) system by the addition of an Orange category, intermediate between the most critically injured (Red) and the non-critical, non-ambulatory injured (Yellow), would reduce over- and under-triage rates in a simulated mass-casualty incident (MCI) exercise. METHODS: A computer-simulation exercise of identical presentations of an MCI scenario involving a 2-train collision, with 28 case scenarios, was provided for triaging to two groups: the Fire Department of the City of New York (FDNY; n=1,347) using modified START, and the Emergency Medical Services (EMS) providers from the Eagles 2012 EMS conference (Lafayette, Louisiana USA; n=110) using unmodified START. Percent correct by triage category was calculated for each group. Performance was then compared between the two EMS groups on the five cases where Orange was the correct answer under the modified START system. RESULTS: Overall, FDNY-EMS providers correctly triaged 91.2% of cases using FDNY-START whereas non-FDNY-Eagles providers correctly triaged 87.1% of cases using unmodified START. In analysis of the five Orange cases (chest pain or dyspnea without obvious trauma), FDNY-EMS performed significantly better using FDNY-START, correctly triaging 86.3% of cases (over-triage 1.5%; under-triage 12.2%), whereas the non-FDNY-Eagles group using unmodified START correctly triaged 81.5% of cases (over-triage 17.3%; under-triage 1.3%), a difference of 4.9% (95% CI, 1.5-8.2). CONCLUSIONS: The FDNY-START system may allow providers to prioritize casualties using an intermediate category (Orange) more properly aligned to meet patient needs, and as such, may reduce the rates of over-triage compared with START. The FDNY-START system decreases the variability in patient sorting while maintaining high field utility without needing computer assistance or extensive retraining. Comparison of triage algorithms at actual MCIs is needed; however, initial feedback is promising, suggesting that FDNY-START can improve triage with minimal additional training and cost.

Prehospital Stroke Identification: Factors Associated with Diagnostic Accuracy.

BACKGROUND: Stroke patients misdiagnosed by emergency medical services (EMS) providers have been shown to receive delayed in-hospital care. We aim at determining the diagnostic accuracy of Fire Department of New York (FDNY) EMS providers for stroke and identifying potential reasons for misdiagnosis. METHODS: Prehospital care reports of all patients transported by FDNY EMS to 3 hospitals from January 1, 2010, to December 31, 2011, were compared against the American Heart Association Get With The Guidelines (GWTG) database (reference standard) for the diagnosis of stroke. Age-adjusted logistic regression models were generated to explore prehospital patient characteristics which are associated with stroke misdiagnosis. RESULTS: Of 72,984 patient transports during the study period, 750 had a GWTG diagnosis of stroke, 468 (62%) of which were identified correctly in the field and 282 (38%) were missed. An additional 268 patients were misdiagnosed as stroke when in fact they had an alternative diagnosis. Overall sensitivity was 62.4% (95% confidence interval [CI], 58.9-65.8) and specificity was 99.6% (95% CI, 99.6-99.7). No patients who presented with unilateral weakness, facial weakness, or speech problems were missed, whereas patients with atypical complaints like general malaise, dizziness, and headache were more likely to be missed. Seizures led the EMS providers to both overcall a stroke and miss the diagnosis. CONCLUSIONS: FDNY EMS care providers missed more than a third of stroke cases. Seizures and other atypical presentations contribute significantly to stroke misdiagnosis in the field. Our findings highlight the need for better prehospital stroke identification methods.

Prehospital hypoxemia, measured by pulse oximetry, predicts hospital outcomes during the New York City COVID-19 pandemic.

OBJECTIVE: To determine if oxygen saturation (out-of-hospital SpO2), measured by New York City (NYC) 9-1-1 Emergency Medical Services (EMS), was an independent predictor of coronavirus disease 2019 (COVID-19) in-hospital mortality and length of stay, after controlling for the competing risk of death. If so, out-of-hospital SpO2 could be useful for initial triage. METHODS: A population-based longitudinal study of adult patients transported by EMS to emergency departments (ED) between March 5 and April 30, 2020 (the NYC COVID-19 peak period). Inclusion required EMS prehospital SpO2 measurement while breathing room air, transport to emergency department, and a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription polymerase chain reaction test. Multivariable logistic regression modeled mortality as a function of prehospital SpO2, controlling for covariates (age, sex, race/ethnicity, and comorbidities). A competing risk model also was performed to estimate the absolute risks of out-of-hospital SpO2 on the cumulative incidence of being discharged from the hospital alive. RESULTS: In 1673 patients, out-of-hospital SpO2 and age were independent predictors of in-hospital mortality and length of stay, after controlling for the competing risk of death. Among patients ≥66 years old, the probability of death was 26% with an out-of-hospital SpO2 >90% versus 54% with an out-of-hospital SpO2 ≤90%. Among patients <66 years old, the probability of death was 11.5% with an out-of-hospital SpO2 >90% versus 31% with an out-of-hospital SpO2 ≤ 90%. An out-of-hospital SpO2 level ≤90% was associated with over 50% decreased likelihood of being discharged alive, regardless of age. CONCLUSIONS: Out-of-hospital SpO2 and age predicted in-hospital mortality and length of stay: An out-of-hospital SpO2 ≤90% strongly supports a triage decision for immediate hospital admission. For out-of-hospital SpO2 >90%, the decision to admit depends on multiple factors, including age, resource availability (outpatient vs inpatient), and the potential impact of new treatments.