Sepsis alerts in EMS and the results of pre-hospital ETCO2.

BACKGROUND: Field sepsis alerts have the ability to expedite initial ED sepsis treatment. Our hypothesis is that in patients that meet EMS sepsis alert criteria there is a strong relationship between prehospital end-tidal carbon dioxide (ETCO2) readings and the outcome of diagnosed infection. METHODS: In 2014, our EMS service initiated a protocol requiring hospitals to receive notification of a "sepsis alert" on all suspected sepsis patients. The EMS service transports 70,000 patients/year to a number of urban centers. All patients transported to our major urban teaching hospital by our EMS service in one year in which a sepsis alert was announced were included in this study. The primary outcome variable was diagnosed infection and secondary outcomes were hospital admission, ICU admission and mortality. Positive lactate was defined as >4.0 mmol/L. ROC curve analysis was used to define the best cutoff for ETCO2. RESULTS: 351 patients were announced as EMS sepsis alert patients and transported to our center over a one year period. Positive outcomes were as follows: diagnosed infection in 28% of patients, hospital admission in 63% and ICU admission in 11%. The correlation between lactate and ETCO2 was -0.45. A ROC curve analysis of ETCO2 vs. lactate >4 found that the best cutoff to predict a high lactate was an ETCO2 of 25 or less, which was considered a positive ETCO2 (AUC = 0.73). 27% of patients had a positive ETCO2 and 24% had a positive lactate. A positive ETCO2 predicted a positive lactate with 76% accuracy, 63% sensitivity and 80% specificity. 27% of those with a positive ETCO2 and 44% of those with a positive lactate had a diagnosed infection. 59% of those with a positive ETCO2 and 89% of those with a positive lactate had admission to the hospital. 15% of those with a positive ETCO2 and 18% of those with a positive lactate had admission to the ICU. Neither lactate nor ETCO2 were predictive of an increased risk for diagnosed infection, hospital admission or ICU admission in this patient population. CONCLUSION: While ETCO2 predicted the initial ED lactate levels it did not predict diagnosed infection, admission to the hospital or ICU admission in our patient population but did predict mortality.

Ondansetron oral dissolving tablets are superior to normal saline alone for prehospital nausea.

BACKGROUND: Antiemetics have been shown to be effective in multiple hospital settings, but few studies have been done in the prehospital environment. OBJECTIVES: Our hypotheses were 1) that the amount of normal saline administered during an emergency medical services (EMS) transport was not related to a change in nausea and vomiting and 2) that the addition of the ondansetron orally disintegrating tablet (ODT) would decrease the degree of nausea. METHODS: This was a pre-post study of two cohorts of consecutive patients with nausea in the prehospital setting. During phase 1 of the study, our local EMS agency adopted a protocol form to complete whenever a patient with nausea and/or vomiting was assessed and transported to one of the area hospitals. Patients were asked to rate their nausea on a visual analog scale (VAS) and a Likert scale, and saline administration and active vomiting were documented. During phase 2, our EMS system adopted the use of ondansetron ODT for nausea and continued to complete the same forms. The nausea forms completed by EMS during phase 1 (saline only) and phase 2 (ondansetron ODT) were evaluated and compared. For both phases, the primary outcome measures were the change in VAS nausea rating (0 = no nausea, 100 = most nausea imaginable) from beginning to end of the transport and the results on the Likert scale completed at the end of the transport. Relationships were considered significant if p < 0.01. RESULTS: Data were collected from 274 transports in phase 1 and 372 transports in phase 2. The average patient age was 50 ± 12 years. In phase 1 of the study, 178 of 274 patients (65%) received normal saline (mean volume ± standard deviation = 265 ± 192 mL). There was no significant correlation between the VAS change and the amount of fluid administration in either phase of the study. Conversely, during phase 2, patients receiving ondansetron ODT showed significant improvement in both measures of nausea. The difference in nausea improvement between phase 1 and phase 2 was significant (difference in VAS change: 24.6; 95% confidence interval 20.9, 28.3). CONCLUSION: There was no improvement in patient nausea related to quantity of saline alone during an EMS transport. The addition of ondansetron ODT resulted in a significant improvement in degree of nausea.

The use of chemical restraints reduces agitation in patients transported by emergency medical services.

BACKGROUND: Agitated patients are the primary source of injury to patients and providers during ambulance transport. OBJECTIVE: Our primary hypothesis was that the addition of a chemical restraint agent (midazolam) to a restraint protocol would reduce agitation to a greater extent than a restraint protocol with physical restraint alone. METHODS: The local emergency medical services restraint protocol (RP) was implemented on October 1, 2006. It included a form for data collection about each restrained patient. On April 1, 2007, chemical restraint (CR) using midazolam in addition to physical restraints was made available through the RP, and paramedics were educated in its use. Transported patients were divided into pre-CR and post-CR. The post-CR group was split into those who received and those who did not receive midazolam. Agitation was measured on a validated agitation behavior scale with a parametric (Rasch) adjustment. RESULTS: There were 96 patients in the pre-CR group and 522 patients in the post-CR group. Forty-three percent of the pre-CR group and 49% of the post-CR group had a decrease in agitation during transport (NS). Of the 522 in the post-CR group, 110 were physically restrained and given midazolam (21%) and 412 were physically restrained without midazolam (79%). There was a significantly greater decrease in agitation scores (-17 ± 21 vs. -7 ± 17) in the subjects receiving midazolam compared to those who did not. CONCLUSION: If available, CR is used in about 20% of restrained patients. When CR is used, there is a decrease in the subject's agitation.

Computer-aided diagnosis with potential application to rapid detection of disease outbreaks.

Our objectives are to quickly interpret symptoms of emergency patients to identify likely syndromes and to improve population-wide disease outbreak detection. We constructed a database of 248 syndromes, each syndrome having an estimated probability of producing any of 85 symptoms, with some two-way, three-way, and five-way probabilities reflecting correlations among symptoms. Using these multi-way probabilities in conjunction with an iterative proportional fitting algorithm allows estimation of full conditional probabilities. Combining these conditional probabilities with misdiagnosis error rates and incidence rates via Bayes theorem, the probability of each syndrome is estimated. We tested a prototype of computer-aided differential diagnosis (CADDY) on simulated data and on more than 100 real cases, including West Nile Virus, Q fever, SARS, anthrax, plague, tularaemia and toxic shock cases. We conclude that: (1) it is important to determine whether the unrecorded positive status of a symptom means that the status is negative or that the status is unknown; (2) inclusion of misdiagnosis error rates produces more realistic results; (3) the naive Bayes classifier, which assumes all symptoms behave independently, is slightly outperformed by CADDY, which includes available multi-symptom information on correlations; as more information regarding symptom correlations becomes available, the advantage of CADDY over the naive Bayes classifier should increase; (4) overlooking low-probability, high-consequence events is less likely if the standard output summary is augmented with a list of rare syndromes that are consistent with observed symptoms, and (5) accumulating patient-level probabilities across a larger population can aid in biosurveillance for disease outbreaks.