Mary Colleen Bhalla

Prehospital Electrocardiographic Computer Identification of ST-segment Elevation Myocardial Infarction

Abstract Background. Identifying ST-segment elevation myocardial infarctions (STEMIs) in the field can decrease door-to-balloon times. Paramedics may use a computer algorithm to help them interpret prehospital electrocariograms (ECGs). It is unknown how accurately the computer can identify STEMIs. Objectives. To Determine the sensitivity and specificity of prehospital ECGs in identifying patients with STEMI. Methods. Retrospective cross-sectional study of 200 prehospital ECGs acquired using Lifepak 12 monitors and transmitted by one of more than 20 emergency medical services (EMS) agencies to the emergency department (ED) of a Summa Akron City Hospital, a level 1 trauma center between January 1, 2007, and February 18, 2010. The ED sees more than 73,000 adult patients and treats 120 STEMIs annually. The laboratory performs 3,400 catheterizations annually. The first 100 patients with a diagnosis of STEMI and cardiac catheterization laboratory activation from the ED were analyzed. For comparison, a control group of 100 other ECGs from patients without a STEMI were randomly selected from our Medtronic database using a random-number generator. For patients with STEMI, an accurate computer interpretation was “acute MI suspected.” Other interpretations were counted as misses. Specificity and sensitivity were calculated with confidence intervals (CIs). The sample size was determined a priori for a 95% CI of ±10%. Results. Zero control patients were incorrectly labeled “acute MI suspected.” The specificity was 100% (100/100; 95% CI 0.96–1.0), whereas the sensitivity was 58% (58/100; 95% CI 0.48–0.67). This would have resulted in 42 missed cardiac catheterization laboratory activations, but zero inappropriate activations. The most common incorrect interpretation of STEMI ECGs by the computer was “data quality prohibits interpretation,” followed by “abnormal ECG unconfirmed.” Conclusions. Prehospital computer interpretation is not sensitive for STEMI identification and should not be used as a single method for prehospital activation of the cardiac catheterizing laboratory. Because of its high specificity, it may serve as an adjunct to interpretation.

Paramedic ability to recognize ST-segment elevation myocardial infarction on prehospital electrocardiograms.

Abstract Background. Identifying ST-segment elevation myocardial infarctions (STEMIs) by paramedics can decrease door-to-balloon times. While many paramedics are trained to obtain and interpret electrocardiograms (ECGs), it is unknown how accurately they can identify STEMIs. Objective. This study evaluated paramedics’ accuracy in recognizing STEMI on ECGs when faced with potential STEMI mimics. Methods. This was a descriptive cohort study using a survey administered to paramedics. The survey contained questions about training, experience, and confidence, along with 10 ECGs: three demonstrating STEMIs (inferior, anterior, and lateral), two with normal results, and five STEMI mimics (left ventricular hypertrophy [LVH], ventricular pacing, left and right bundle branch blocks [LBBB, RBBB], and supraventricular tachycardia [SVT]). We calculated the overall sensitivity and specificity and the proportion correct with 95% confidence intervals (CIs). Results. We obtained 472 surveys from 30 municipal emergency medical services (EMS) agencies in five counties with 15 medical directors from seven hospitals. The majority (69%) reported ECG training within the preceding year, 31% within six months; and 74% were confident in recognizing STEMIs. The overall sensitivity and specificity for STEMI detection were 75% and 53% (95% CI 73%–77%, 51%–55%), respectively. Ninety-six percent (453/472, 95% CI 94%–98%) correctly identified the inferior myocardial infarction (MI), but only 78% (368/472, 94% CI 74%–82%) identified the anterior MI and 51% (241/472, 46%–56%) the lateral MI. Thirty-seven percent (173/472, 95% CI 32%–41%) of the paramedics correctly recognized LVH, 39% (184/472, 95% CI 35%–44%) LBBB, and 53% (249/472, 95% CI 48%–57%) ventricular pacing as not a STEMI. Thirty-nine percent (185/472, 95% CI 35%–44%) correctly identified all three STEMIs; however, only 3% of the paramedics were correct in all interpretations. The two normal ECGs were recognized as not a STEMI by 97% (459/472, 95% CI 95%–99%) and 100% (472/472, 95% CI 99%–100%). There was no correlation between training, experience, or confidence and accuracy in recognizing STEMIs. Conclusions. Despite training and a high level of confidence, the paramedics in our study were only able to identify an inferior STEMI and two normal ECGs. Given the paramedics’ low sensitivity and specificity, we cannot rely solely on their ECG interpretation to activate the cardiac catheterization laboratory. Future research should involve the evaluation of training programs that include assessment, initial training, testing, feedback, and repeat training.

Simple Triage Algorithm and Rapid Treatment and Sort, Assess, Lifesaving, Interventions, Treatment, and Transportation mass casualty triage methods for sensitivity, specificity, and predictive values ☆ ☆☆

OBJECTIVE Two common mass casualty triage algorithms are Simple Triage Algorithm and Rapid Treatment (START) and Sort, Assess, Lifesaving, Interventions, Treatment, and Transportation (SALT). We sought to determine the START and SALT efficacy in predicting clinical outcome by appropriate triage. METHODS We performed a retrospective chart review of trauma registry of patients from our emergency department (ED). We applied the triage algorithms to 100 patient charts. The end points categories were defined by patient outcomes and the need for intervention: minor/green, discharged without intervention other than minor ED procedure; delayed/yellow, patients get an intervention more than 12 hours after arrival to the ED; immediate/red, patients get an intervention less than 12 hours after arrival; dead/expectant/black, patients die within 48 hours after arrival. RESULTS The mean age was 47 years (range, 17-92 years), and 72% were male. The mechanism of injury was 41% motor vehicle collision, 32% fall, and 16% penetrating trauma. Hospital outcome was 60% minor/green, 5% delayed/yellow, 29% immediate/red, and 6% dead/black. The SALT method resulted in 5 patients overtriaged (95% confidence interval [CI], 1.6-11.2), 30 undertriaged (95% CI, 21.2-40), and 65 met triage level (95% CI, 54.8-74.3). The START method resulted in 12 overtriage (95% CI, 6.4-20), 33 undertriaged (95% CI, 23.9-43.1), and 55 at triage level (95% CI, 44.7-65). Within triage levels, sensitivity ranged from 0% to 92%, specificity from 55% to 100%, positive predictive values from 10% to 100%, and negative predictive value from 65% to 97%. CONCLUSION Overall, neither SALT nor START was sensitive or specific for predicting clinical outcome.

Characteristics of prehospital ST-segment elevation myocardial infarctions.

Abstract Introduction. Despite attention directed at treatment times of ST-segment elevation myocardial infarctions (STEMIs), little is known about the types of STEMIs presenting to the emergency department (ED). Objective. The purpose of this study was to determine the relative frequencies and characteristics of emergency medical services (EMS) STEMIs compared with those in patients who present to the ED by walk-in. This information may be applied in EMS training, system planning, and public education. Methods. This was a query of a prospectively gathered database of all STEMIs in patients presenting to Summa Akron City Hospital ED in 2009 and 2010. We collected demographic information, chief complaint, mode and time of arrival, and STEMI pattern (anterior, lateral, inferior, or posterior). We excluded transfers and in-hospital STEMIs. We calculated means, percentages, significance, and 95% confidence intervals (CIs) ± 10%. Results. We analyzed data from 308 patients. Most patients (241/308, 78%, CI 73%–83%) arrived by EMS, were male (203/308, 66%, CI 60%–71%), and were white (286/308, 93%, CI 89%–96%). Patients arriving by EMS were older (average 63 years, range 35–95) than walk-in patients (average 57 years, range 24–92). Two percent (5/241, 2%, CI 1%–5%) of EMS STEMI patients were under 40 years of age, compared with 10% (7/67, 10%, CI 4%–20%) of walk-in patients (p = 0.0017). The most common chief complaint was chest pain (278/308, 90%, CI 86%–93%). Inferior STEMIs were most common (167/308, 54%, CI 49%–60%), followed by anterior (127/308, 41%, CI 48%–60%), lateral (8/308, 3%, CI 1%–5%), and posterior (6/308, 2%, CI 1%–4%). A day-of-the-week analysis showed that no specific day was most common for STEMI presentation. Forty percent (122/308, 40%, CI 34%–45%) of patients presented during open catheterization laboratory hours (Monday through Friday, 0730–1700 hours). There was no significant statistical difference between EMS and walk-in patients with regard to STEMI pattern or patient demographics. Conclusions. In this study, 95% (294/308) of all STEMIs were inferior or anterior infarctions, and these types of presentations should be stressed in EMS education. Most STEMI patients at this institution arrived by ambulance and during off-hours. Younger patients were more likely to walk in. We need further study, but we may have identified a target population for future interventions. Key words: emergency medical services; allied health personnel; electrocardiography; myocardial infarction; heart catheterization; STEMI

Use of Radio Frequency Identification to Establish Emergency Medical Service Offload Times.

Abstract Emergency medical services (EMS) crews often wait for emergency department (ED) beds to become available to offload their patients. Presently there is no national benchmark for EMS turnaround or offload times, or method for objectively and reliably measuring this. This study introduces a novel method for monitoring offload times and identifying variance. We performed a descriptive, observational study in a large urban community teaching hospital. We affixed radio frequency identification (RFID) tags (Confidex Survivor™, Confidex, Inc., Glen Ellyn, IL) to 65 cots from 19 different EMS agencies and placed a reader (CaptureTech Weatherproof RFID Interpreter, Barcoding Inc., Baltimore, Maryland) in the ED ambulance entrance, allowing for passive recording of traffic. We recorded data for 16 weeks starting December 2009. Offload times were calculated for each visit and analyzed using STATA to show variations in individual and cumulative offload times based on the time of day and day of the week. Results are presented as median times, confidence intervals (CIs), and interquartile ranges (IQRs). We collected data on 2,512 visits. Five hundred and ninety-two were excluded because of incomplete data, leaving 1,920 (76%) complete visits. Average offload time was 13.2 minutes. Median time was 10.7 minutes (IQR 8.1 minutes to 15.4 minutes). A total of 43% of the patients (833/1,920, 95% CI 0.41–0.46) were offloaded in less than 10 minutes, while 27% (513/1,920, 95% CI 0.25–0.29) took greater than 15 minutes. Median times were longest on Mondays (11.5 minutes) and shortest on Wednesdays (10.3 minutes). Longest daily median offload time occurred between 1600 and 1700 (13.5 minutes), whereas the shortest median time was between 0800 and 0900 (9.3 minutes). Cumulative time spent waiting beyond 15 minutes totaled 72.5 hours over the study period. RFID monitoring is a simple and effective means of monitoring EMS traffic and wait times. At our institution, most squads are able to offload their patients within 15 minutes, with many in less than 10 minutes. Variations in wait times are seen and are a topic for future study.

Outcomes of non-STEMI patients transported by emergency medical services vs private vehicle ☆

BACKGROUND Non-ST-segment elevation myocardial infarctions (NSTEMIs) are more common but less studied than ST-segment elevation myocardial infarctions (STEMIs) treated by emergency medical services (EMS). OBJECTIVE The purpose of this study was to evaluate the differences in baseline characteristics and outcomes of NSTEMI patients when arriving by EMS vs self-transport. METHODS We performed a retrospective medical record review of 96 EMS patients and 96 self-transport patients with the diagnosis of NSTEMI based on billing code. RESULTS The mean age of patients arriving by EMS was 75 vs 65 years for self-transport patients (P≤ .000). Patients arriving by self-transport received cardiac catheterization more often than patients arriving by EMS (84% vs 49%, P≤ .001). Emergency medical services patients had significantly longer average hospital length of stay and intensive care unit length of stay than did patients arriving by self-transport (6.5 vs 4 days [P≤ .001] and 4.1 vs 2.7 days [P= .019]). Significantly more EMS patients were discharged to a new extended care facility (25% vs 3.1%, P≤ .001). Finally, more EMS patients died in the hospital (18.8 vs 4.2%, P= .002). CONCLUSIONS Patients with NSTEMI who arrived by EMS are older, are more ill, and have worse outcomes compared with patients who arrived by self-transport. Further research into patient reasoning for mode of transportation to the ED may influence public health interventions, public policy development, and EMS and hospital protocols for management of NSTEMIs. The high mortality in prehospital cohort should prompt further investigation to develop evidence-based protocols.

Improving Bariatric Patient Transport and Care with Simulation

Introduction Obesity is prevalent in the United States. Obese patients have physiologic differences from non-obese individuals. Not only does transport and maintenance of these patients require use of specialized equipment, but it also requires a distinct skill set and knowledge base. To date, there is no literature investigating simulation as a model for educating pre-hospital providers in the care of bariatric patients. The purpose of this study was to determine if a 3-hour educational course with simulation could improve paramedics’ knowledge and confidence of bariatric procedures and transport. This study also examined if prior experience with bariatric transport affected training outcomes. Methods Our study took place in August 2012 during paramedic training sessions. Paramedics completed a pre- and post-test that assessed confidence and knowledge and provided information on previous experience. They had a 30-minute didactic and participated in 2 20-minute hands-on skills portions that reviewed procedural issues in bariatric patients, including airway procedures, peripheral venous and intraosseous access, and cardiopulmonary resuscitation. Study participants took part in one of two simulated patient encounters. Paramedics were challenged with treating emergent traumatic and/or medical conditions, as well as extricating and transporting bariatric patients. Each group underwent a debriefing of the scenario immediately following their case. We measured confidence using a 5-point Likert-type response scale ranging from 1 (strongly disagree) to 5 (strongly agree) on a 7-item questionnaire. We assessed knowledge with 12 multiple choice questions. Paired-sample t-tests were used to compare pre- and post-simulation confidence and knowledge with a significance level of p≤0.05. We used analysis of covariance to examine the effect of previous experiences on pre-and post-educational activity confidence and knowledge with a significance level of p ≤0.05. Proportions and 95% confidence intervals are presented as appropriate. We determined the magnitude of significant pre-post differences with Cohen’s d. We assessed scale reliability using Cronbach’s alpha and was found to be reliable with scores of 0.83 and 0.88 across pre- and post-test responses, respectively. Results Participants exhibited a significant increase in confidence in performing procedures (p<0.01) and knowledge of bariatric patient management (p<0.001) after the simulation. The current study also found an increase in knowledge of transport, vascular access/circulation and airway management (p<0.001). Participant background showed no effects on these changes. Conclusion This study suggests that simulation paired with a didactic is an effective method of education for paramedics caring for and transporting bariatric patients. The data show a significant increase in knowledge and confidence with a 3-hour training session, irrespective of previous training or experience with bariatric patients. This is the first study of its kind to apply simulation training for the pre-hospital care of bariatric patients.

Pulmonary embolism and heparin-induced thrombocytopenia successfully treated with tissue plasminogen activator and argatroban.

Heparin-induced thrombocytopenia (HIT) is a disorder characterized by antibodies formed against the heparin-platelet factor 4 (PF4) complex that results in thrombosis and platelet consumption. It can lead to extensive thromboembolic disease and coagulopathy. Diagnosis remains a challenge, but there are now assays that can be used for confirmation. A 56 year old female presented to the emergency department with a complaint of shortness of breath. She had been hospitalized five weeks prior for a laparatomy, which was complicated by pneumonia requiring intubation, and deep vein thrombosis. Initial platelet level was 63 x 10/L after being 275 upon discharge. Computed tomography angiography revealed massive bilateral saddle pulmonary emboli. She was hemodynamically stable when she was started on a heparin bolus and drip, but it was then revealed that she had received low molecular weight heparin (LMWH) as an outpatient. Her blood pressure dropped and the heparin was discontinued. She was given 100 mg of tissue plasminogen activator (tPA) over one hour rather than two, with symptomatic improvement. She was treated with argatroban and later tested positive for antibodies against the heparin-PF4 complex which confirmed the diagnosis of HIT. She was converted to and discharged on warfarin and has done well. This case demonstrates the proper diagnosis and workup for pulmonary embolism caused by HIT, with successful treatment of both the underlying disease and its life-threatening complications. It demonstrates that, though the risk of HIT is less with LMWH than for heparin, it is not zero.