Selective classification with machine learning uncertainty estimates improves ACS prediction: A retrospective study in the prehospital setting.

Accurate identification of acute coronary syndrome (ACS) in the prehospital sestting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in or rule-out ACS. Our goal is to identify a method that bridges this gap. To do so, we retrospectively evaluate two promising approaches, an ensemble of gradient boosted decision trees (GBDT) and selective classification (SC) on consecutive patients transported by ambulance to the ED with chest pain and/or anginal equivalents. On the task of ACS classification with 23 prehospital covariates, we found the fusion of the two (GBDT+SC) improves the best reported sensitivity and specificity by 8% and 23% respectively. Accordingly, GBDT+SC is safer than current machine learning approaches to rule-in and rule-out of ACS in the prehospital setting.

Challenges & barriers for real-time integration of drones in emergency cardiac care: Lessons from the United States, Sweden, & Canada.

Importance: Out-of-hospital cardiac arrest (OHCA) is a leading cause of morbidity and mortality in the US and Europe (∼600,000 incident events annually) and around the world (∼3.8 million). With every minute that passes without cardiopulmonary resuscitation or defibrillation, the probability of survival decreases by 10%. Preliminary studies suggest that uncrewed aircraft systems, also known as drones, can deliver automated external defibrillators (AEDs) to OHCA victims faster than ground transport and potentially save lives. Objective: To date, the United States (US), Sweden, and Canada have made significant contributions to the knowledge base regarding AED-equipped drones. The purpose of this Special Communication is to explore the challenges and facilitators impacting the progress of AED-equipped drone integration into emergency medicine research and applications in the US, Sweden, and Canada. We also explore opportunities to propel this innovative and important research forward. Evidence review: In this narrative review, we summarize the AED-drone research to date from the US, Sweden, and Canada, including the first drone-assisted delivery of an AED to an OHCA. Further, we compare the research environment, emergency medical systems, and aviation regulatory environment in each country as they apply to OHCA, AEDs, and drones. Finally, we provide recommendations for advancing research and implementation of AED-drone technology into emergency care. Findings: The rates that drone technologies have been integrated into both research and real-life emergency care in each country varies considerably. Based on current research, there is significant potential in incorporating AED-equipped drones into the chain of survival for OHCA emergency response. Comparing the different environments and systems in each country revealed ways that each can serve as a facilitator or barrier to future AED-drone research. Conclusions and relevance: The US, Sweden, and Canada each offers different challenges and opportunities in this field of research. Together, the international community can learn from one another to optimize integration of AED-equipped drones into emergency systems of care.

Emergency Nurses' Recognition of and Perception of Sex Differences in Acute Coronary Syndrome Symptoms.

INTRODUCTION: Emergency nurses must quickly identify patients with potential acute coronary syndrome. However, no recent nationwide research has explored nurses' knowledge of acute coronary syndrome symptoms. The purpose of this study was to explore emergency nurses' recognition of acute coronary syndrome symptoms, including whether nurses attribute different symptoms to women and men. METHODS: We used a cross-sectional, descriptive design using an online survey. Emergency nurses from across the United States were recruited using postcards and a posting on the Emergency Nurses Association website. Demographic data and participants' recognition of acute coronary syndrome symptoms, using the Acute Coronary Syndrome Symptom Checklist, were collected. Descriptive statistics and ordinal regression were used to analyze the data. RESULTS: The final sample included 448 emergency nurses with a median 7.0 years of emergency nursing experience. Participants were overwhelmingly able to recognize common acute coronary syndrome symptoms, although some symptoms were more often associated with women or with men. Most participants believed that women and men's symptoms were either "slightly different" (41.1%) or "fairly different" (42.6%). Nurses who completed training for the triage role were significantly less likely to believe that men and women have substantially different symptoms (odds ratio 0.47; 95% CI 0.25-0.87). DISCUSSION: Emergency nurses were able to recognize common acute coronary syndrome symptoms, but some reported believing that the symptom experience of men and women is more divergent than what is reported in the literature.

Ventricular tachycardia and in-hospital mortality in the intensive care unit.

Background: Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently. Objective: The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients. Methods: We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality. Results: We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73). Conclusion: Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.

Race-Based Differences in ST-Segment-Elevation Myocardial Infarction Process Metrics and Mortality From 2015 Through 2021: An Analysis of 178 062 Patients From the American Heart Association Get With The Guidelines-Coronary Artery Disease Registry.

BACKGROUND: Systems of care have been developed across the United States to standardize care processes and improve outcomes in patients with ST-segment-elevation myocardial infarction (STEMI). The effect of contemporary STEMI systems of care on racial and ethnic disparities in achievement of time-to-treatment goals and mortality in STEMI is uncertain. METHODS: We analyzed 178 062 patients with STEMI (52 293 women and 125 769 men) enrolled in the American Heart Association Get With The Guidelines-Coronary Artery Disease registry between January 1, 2015, and December 31, 2021. Patients were stratified into and outcomes compared among 3 racial and ethnic groups: non-Hispanic White, Hispanic White, and Black. The primary outcomes were the proportions of patients achieving the following STEMI process metrics: prehospital ECG obtained by emergency medical services; hospital arrival to ECG obtained within 10 minutes for patients not transported by emergency medical services; arrival-to-percutaneous coronary intervention time within 90 minutes; and first medical contact-to-device time within 90 minutes. A secondary outcome was in-hospital mortality. Analyses were performed separately in women and men, and all outcomes were adjusted for age, comorbidities, acuity of presentation, insurance status, and socioeconomic status measured by social vulnerability index based on patients' county of residence. RESULTS: Compared with non-Hispanic White patients with STEMI, Hispanic White patients and Black patients had lower odds of receiving a prehospital ECG and achieving targets for door-to-ECG, door-to-device, and first medical contact-to-device times. These racial disparities in treatment goals were observed in both women and men, and persisted in most cases after multivariable adjustment. Compared with non-Hispanic White women, Hispanic White women had higher adjusted in-hospital mortality (odds ratio, 1.39 [95% CI, 1.12-1.72]), whereas Black women did not (odds ratio, 0.88 [95% CI, 0.74-1.03]). Compared with non-Hispanic White men, adjusted in-hospital mortality was similar in Hispanic White men (odds ratio, 0.99 [95% CI, 0.82-1.18]) and Black men (odds ratio, 0.96 [95% CI, 0.85-1.09]). CONCLUSIONS: Race- or ethnicity-based disparities persist in STEMI process metrics in both women and men, and mortality differences are observed in Hispanic White compared with non-Hispanic White women. Further research is essential to evolve systems of care to mitigate racial differences in STEMI outcomes.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Integrating multimodal information in machine learning for classifying acute myocardial infarction.

Objective. Prompt identification and recognization of myocardial ischemia/infarction (MI) is the most important goal in the management of acute coronary syndrome. The 12-lead electrocardiogram (ECG) is widely used as the initial screening tool for patients with chest pain but its diagnostic accuracy remains limited. There is early evidence that machine learning (ML) algorithms applied to ECG waveforms can improve performance. Most studies are designed to classify MI from healthy controls and thus are limited due to the lack of consideration of ECG abnormalities from other cardiac conditions, leading to false positives. Moreover, clinical information beyond ECG has not yet been well leveraged in existing ML models.Approach.The present study considered downstream clinical implementation scenarios in the initial model design by dichotomizing study recordings from a public large-scale ECG dataset into a MI class and a non-MI class with the inclusion of MI-confounding conditions. Two experiments were conducted to systematically investigate the impact of two important factors entrained in the modeling process, including the duration of ECG, and the value of multimodal information for model training. A novel multimodal deep learning architecture was proposed to learn joint features from both ECG and patient demographics.Main results.The multimodal model achieved better performance than the ECG-only model, with a mean area under the receiver operating characteristic curve of 92.1% and a mean accuracy of 87.4%, which is on par with existing studies despite the increased task difficulty due to the new class definition. By investigation of model explainability, it revealed the contribution of patient information in model performance and clinical concordance of the model's attention with existing clinical insights.Significance.The findings in this study help guide the development of ML solutions for prompt MI detection and move the models one step closer to real-world clinical applications.

An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients.

BACKGROUND: False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies. PURPOSE: The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group. METHODS: The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO2 , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations. RESULTS: Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both. CONCLUSIONS: The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Patient characteristics and outcomes of acute myocardial infarction presenting without ischemic pain: Insights from the Atherosclerosis Risk in Communities Study.

Background: Our objective was to describe characteristics of patients presenting with and without ischemic pain among those diagnosed with acute myocardial infarction (MI) using individual-level data from the Atherosclerosis Risk in Communities Study from 2005 to 2019. Methods: Acute MI included events deemed definite or probable MI by a physician panel based on ischemic pain, cardiac biomarkers, and ECG evidence. Patient characteristics included age at hospitalization, sex, race/ethnicity, comorbidities (smoking status, diabetes, hypertension, history of previous stroke, MI, or cardiovascular procedure, and history of valvular disease or cardiomyopathy) and in-hospital complications occurring during the event of interest (pulmonary edema, pulmonary embolism, in-hospital stroke, pneumonia, cardiogenic shock, ventricular fibrillation). Analyses were stratified by MI subtype (STEMI, NSTEMI, Unclassified) and patient characteristics and 28-day case fatality was compared between MI presenting with or without ischemic pain. Results: Between 2005 and 2019, there were 1711 hospitalized definite/probable MI events (47 % female, 26 % black, and age of 78 [6.7 years]). A smaller proportion of STEMI patients presented without ischemic pain compared to NSTEMI patients (20 % vs 32 %). Race, sex, age, and comorbidity profiles did not differ significantly across ischemic pain presentations. Patients presenting without ischemic pain had a higher 28-day all-cause case fatality after adjusting for age, race, sex, and comorbidities. However, after further adjustment, time from symptom onset to hospital arrival, time to treatment, and in-hospital complications explained the difference in 28-day case fatality between ischemic pain presentations. Conclusions: Future research should focus on differences in treatment delay across ischemic pain presentations rather than sex differences in acute coronary syndrome presentation.

Treatment Time and In-Hospital Mortality Among Patients With ST-Segment Elevation Myocardial Infarction, 2018-2021.

Importance: Recognizing the association between timely treatment and less myocardial injury for patients with ST-segment elevation myocardial infarction (STEMI), US national guidelines recommend specific treatment-time goals. Objective: To describe these process measures and outcomes for a recent cohort of patients. Design, Setting, and Participants: Cross-sectional study of a diagnosis-based registry between the second quarter of 2018 and the third quarter of 2021 for 114 871 patients with STEMI treated at 648 hospitals in the Get With The Guidelines-Coronary Artery Disease registry. Exposures: STEMI or STEMI equivalent. Main Outcomes and Measures: Treatment times, in-hospital mortality, and adherence to system goals (75% treated ≤90 minutes of first medical contact if the first hospital is percutaneous coronary intervention [PCI]-capable and ≤120 minutes if patients require transfer to a PCI-capable hospital). Results: In the study population, median age was 63 (IQR, 54-72) years, 71% were men, and 29% were women. Median time from symptom onset to PCI was 148 minutes (IQR, 111-226) for patients presenting to PCI-capable hospitals by emergency medical service, 195 minutes (IQR, 127-349) for patients walking in, and 240 minutes (IQR, 166-402) for patients transferred from another hospital. Adjusted in-hospital mortality was lower for those treated within target times vs beyond time goals for patients transported via emergency medical services (first medical contact to laboratory activation ≤20 minutes [in-hospital mortality, 3.6 vs 9.2] adjusted OR, 0.54 [95% CI, 0.48-0.60], and first medical contact to device ≤90 minutes [in-hospital mortality, 3.3 vs 12.1] adjusted OR, 0.40 [95% CI, 0.36-0.44]), walk-in patients (hospital arrival to device ≤90 minutes [in-hospital mortality, 1.8 vs 4.7] adjusted OR, 0.47 [95% CI, 0.40-0.55]), and transferred patients (door-in to door-out time <30 minutes [in-hospital mortality, 2.9 vs 6.4] adjusted OR, 0.51 [95% CI, 0.32-0.78], and first hospital arrival to device ≤120 minutes [in-hospital mortality, 4.3 vs 14.2] adjusted OR, 0.44 [95% CI, 0.26-0.71]). Regardless of mode of presentation, system goals were not met in most quarters, with the most delayed system performance among patients requiring interhospital transfer (17% treated ≤120 minutes). Conclusions and Relevance: This study of patients with STEMI included in a US national registry provides information on changes in process and outcomes between 2018 and 2021.

Emergency Interhospital Transfer of Patients With ST-Segment-Elevation Myocardial Infarction: Call 9-1-1-The American Heart Association Mission: Lifeline Program.

The American Heart Association Mission: Lifeline program objectives are to improve the quality of care and outcomes for patients with ST-segment-elevation myocardial infarction. Every minute of delay in treatment adversely affects 1-year mortality. Transfer of patients safely and timely to hospitals with primary percutaneous coronary intervention capability is needed to improve outcomes. But treatment times continue to show delays, especially during interhospital transfers. A simple 3-step process of an interhospital "Call 9-1-1" protocol may expedite this process. This STAT TRANSFER process uses a systems approach that considers diverse ways in which patients access care, how EMS responds and determines destinations, how referring hospital transfers are performed, urban and rural differences, and how receiving hospitals prepare for an incoming patient with ST-segment-elevation myocardial infarction. This initiative suggests a strategy to reduce variability in interhospital transfer times using a STAT TRANSFER and a Call 9-1-1 process in a system of care that involves all stakeholders.

There is little association between prehospital delay, persistent symptoms, and post-discharge healthcare utilization in patients evaluated for acute coronary syndrome.

AIMS: Test for an association between prehospital delay for symptoms suggestive of acute coronary syndrome (ACS), persistent symptoms, and healthcare utilization (HCU) 30-days and 6-months post hospital discharge. BACKGROUND: Delayed treatment for ACS increases patient morbidity and mortality. Prehospital delay is the largest factor in delayed treatment for ACS. METHODS: Secondary analysis of data collected from a multi-center prospective study. Included were 722 patients presenting to the Emergency Department (ED) with symptoms that triggered a cardiac evaluation. Symptoms and HCU were measured using the 13-item ACS Symptom Checklist and the Froelicher's Health Services Utilization Questionnaire-Revised instrument. Logistic regression models were used to examine hypothesized associations. RESULTS: For patients with ACS (n = 325), longer prehospital delay was associated with fewer MD/NP visits (OR, 0.986) at 30 days. Longer prehospital delay was associated with higher odds of calling 911 for any reason (OR, 1.015), and calling 911 for chest related symptoms (OR, 1.016) 6 months following discharge. For non-ACS patients (n = 397), longer prehospital delay was associated with higher odds of experiencing chest pressure (OR, 1.009) and chest discomfort (OR, 1.008) at 30 days. At 6 months, longer prehospital delay was associated with higher odds of upper back pain (OR, 1.013), palpitations (OR 1.014), indigestion (OR, 1.010), and calls to the MD/NP for chest symptoms (OR, 1.014). CONCLUSIONS: There were few associations between prehospital delay and HCU for patients evaluated for ACS in the ED. Associations between prolonged delay and persistent symptoms may lead to increased HCU for those without ACS.

Out-of-Hospital Cardiac Arrest Bystander Defibrillator Search Time and Experience With and Without Directional Assistance: A Randomized Simulation Trial in a Community Setting.

INTRODUCTION: Probability of survival after out-of-hospital cardiac arrest (OHCA) doubles when a bystander initiates cardiopulmonary resuscitation and uses an automated external defibrillator (AED) rapidly. National, state, and community efforts have increased placement of AEDs in public spaces; however, bystander AED use remains less than 2% in the United States. Little is known about the effect of giving bystanders directional assistance to the closest public access AED. METHODS: We conducted 35 OHCA simulations using a life-sized manikin with participants aged 18 through 65 years who searched for public access AEDs in 5 zones on a university campus. Zones varied by challenges to pedestrian AED acquisition and number of fixed AEDs. Participants completed 2 searches-first unassisted and then with verbal direction to the closest AED-and we compared AED delivery times. We conducted pretest and posttest surveys. RESULTS: In all 5 zones, the median time from simulated OHCA onset to AED delivery was lower when the bystander received directional assistance. Time savings (minutes:seconds) varied by zone, ranging from a median of 0:53 (P = 0.14) to 3:42 (P = 0.02). Only 3 participants immediately located the closest AED without directional assistance; more than half reported difficulty locating an AED. CONCLUSIONS: These findings may inform strategies to ensure that AEDs are consistently marked and placed in visible, accessible locations. Continued emphasis on developing strategies to improve lay bystanders' ability to locate and use AEDs may improve AED retrieval times and OHCA outcomes.

Impact of Using Drones in Emergency Medicine: What Does the Future Hold?

The use of unmanned aerial vehicles or "drones" has expanded in the last decade, as their technology has become more sophisticated, and costs have decreased. They are now used routinely in farming, environmental surveillance, public safety, commercial product delivery, recreation, and other applications. Health-related applications are only recently becoming more widely explored and accepted. The use of drone technology in emergency medicine is especially promising given the need for a rapid response to enhance patient outcomes. The purpose of this paper is to describe some of the main current and expanding applications of drone technology in emergency medicine and to describe challenges and future opportunities. Current applications being studied include delivery of defibrillators in response to out-of-hospital cardiac arrest, blood and blood products in response to trauma, and rescue medications. Drones are also being studied and actively used in emergency response to search and rescue operations as well as disaster and mass casualty events. Current challenges to expanding their use in emergency medicine and emergency medical system (EMS) include regulation, safety, flying conditions, concerns about privacy, consent, and confidentiality, and details surrounding the development, operation, and maintenance of a medical drone network. Future research is needed to better understand end user perceptions and acceptance. Continued technical advances are needed to increase payload capacities, increase flying distances, and integrate drone networks into existing 9-1-1 and EMS systems. Drones are a promising technology for improving patient survival, outcomes, and quality of life, particularly for those in areas that are remote or that lack funds or infrastructure. Their cost savings compared with ground transportation alone, speed, and convenience make them particularly applicable in the field of emergency medicine. Research to date suggests that use of drones in emergency medicine is feasible, will be accepted by the public, is cost-effective, and has broad application.

Systems of Care for ST-Segment-Elevation Myocardial Infarction: A Policy Statement From the American Heart Association.

The introduction of Mission: Lifeline significantly increased timely access to percutaneous coronary intervention for patients with ST-segment-elevation myocardial infarction (STEMI). In the years since, morbidity and mortality rates have declined, and research has led to significant developments that have broadened our concept of the STEMI system of care. However, significant barriers and opportunities remain. From community education to 9-1-1 activation and emergency medical services triage and from emergency department and interfacility transfer protocols to postacute care, each critical juncture presents unique challenges for the optimal care of patients with STEMI. This policy statement sets forth recommendations for how the ideal STEMI system of care should be designed and implemented to ensure that patients with STEMI receive the best evidence-based care at each stage in their illness.

Prehospital ECG with ST-depression and T-wave inversion are associated with new onset heart failure in individuals transported by ambulance for suspected acute coronary syndrome.

BACKGROUND: Prehospital electrocardiogram(s) (ECG) can improve early detection of acute coronary syndrome (ST-segment elevation myocardial infarction [STEMI], non-STEMI, and unstable angina) and inform prehospital activation of cardiac catheterization lab; thus, reducing total ischemic time and improving patient outcomes. Less is known, however, about the association of prehospital ECG ischemic findings and long term adverse clinical events. With this in mind, this study was designed to examine the: 1) frequency of prehospital ECGs for acute myocardial ischemia (ST-elevation, ST-depression, and/or T-wave inversion); and, 2) whether any of these specific ECG features are associated with adverse clinical events within 30 day of initial presentation to the emergency department (ED). METHODS: We included consecutive patients ≥ 21 years during a five-year period (2013-2017), who were transported by ambulance to the ED with non-traumatic chest pain and/or anginal equivalent(s) and had a prehospital 12­lead ECG. Two cardiologists (LG, EC), blinded to clinical data, interpreted the 12­lead ECGs applying current guideline based ischemia criteria. Adverse clinical events, return to ED, and rehospitalization evaluated at 30-days. RESULTS: We identified 3646 patients (mean age, 59.7 years ±15.7; 45% female) with ECGs, of which N = 3587 had data on the three ischemic markers of interest. Of these, 1762 (49.1%) had ECG evidence of ischemia. In adjusted logistic regression models, those with T-wave inversion had a higher odds (OR = 1.59) of new onset heart failure, while ST-elevation was associated with lower odds (OR = 0.69). Patients with ST-depression had higher odds of new onset heart failure and death within 30 days (OR = 1.29, 1.49 respectively), but this association attenuated after controlling for other ECG features. CONCLUSIONS: ST-depression and/or T-wave inversion are independent predictors of new onset heart failure, within 30 days of initial ED presentation. Our study in a large cohort of patients, suggests that using ECG ST-elevation alone may not capture patients with ischemia who may benefit from aggressive anti-ischemic therapies to reduce myocardial damage with resultant heart failure.

Disparities in Emergency Medical Services Time Intervals for Patients with Suspected Acute Coronary Syndrome: Findings from the North Carolina Prehospital Medical Information System.

Background Timely emergency medical services (EMS) response, management, and transport of patients with suspected acute coronary syndrome (ACS) significantly reduce delays to emergency treatment and improve outcomes. We evaluated EMS response, scene, and transport times and adherence to proposed time benchmarks for patients with suspected ACS in North Carolina from 2011 to 2017. Methods and Results We conducted a population-based, retrospective study with the North Carolina Prehospital Medical Information System, a statewide electronic database of all EMS patient care reports. We analyzed 2011 to 2017 data on patient demographics, incident characteristics, EMS care, and county population density for EMS-suspected patients with ACS, defined as a complaint of chest pain or suspected cardiac event and documentation of myocardial ischemia on prehospital ECG or prehospital activation of the cardiac care team. Descriptive statistics for each EMS time interval were computed. Multivariable logistic regression was used to quantify relationships between meeting response and scene time benchmarks (11 and 15 minutes, respectively) and prespecified covariates. Among 4667 patients meeting eligibility criteria, median response time (8 minutes) was shorter than median scene (16 minutes) and transport (17 minutes) time. While scene times were comparable by population density, patients in rural (versus urban) counties experienced longer response and transport times. Overall, 62% of EMS encounters met the 11-minute response time benchmark and 49% met the 15-minute scene time benchmark. In adjusted regression analyses, EMS encounters of older and female patients and obtaining a 12-lead ECG and venous access were independently associated with lower adherence to the scene time benchmark. Conclusions Our statewide study identified urban-rural differences in response and transport times for suspected ACS as well as patient demographic and EMS care characteristics related to lower adherence to scene time benchmark. Strategies to reduce EMS scene times among patients with ACS need to be developed and evaluated.

Unusual Fatigue and Failure to Utilize EMS Are Associated With Prolonged Prehospital Delay for Suspected Acute Coronary Syndrome.

BACKGROUND: Rapid reperfusion reduces infarct size and mortality for acute coronary syndrome (ACS), but efficacy is time dependent. The aim of this study was to determine if transportation factors and clinical presentation predicted prehospital delay for suspected ACS, stratified by final diagnosis (ACS vs. no ACS). METHODS: A heterogeneous sample of emergency department (ED) patients with symptoms suggestive of ACS was enrolled at 5 US sites. Accelerated failure time models were used to specify a direct relationship between delay time and variables to predict prehospital delay by final diagnosis. RESULTS: Enrolled were 609 (62.5%) men and 366 (37.5%) women, predominantly white (69.1%), with a mean age of 60.32 (±14.07) years. Median delay time was 6.68 (confidence interval 1.91, 24.94) hours; only 26.2% had a prehospital delay of 2 hours or less. Patients presenting with unusual fatigue [time ratio (TR) = 1.71, P = 0.002; TR = 1.54, P = 0.003, respectively) or self-transporting to the ED experienced significantly longer prehospital delay (TR = 1.93, P < 0.001; TR = 1.71, P < 0.001, respectively). Predictors of shorter delay in patients with ACS were shoulder pain and lightheadedness (TR = 0.65, P = 0.013 and TR = 0.67, P = 0.022, respectively). Predictors of shorter delay for patients ruled out for ACS were chest pain and sweating (TR = 0.071, P = 0.025 and TR = 0.073, P = 0.032, respectively). CONCLUSION: Patients self-transporting to the ED had prolonged prehospital delays. Encouraging the use of EMS is important for patients with possible ACS symptoms. Calling 911 can be positively framed to at-risk patients and the community as having advanced care come to them because EMS capabilities include 12-lead ECG acquisition and possibly high-sensitivity troponin assays.