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.

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.

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.

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.

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.

A Statewide Assessment of Prehospital Electrocardiography Approaches of Acquisition and Interpretation for ST-Elevation Myocardial Infarction Based on Emergency Medical Services Characteristics.

Background: The American Heart Association recommends acquiring and interpreting prehospital electrocardiograms (ECG) for patients transported by Emergency Medical Services (EMS) to the emergency department with symptoms highly suspicious of acute coronary syndrome. If interpreted correctly, prehospital ECGs have the potential to improve early detection of ST-elevation myocardial infarction (STEMI) and inform prehospital activation of the cardiac catheterization laboratory, thus reducing total ischemic time and improving patient outcomes. Standardized protocols for prehospital ECG interpretation methods are lacking due to variations in EMS system design, training, and procedures. Objectives: We aimed to describe approaches for prehospital ECG interpretation in EMS systems across North Carolina (NC), and examine potential differences among systems. Methods: A 35-item internet survey was sent to all NC EMS systems (n = 99). Questions pertaining to prehospital ECG interpretation methods included: paramedic, computerized algorithm (i.e., software interpretation), combined approaches, and/or transmission for physician interpretation, transmission capability, cardiac catheterization laboratory activation, and EMS system characteristics (e.g. rural versus urban). Data were summarized and compared. Results: A total of 96 EMS systems across NC responded to the survey (97% response rate); of these, 69% were rural. EMS medical directors (53%) or EMS administrative directors (42%) completed the majority of surveys. While 91% of EMS systems had a prehospital ECG interpretation protocol in place, only 61% had a written cardiac catheterization laboratory activation policy. More than half (55%) of systems reported paramedic interpretation of prehospital ECGs, followed by a combined paramedic and software interpretation approach (39%), physician interpretation (4%), or software interpretation only approach (2%). Nearly 80% of EMS systems transmitted prehospital ECGs to receiving hospitals (always or sometimes), regardless of interpretation method. All EMS systems had some paid versus non-paid EMS personnel and the majority (86%) had both basic and advanced life support capabilities. Conclusions: Most NC EMS systems had a paramedic only ECG interpretation or paramedic in combination with a computerized algorithm approach. Very few used a physician read approach following transmission, even in rural service areas.

Characteristics of Prehospital Electrocardiogram Use in North Carolina Using a Novel Linkage of Emergency Medical Services and Emergency Department Data.

Objective: Prehospital electrocardiography (ECG) is recommended for patients with suspected acute coronary syndrome (ACS), yet only 20-80% of chest pain patients receive a prehospital ECG. Less is known about prehospital ECG use in patients with less common complaints (e.g., fatigue) suspicious for ACS who are transported by emergency medical services (EMS). The aims of this study were to determine: (1) the proportion of patients with chest pain and less typical complaints, and (2) patient characteristics associated with prehospital ECG use in patients transported by EMS to emergency departments across North Carolina. Methods: A novel linked database was created between prehospital and emergency department (ED) patient care data from the North Carolina Prehospital Medical Information System and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool. Institutional review board approval and a data use agreement were received prior to the start of the study. Patients ≥21 transported during 2010-14 by EMS with select variables were included. We examined patients' complaints (symptoms), characteristics (e.g., race, ethnicity, final hospital diagnosis), and prehospital ECG use (yes/no). Analysis included descriptive statistics and mixed logistic regression. Results: During 2010-14, there were 1,967,542 patients with linked EMS-ED data (mean age: 56.9 [SD: 22.2], 43.2% male, 63.7% White). Of these, 643,174 (32.6%) received a prehospital ECG. Patients with prehospital ECG presented with the following complaints: 20% chest pain; 10% shortness of breath; 6% abdominal pain/problems; 6% altered level of consciousness; 5% syncope/dizziness; 4% palpitations; 12% other complaints; and 37% missing. Patients' presenting complaints were the strongest predictor of prehospital ECG use, adjusting for age, sex, race, ethnicity, urbanicity, and date and time of EMS dispatch. Conclusions: Patients with chest pain were significantly more likely to receive a prehospital ECG compared to those with less typical but suspicious complaints for ACS. Patients with less common presentations remain disadvantaged for early triage, risk stratification, and intervention prior to the hospital.

The Case for Drone-assisted Emergency Response to Cardiac Arrest: An Optimized Statewide Deployment Approach.

BACKGROUND Despite evidence linking rapid defibrillation to out-of-hospital cardiac arrest (OHCA) survival, bystander use of automatic external defibrillators (AEDs) remains low, due in part to AED placement and accessibility. AED-equipped drones may improve time-to-defibrillation, yet the benefits and costs are unknown.METHODS We designed drone deployment networks for the state of North Carolina using mathematical optimization models to select drone stations from existing infrastructure by specifying the number of stations and the targeted AED arrival time. Expected outcomes were evaluated over the drone's lifespan (4 years). We estimated the following parameters: proportion of OHCAs within a targeted AED delivery time, bystander utilization of AEDs, survival/neurological status, and incremental cost per quality-adjusted life year (QALY).RESULTS Statewide, 16,503 adults aged 18 or older were expected to experience OHCA with an attempted resuscitation over 4 years. Compared to no drone network, all proposed drone networks were expected to improve survival outcomes. For example, assuming 46% of OHCAs have bystanders willing to use an AED, a 500-drone network decreased the median time of defibrillator arrival from 7.7 to 2.7 minutes compared to no drone network. Expected survival rates doubled (24.5% versus 12.3%), resulting in an additional 30,267 QALYs ($858/incremental QALY). If just 4.5% of OHCAs had willing bystanders, 13.8% of victims would have survived. Sensitivity analysis demonstrated that an AED drone network remained cost-effective over a wide range of assumptions.CONCLUSIONS With proper integration into existing systems, large-scale networks for drone AED delivery have the potential to substantially improve OHCA survival rates while remaining cost-effective. Public health researchers should consider advocating for feasibility studies and policy development surrounding drones.

Patient-reported symptoms improve prediction of acute coronary syndrome in the emergency department.

Early diagnosis is critical in the management of patients with acute coronary syndrome (ACS), particularly ST-elevation myocardial infarction (STEMI), because effective therapies are time-dependent. Aims of this secondary analysis were to determine: (i) the prognostic value of symptoms for an ACS diagnosis in conjunction with electrocardiographic (ECG) and troponin results; and (ii) if any of 13 symptoms were associated with prehospital delay in those presenting to the emergency department (ED) with potential ACS. Patients receiving a cardiac evaluation in the ED were eligible for the study. Thirteen patient-reported symptoms were assessed in triage. Prehospital delay time was calculated as the time from symptom onset until registration in the ED. A total of 1,064 patients were enrolled in five EDs. The sample was 62% male, 70% white, and had a mean age of 60.2 years. Of 474 participants diagnosed with ACS, 118 (25%) had STEMI; 251 (53%) had non-ST elevation myocardial infarction (NSTEMI); and 105 (22%) had unstable angina. Sweating (OR = 1.42 CI [1.01, 2.00]) and shoulder pain (OR = 1.64 CI [1.13, 2.38]) added to the predictive value of an ACS diagnosis when combined with ECG and troponin results. Shortness of breath (OR = 0.71 CI [0.50, 1.00]) and unusual fatigue (OR = 0.60 CI [0.42, 0.84]) were predictive of a non-ACS diagnosis. Sweating predicted shorter prehospital delay (HR = 1.35, CI [1.10, 1.67]); shortness of breath (HR = 0.73 CI [0.60, 0.89]) and unusual fatigue (HR = 0.72, CI [0.57, 0.90]) were associated with longer prehospital delay. Patient-reported symptoms are significantly associated with ACS diagnoses and prehospital delay. Sweating and shoulder pain combined with ECG signs of ischemia may improve the timely detection of ACS in the ED.

Electrocardiographic indicators of acute coronary syndrome are more common in patients with ambulance transport compared to those who self-transport to the emergency department journal of electrocardiology.

INTRODUCTION: The American Heart Association recommends individuals with symptoms suggestive of acute coronary syndrome (ACS) activate the Emergency Medical Services' (EMS) 911 system for ambulance transport to the emergency department (ED), which enables treatment to begin prior to hospital arrival. Despite this recommendation, the majority of patients with symptoms suspicious of ACS continue to self-transport to the ED. The IMMEDIATE AIM study was a prospective study that enrolled individuals who presented to the ED with ischemic symptoms. OBJECTIVES: The purpose of this secondary analysis was to determine differences in patients presenting the ED for possible ACS who arrive by ambulance versus self-transport on: 1) time-to-initial hospital electrocardiogram (ECG), 2) presence of ischemic ECG changes, and 3) patient characteristics. METHODS: Initial 12-lead ECGs acquired upon patient arrival to the ED were evaluated for ST-elevation, ST-depression, and T-wave inversion. ECG signs of ischemia were analyzed both individually and collapsed into an independent dichotomous variable (ED ECG ischemia yes/no) for statistical analysis. Patient characteristics tested included: gender, age, race, ethnicity, English speaking, living alone, mode of transport, and presenting symptoms (chest pain, jaw pain, shortness of breath, nausea/vomiting, syncope, and clinical history). RESULTS: In 1299 patients (mean age 63.9, 46.7% male), 384 (29.6%) patients arrived by ambulance to the ED. The mean time-to-initial ECG was 47minutes for ambulance patients versus 53minutes for self-transport patients (p<0.001). Mode of transport was found to be an independent predictor for time-to-initial ECG controlling for age, gender, and race (p=0.004). There were significantly higher rates of ECG changes of ischemia for patients who arrived by ambulance versus self-transport (p=0.02), and patient characteristics differed by mode of transport to the ED. DISCUSSION: Our findings indicate that less than 30% of individuals with symptoms of ACS activate the EMS '911' system for ambulance transport to the ED. Individuals more likely to activate 911 have timelier ECG but higher rates of ischemic changes, specifically ST-depression and T-wave inversion. Individuals least likely to activate 911 are women, younger individuals, Latino ethnicity, live with a significant other, and those experiencing chest or jaw pain.

Normal prehospital electrocardiography is linked to long-term survival in patients presenting to the emergency department with symptoms of acute coronary syndrome.

AIMS/METHODS: We studied 735 patients who activated "911" for chest pain and/or anginal equivalent symptoms and received 12-lead ECG monitoring with specialized ischemia monitoring software in the ambulance. Prehospital electrocardiograms (PH ECG) were analyzed to determine the proportion of patients who present with completely normal PH ECG findings (absence of ischemia/infarction, arrhythmia, or any other abnormality) and to compare outcomes among patients with and without any PH ECG abnormality. RESULTS: Of 735 patients (mean age 70.5, 52.4% male), 68 (9.3%) patients had completely normal PH ECG findings. They experienced significantly less adverse hospital outcomes (12% vs 37%), length of stay (1.19 vs 3.86 days), and long-term mortality (9% vs 28%) than those with any PH ECG abnormality (p<.05). CONCLUSION: Normal PH ECG findings are associated with better short and long-term outcomes in ambulance patients with ischemic symptoms. These findings may enhance early triage and risk stratification in emergency cardiac care.

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.

Prehospital electrocardiographic manifestations of acute myocardial ischemia independently predict adverse hospital outcomes.

BACKGROUND: Prehospital electrocardiography (PH ECG) is becoming the standard of care for patients activating Emergency Medical Services for symptoms of acute coronary syndrome (ACS). Little is known about the prognostic value of ischemia found on PH ECG. OBJECTIVE: The purpose of this study was to determine whether manifestations of acute myocardial ischemia on PH ECG are predictive of adverse hospital outcomes. METHODS: This study was a retrospective analysis of all PH ECGs recorded in 630 patients who called 911 for symptoms of ACS and were enrolled in a prospective clinical trial. ST-segment monitoring software was added to the PH ECG device with automatic storage and transmission of ECGs to the destination Emergency Department. Patient medical records were reviewed for adverse hospital outcomes. RESULTS: In 630 patients who called 911 for ACS symptoms, 270 (42.9%) had PH ECG evidence of ischemia. Overall, 37% of patients with PH ECG ischemia had adverse hospital outcomes compared with 27% of patients without PH ECG ischemia (p < 0.05). Those with PH ECG ischemia were 1.55 times more likely to have adverse hospital outcomes than those without PH ECG ischemia (95% CI 1.09-2.21; p < 0.05), after controlling for other predictors of adverse hospital outcomes (i.e., age, sex, and medical history). CONCLUSIONS: Evidence of ischemia on PH ECG is an independent predictor of adverse hospital outcomes. ST-segment monitoring in the prehospital setting can identify high-risk patients with symptoms of ACS and provide important prognostic information at presentation to the Emergency Department.

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.

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.

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.