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Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain. Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.

Acute stress may impair cognitive performance and multitasking, both vital in the practice of emergency medicine (EM). Previous research has demonstrated that board-certified emergency physicians experience physiologic stress while working clinically. We sought to determine whether EM residents have a similar stress response, and hypothesized that residents experience acute stress while working clinically. We performed a prospective observational study of physiologic stress including heart rate (HR), heart rate variability (HRV), and subjective stress in EM residents during clinical shifts in the emergency department. HR and HRV were measured via 3-lead Holter monitors and compared to baseline data obtained during weekly educational didactics. Subjective stress was assessed before and after clinical shifts via a Likert-scale questionnaire and written comments. We enrolled 21 residents and acquired data from 40 shifts. Residents experienced an increase in mean HR of eight beats per minute (P < 0.001) and decrease in HRV of 53.9 milliseconds (P = 0.005) while working clinically. Subjective stress increased during clinical work (P <0.001). HRV was negatively correlated with subjective stress, but this did not reach statistical significance (P = 0.09). EM residents experience acute subjective and physiologic stress while working clinically. HR, HRV, and self-reported stress are feasible indicators to assess the acute stress response during residency training. These findings should be studied in a larger, more diverse cohort of residents and efforts made to identify characteristics that contribute to acute stress and to elicit targeted educational interventions to mitigate the acute stress response.

HEART score is widely used to stratify patients with chest pain in the emergency department but has never been validated for cocaine-associated chest pain (CACP). We sought to evaluate the performance of HEART score in risk stratifying patients with CACP compared to an age- and sex-matched cohort with non-CACP. The parent study was an observational cohort study that enrolled consecutive patients with chest pain. We identified patients with CACP and age/sex matched them to patients with non-CACP in 1:2 fashion. HEART score was calculated retrospectively from charts. The primary outcome was major adverse cardiac events (MACE) within 30 days of indexed encounter. We included 156 patients with CACP and 312 age-and sex-matched patients with non-CACP (n = 468, mean age 51 ± 9, 22% females). There was no difference in rate of MACE between the groups (17.9% vs. 15.7%, p = 0.54). Compared to the non-CACP group, the HEART score had lower classification performance in those with CACP (AUC = 0.68 [0.56–0.80] vs. 0.84 [0.78–0.90], p = 0.022). In CACP group, Troponin score had the highest discriminatory value (AUC = 0.72 [0.60–0.85]) and Risk factors score had the lowest (AUC = 0.47 [0.34–0.59]). In patients deemed low-risk by the HEART score, those with CACP were more likely to experience MACE (14% vs. 4%, OR = 3.7 [1.3–10.7], p = 0.016). In patients with CACP, HEART score performs poorly in stratifying risk and is not recommended as a rule out tool to identify those at low risk of MACE.

Complex medical terminology utilized in clinical documentation can present barriers to patients understanding their medical findings. We aimed to generate easy-to-understand summaries of clinical radiology reports using large language models (LLMs) and evaluate their safety and quality. Eight board-certified physician reviewers evaluated 1982 LLM-generated radiology report summaries (computed tomography, magnetic resonance imaging, ultrasound, and x-ray) for safety and quality, using predefined rating criteria and the corresponding original radiology reports for reference. Physician reviewers determined 99.2% (1967 out of 1982) of the LLM-generated summaries to be safe. The reviewers scored the quality of the LLM-generated summaries from “5—Very Good” to “1—Very Poor,” respectively, as follows: 80.6%, 11.1%, 5.7%, 1.7%, and 0.9%. Safety varied significantly across imaging modality (P = .002). Large language models can be used to generate safe and high-quality summaries of clinical radiology reports. Further investigation is warranted to determine the impact of LLM-generated summaries on patient perception of understanding, knowledge of their medical conditions, and overall experience.

Background: In response to the 2011 Future of Nursing report, the Robert Wood Johnson Foundation created the Future of Nursing Scholars (FNS) Program in partnership with select schools of nursing to increase the number of PhD-prepared nurses using a 3-year curriculum. Method: A group of scholars and FNS administrative leaders reflect on lessons learned for stakeholders planning to pursue a 3-year PhD model using personal experiences and extant literature. Results: Several factors should be considered prior to engaging in a 3-year PhD timeline, including mentorship, data collection approaches, methodological choices, and the need to balance multiple personal and professional loyalties. Considerations, strategies, and recommendations are provided for schools of nursing, faculty, mentors, and students. Conclusion: The recommendations provided add to a growing body of knowledge that will create a foundation for understanding what factors constitute “success” for both PhD programs and students. [J Nurs Educ. 2022;61(1):19–28.]

ObjectivesChest pain is among the leading causes for emergency medical services (EMS) activation. Acute myocardial infarction (MI) is not only one of the most critical aetiologies of chest pain, but also one of few conditions encountered by EMS that has been shown to follow a circadian pattern. Understanding the diurnal relationship between the inflow of chest pain patients and the likelihood of acute MI may inform prehospital and emergency department (ED) healthcare providers regarding the prediction, and hence prevention, of dire outcomes.MethodsThis was a secondary analysis of previously collected data from an observational prospective study that enrolled consecutive chest pain patients transported by a large metropolitan EMS system in the USA. We used the time of EMS call to determine the time-of-day of the indexed encounter. Two independent reviewers examined available medical data to determine our primary outcome, the presence of MI, and our secondary outcomes, infarct size and 30-day major adverse cardiac events (MACE). We estimated infarct size using peak troponin level.ResultsWe enrolled 2065 patients (age 56±17, 53% males, 7.5% with MI). Chest pain encounters increased from 9:00 AM to 2:00 PM, with a peak at 1:00 PM and a nadir at 6:00 AM. Acute MI had a bimodal distribution with two peaks: 10 AM in ST-elevation MI, and 10 PM in non-ST-elevation MI. ST-elevation MI with afternoon onset was an independent predictor of infarct size. Acute MI with winter and early spring presentation was an independent predictor of 30-day MACE.ConclusionsEMS-attended chest pain calls follow a diurnal pattern, with the most vulnerable patients encountered during afternoons and winter/spring seasons. These data can inform prehospital and ED healthcare providers regarding the time of presentation where patients are more likely to have an underlying MI and subsequently worse outcomes.

Appropriate prehospital (PH) triage of patients with chest pain can significantly improve outcomes in acute myocardial infarction (MI). We sought to explore how PH providers triage chest pain as high versus low risk and to evaluate the accuracy and predictors of their triage decision. This was a prospective, observational cohort study that enrolled consecutive patients with chest pain transported by emergency medical services (EMS) to 3 tertiary care hospitals in the US. EMS triage decision (high risk versus low-risk) was defined based on the transmission of PH electrocardiogram (ECG) to a command center for medical consultation with or without catheter laboratory activation. Two independent reviewers examined in-hospital medical records to adjudicate the presence of acute MI and to audit the findings on the presenting ECG. We enrolled 2,065 patients (aged 56 ± 17, 53% male) of whom 768 (37%) were triaged as high risk. Those triaged as high risk were older, were more likely to be men or have significant cardiac history, and had a higher rate of acute MI events (14.2% versus 3.5%). The sensitivity and specificity for triaging MI events as high risk were 70% and 97%, respectively. A total of 46/155 (30%) MI events were misclassified as low risk. No previous coronary revascularization and ECG misinterpretation were strong independent predictors of such undertriage. PH providers have moderate sensitivity in triaging high-risk patients; 1 in 3 MI events are undertriaged. Emergency nurses need to pay special attention to patients with benign past histories during transition of care and should always reinterpret ECGs for subtle ischemic changes.

Many patients transported by emergency medical services (EMS) may require advanced cardiac care but do not have ST-segment elevation (STEMI) on the initial prehospital EKG. We sought to identify factors associated with the need for advanced cardiac care in undifferentiated EMS patients reporting chest pain in the absence of STEMI on EKG. We performed a retrospective analysis of all adult patients, reporting atraumatic chest pain from a single EMS agency, presenting to a single, urban hospital over a 10-year period. Patients with STEMI on prehospital electrocardiogram were excluded. Patient demographics, chest pain characteristics and prehospital factors were abstracted for all patients. We identified those patients that required advanced cardiac care and performed regression analysis to determine associated factors. A total of 956 charts were analyzed. Of this total, 193 patients (20.2%) met the primary composite outcome. Of the outcome group, 185 patients (95.9%) had coronary artery disease documented on cardiac catheterization, 22 patients (11.4%) underwent CABG, and seven patients (3.6%) died in the hospital. Most significant variables (multivariable IRR) included age (1.02), male gender (1.65), history of MI (1.47), PCI (1.66), hyperlipidemia (1.40), diaphoresis (1.51), home aspirin (1.53), and improvement with EMS treatment (1.60). We have identified several factors that could be considered when risk stratifying prehospital patients reporting chest pain. While potentially predictive, the factors are broad and support the need for other objective factors that could augment prediction of patients who may benefit from early advanced cardiac care.

The purpose of this study was to obtain a broad view of the knowledge, attitudes, beliefs, and lived experiences of emergency nurses regarding implicit and explicit bias. An exploratory, descriptive, sequential mixed-methods approach using online surveys and focus groups to generate study data. Two validated instruments were incorporated into the survey to evaluate experiences of microaggression in the workplace and ethnocultural empathy. Focus group data were collected using Zoom meetings. The final sample comprised 1140 participants in the survey arm and 23 focus group participants. Significant differences were found in reported experiences of institutional, structural, and personal microaggressions for non-white vs white participants. Respondents who identified Christianity as their religious group had lower mean scores on items representing empathetic awareness. Respondents who identified as nonheterosexual had significantly higher mean total Scale of Ethnocultural Empathy scores, empathetic awareness subscale scores, and empathetic feeling and expression subscale scores. Thematic categories that arose from the focus group data included witnessed bias, experienced bias, responses to bias, impact of bias on care, and solutions. In both our survey and focus group data, we see evidence that racism and other forms of bias are threats to safe patient care. We challenge all emergency nurses and institutions to reflect on the implicit and explicit biases they hold and to engage in purposeful learning about the effects of individual and structural bias on patients and colleagues. We suggest an approach that favors structural analysis, intervention, and accountability.