Patient-Reported Symptoms of Acute Coronary Syndrome in the Prehospital Period in a Prospective Study: Implications for Emergency Nurse Triage, Diagnosis, and Clinical Outcomes.

Patient-reported symptoms of acute coronary syndrome in the prehospital period in a prospective study: Implications for emergency nurse triage, diagnosis, and clinical outcomes - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Emerg Nurs . Author manuscript; available in PMC: 2026 Jun 2. Published in final edited form as: J Emerg Nurs. 2025 Jun 2;51(6):1070–1083. doi: 10.1016/j.jen.2025.04.016 Patient-reported symptoms of acute coronary syndrome in the prehospital period in a prospective study: Implications for emergency nurse triage, diagnosis, and clinical outcomes Jessica K Zègre-Hemsey a, * , Eugenia Wong b , Jamie Crandell a , Wayne Rosamond b , Kevin Chronowski c , Kyle Ronn d , Jane H Brice c, e , Joseph Grover c, d, e , Victoria Vaughan Dickson f , Debra K Moser g , Holli A DeVon h Author information Article notes Copyright and License information a University of North Carolina at Chapel Hill, School of Nursing b Gillings Global School of Public Health, University of North Carolina at Chapel Hill c Department of Emergency Medicine, University of North Carolina at Chapel Hill d Orange County Emergency Services e School of Medicine, University of North Carolina at Chapel Hill f School of Nursing, University of Connecticut g School of Nursing, University of Tennessee, Knoxville h School of Nursing, University of California Los Angeles Jessica K. Zègre-Hemsey PhD, RN, FAHA, Associate Professor, University of North Carolina at Chapel Hill, School of Nursing and Adjunct Associate Professor, University of North Carolina at Chapel Hill, School of Medicine, Department of Emergency Medicine. Eugenia Wong PhD, University of North Carolina at Chapel Hill, School of Public Health, Cardiovascular Epidemiology. Jamie Crandell PhD, University of North Carolina at Chapel Hill, School of Nursing. Wayne Rosamond PhD, University of North Carolina at Chapel Hill, School of Public Health, Cardiovascular Epidemiology. Kevin Chronowski, University of North Carolina at Chapel Hill, School of Medicine, Department of Emergency Medicine. Kyle Ronn NRP, Orange County Emergency Services. Jane H. Brice MD, MPH, University of North Carolina at Chapel Hill, Department of Emergency Medicine. Joseph Grover MD, University of North Carolina at Chapel Hill, Department of Emergency Medicine, Orange County Emergency Services. Victoria Vaughan Dickson PhD, RN, University of Connecticut, School of Nursing. Debra K. Moser PhD, RN. University of Tennessee, Knoxville, School of Nursing. Holli A. DeVon PhD, RN. University of California at Los Angeles. * Corresponding Author: Jessica K. Zègre-Hemsey, PhD, RN, jzhemsey@email.unc.edu , University of North Carolina at Chapel Hill, School of Nursing, Chapel Hill, North Carolina, 27599-7460, Home address: 504 N Boundary Street, Chapel Hill NC 27514 Issue date 2025 Nov. PMC Copyright notice PMCID: PMC12353431  NIHMSID: NIHMS2080182  PMID: 40455184 The publisher's version of this article is available at J Emerg Nurs Abstract Introduction: Improving the prompt recognition of acute coronary syndrome (ACS) symptoms in the prehospital period may reduce total ischemic time and improve patient outcomes. The purpose of this study was to evaluate the occurrence, severity, and changes in patient-reported symptoms between the prehospital period and emergency department and determine if symptoms predicted an ACS diagnosis and/or adverse patient outcomes (eg, death). Methods: Individuals ≥21 years old, transported by emergency medical services (EMS) with non-traumatic chest pain or anginal equivalent symptoms were eligible. Patients completed the ACS Symptom Checklist in the ambulance and on arrival to the emergency department. Chi-square, t-tests, and logistic regression were used, adjusting for age, sex and race, and estimate associations between symptoms and ACS diagnoses and adverse events within 30 days. Results: The sample included 206 individuals. Chest pain was the chief complaint at T1 and T2. Participants reporting chest pressure, shoulder pain, palpitations, light-headedness, and chest pain were significantly younger than participants without these symptoms. Sweating at T1 was associated with increased odds of an ACS diagnosis (OR = 3.24, P = .01). At T2, chest discomfort and unusual fatigue were predictive of ACS diagnosis (OR = 2.59, 2.98; P<.045, P<0.03, respectively). Patients experiencing shortness of breath (SOB) at either T1 or T2 had significantly increased odds of adverse events (OR 3.96 and 3.26, respectively; P = 0.02, 0.04). Six symptoms decreased by emergency department arrival. Discussion: Chest symptoms, sweating, fatigue, and SOB should trigger concern for ACS by clinicians. Results indicate the importance of calling EMS which was associated with a prehospital reduction in symptoms. Keywords: Acute coronary syndrome, Emergency nursing, Emergency medical services, Symptoms, Triage Contribution to Emergency Nursing Practice Patients presenting with suspected ACS present a diagnostic challenge in emergency cardiac care. Using a prospective approach, this paper improves our understanding of ischemic symptoms in the very early period of evolving ACS, before patients receive symptom altering treatment (e.g., aspirin) in the ambulance or ED. Chest symptoms and several non-chest symptoms, including sweating and fatigue, were predictive of an ACS in the prehospital and ED periods. Emergency nurses’ understanding of patient-reported symptoms in the prehospital period and ED may improve early triage and risk stratification for myocardium sparing cardiac care. Introduction Cardiovascular disease is the leading cause of mortality globally, and acute coronary syndrome (ACS) accounts for approximately half of all such cardiovascular deaths. 1 , 2 Reduction of total ischemic time (symptom onset to reperfusion) significantly improves survival in instances of acute coronary occlusions. 3 For every 30 minute delay in time to treatment patients experience a 7.5% increase in 1-year mortality risk. 4 Early identification and recognition of ACS symptoms are important because they trigger an individual to seek care and augment clinicians’ triage and treatment decisions. 3 Patients with suspected ACS present with an array of diverse symptoms including chest pain or discomfort, shortness of breath, cold sweats, nausea, and lightheadedness. 5 , 6 ACS symptoms vary by age and sex , 7 – 9 and females are more likely to present with non-chest symptoms (e.g., shoulder pain, arm pain) compared to males. 7 , 10 Many ACS patients do not present with chest pain symptoms, 7 , 11 and have longer ischemic times and worse outcomes. 12 A current focus on cardiac symptoms in emergency cardiac care is to minimize total ischemic time, yet it is often difficult to distinguish between cardiac and noncardiac conditions based on patient-reported symptoms. 11 Patient-reported symptoms and characteristics have not been well described in the prehospital setting. Further research is needed to improve our understanding of ischemic symptoms in the very early period of evolving ACS, before patients receive symptom altering treatment (e.g., aspirin) in the ambulance or ED. The primary objectives of this study were to evaluate the occurrence, severity, and changes in patient-reported symptoms between the prehospital period (i.e., 9-1-1 call to emergency department [ED] arrival) (T1) and ED arrival (T2), differences in symptoms by patient characteristics, and if symptoms predicted ACS and adverse outcomes. We hypothesized that patient-reported symptoms would be dynamic between T1 and T2 and would differ based on patient age and sex. Based on prior research, 13 , 14 we further hypothesized that patients reporting non-chest symptoms (e.g., sweating) would more likely experience adverse outcomes compared to those who report chest symptoms (e.g., chest pressure, chest discomfort, chest pain). Methods Design We used a prospective cohort study design. Institutional review board (IRB) approval was obtained for all study procedures. Sample and Setting Individuals who activated 9-1-1 with suspected ACS (i.e., patients presenting with undifferentiated chest pain and/or anginal equivalent symptoms) 15 and transported to the ED by ambulance. We enrolled patients between November 2019 and July 2022 in collaboration with emergency medical service (EMS) clinician partners. Orange County Emergency Services (OCES) is the primary 9-1-1 EMS agency serving the community, which has both rural and urban areas. Approximately 25% of the population is Black and there is a growing Hispanic population. Recruitment occurred 7 days a week and 24 hours per day. Inclusion criteria were individuals ≥ 21 years; complaints of non-traumatic chest pain or anginal equivalent symptoms (e.g. shortness of breath); and English speaking. Exclusion criteria were being transferred from another facility; exacerbation of pre-existing heart failure; non-English speaking; or cognitive impairment determined by difficulty in understanding study information or giving verbal assent. Consent A staged consent strategy was used for emergency care research, as recommended by the National Institutes of Health (NIH) Office of Emergency Care and Research and Department of Bioethics. 16 Staged involvement consent includes all required regulatory elements, but prioritizes the presentation of information related to the most immediate decision first, thus recognizing consent as an ongoing process while allowing patients to focus on 1 decision at a time. 16 In our study, EMS provided a brief study description during emergency transport (T1), because obtaining written consent in the field would likely cause delays that could be harmful to the patient. 16 EMS clinicians followed routine chest pain protocols, and no usual care was altered. EMS clinicians (i.e., paramedics, emergency medical technicians) all received study orientation at monthly continuing education meetings and ongoing support during study duration. Trained research specialists approached patients for written consent upon ED arrival (T2). Measures Symptoms were measured with the validated 13-item ACS Symptom Checklist. 7 , 17 Trained EMS clinicians first administered the ACS Symptom Checklist to patients in the ambulance (T1) and trained research staff administered it a second time (T2) on arrival to ED, once patients provided written consent. The ACS Symptom Checklist is a 13-item validated instrument that measures symptoms of ACS; it takes 1 minute to complete, on average. 15 , 17 Participants indicate whether each symptom is present or absent, and each symptom is analyzed individually. 15 Patients select 1 chief complaint and rate severity on a scale from 1 to 10; there is no summary score. During hospitalization, trained research specialists abstracted clinical data per key data elements 18 including final diagnoses, cardiac catheterization laboratory activations, and adverse events up for 30-day follow-up from index hospitalization, from electronic health records. Adverse events included death, new onset or worsening heart failure event, reinfarction (after hospital admission), and/or other per clinical data standards. 18 Trained research specialists also conducted 30 day follow-up telephone calls, as needed, for participants discharged directly from the ED. The Research Electronic Data Capture system (REDCap, Nashville, TN) was used for data collection and management. 19 , 20 Based on EMS feedback, the 13-item ACS Symptom Checklist was reformatted into a public access REDCap survey that was accessed with a single-click or QR code ( Figure 1 ). The survey link did not require a REDCap account or login, which streamlined the data collection process in the fast-paced prehospital setting. The completion of each 13-item ACS Symptom Checklist triggered a unique study I.D. record in the REDCap database. REDCap autogenerated an email notification of a new potential study participant for trained research staff in the ED. Figure 1. Open in a new tab Study protocol Data Analysis Data were cleaned via examination for out-of-range values, which were corrected via EHR review. Descriptive statistics were used to analyze sample demographic and clinical characteristics, including symptom prevalence at T1 and T2 and outcomes. We used chi-square tests and 2-sample t-tests to examine whether symptom prevalence varied by age, sex, or race. Clinical outcomes were ACS diagnosis (STEMI, NSTEMI, or unstable angina) and adverse events within 30 days (death, heart failure event [HF], or MI). We used chi-square and Fisher’s exact test to examine the bivariate associations between symptoms and ACS diagnosis. We used logistic regression to control this comparison for age, sex, and race, estimating adjusted odds ratios (OR) for each symptom at each timepoint. As an exploratory analysis, we used logistic regression to examine which symptoms, controlling for age, sex and race, were associated with adverse evens within 30 days. Data were analyzed using SAS (Cary, NC) statistical software. Significance was set at p<0.05 for all statistical tests. Statistical Power The parent study (target N = 300) had >80% power to detect associations between symptoms and the presence/absence of an ACS outcome equal to an OR of 2.15 or higher. With a final sample size of N = 206, we had 80% power to detect associations between symptoms and the presence/absence of an ACS outcome equal to an OR of 2.45 or higher. Results A total of 442 individuals were transported by ambulance and completed the ACS Symptom Checklist (T1). Of these, 206 patients fully consented to study participation and were included ( Figure 2 ). The final sample was 54% female, 46% male, 71.8% White, 1.5% Hispanic/Latino ethnicity, and had a mean age of 63.1(SD = 14.7, range = 25–97) years. A final diagnosis of ACS was observed in 29 participants (14.1%) and over half were admitted to the hospital from the ED. At 30-day follow-up, 15(7.3%) patients experienced a heart failure event, 1 (0.5%) had myocardial infarction after hospital admission, and 1 (0.5%) died ( Table 1 ). Figure 2. Open in a new tab Enrollment flowchart of patients transported by EMS with suspected ACS. *Patients not enrolled because they did not provide written consent. Table 1. Sample characteristics among patients transported by ambulance (n = 206). Characteristic Mean (SD, range) SD, range n % Age in years 63.1 14.7, 25–97 Female 112 54 Male 94 46 Race American Indian/Alaska Native 0 0 Asian 3 1.5 Black and African American 51 27.8 White 148 71.8 Other 4 1.9 Ethnicity Hispanic/Latino 3 1.5 Not Hispanic/Latino 200 97.1 Unknown 3 1.5 Elevated initial troponin 13 6.4 Elevated peak troponin 26 12.9 Catheterization performed 23 11.1 PCI occurrence 13 6.3 Primary hospital diagnosis Any ACS 29 14.1 STEMI 6 2.9 NSTEMI 18 8.7 Unstable angina 5 2.4 Patient disposition Admitted 109 52.9 Discharged 94 45.6 Other 3 1.5 Outcomes within 30 days Any of the 3 adverse outcomes below 17 8.3 Death 1 0.5 HF event 15 7.3 MI after hospital admission 1 0.5 Open in a new tab ACS=acute coronary syndrome, STEMI=ST-elevation myocardial infarction, NSTEMI=non-ST-elevation myocardial infarction, HF=heart failure Symptom Prevalence at T1 and T2 We examined the prevalence of patient-reported symptoms at 2 intervals, ambulance (T1) and ED arrival (T2). At T1, chest pain (54.9%) was the most common reported symptom, followed by chest pressure (53.4%) and chest discomfort (53.4%). At T2, chest pressure (46.6%) was the most common reported symptom followed by chest discomfort (45.4%) and unusual fatigue (45.4%). Of the 13 symptoms on the ACS Symptom Checklist, 9 changed significantly between T1 and T2 ( Table 2 ). Patient-reported symptoms of chest pressure, sweating, palpitations, chest discomfort, shortness of breath, nausea, chest pain and indigestion decreased significantly between T1 and T2. Unusual fatigue was the only symptom that increased significantly from T1 to T2. Among the 13 symptoms on the ACS Symptom Checklist, chest pain was the most common chief complaint at either T1 or T2, followed by chest pressure All patients (n 206) at T1 and most patients at T2 (n = 152, 87.4%) identified a chief complaint. Severity of the chief complaint decreased on average from 6.6 to 5.6 out of 10 (P < 0.0001) between T1 and T2. There was an overall decrease in the number of symptoms from the prehospital to ED period with a concurrent decrease in the severity of chief complaint. Table 2. Prevalence of 13 symptoms from the ACS Symptom Checklist and severity of chief complaint between baseline ambulance (T1) and emergency department (T2). Symptom T1 (n = 206) T2 (n = 174 * ) P-value n % Mean SD n % Mean SD Chest pressure 110 53.4 81 46.6 0.03 Shoulder pain 38 18.5 43 24.7 0.14 Sweating 55 26.7 17 9.8 <.0001 Palpitations 66 32 41 23.6 0.02 Chest discomfort 110 53.4 79 45.4 0.009 Upper back pain 39 18.9 38 21.8 0.72 Short of breath 96 46.6 65 37.4 0.004 Arm pain 37 18 43 24.7 0.27 Unusual fatigue 62 30.1 79 45.4 0.002 Nausea 69 33.5 46 26.4 0.047 Lightheaded 80 38.8 66 37.9 0.29 Chest pain 113 54.9 65 37.4 <.0001 Indigestion 39 18.9 22 12.6 0.03 Severity of chief complaint (1–10) at both T1 and T2 6.60 2.51 5.59 2.71 P<.0001 Open in a new tab * n = 32 not included in T2 because of retrospective T2 data collection (n=18) and because T2 never collected (n = 14). Note: P-values from McNemar’s test for symptoms (n = 174); p-value for matched pairs t-test for severity (n = 152) Symptoms and Outcomes by Age, Sex, and Race The prevalence of individual symptoms was examined by age, sex and race in patients transported by EMS. At T1 and T2, patients reporting chest pressure, shoulder pain, palpitations, light-headedness, and chest pain were significantly younger than those participants who did not report those symptoms ( Table 3 ). At T1, patients reporting sweating, shortness of breath, and nausea were significantly younger compared to those not reporting these symptoms. At T2, patients reporting chest discomfort and unusual fatigue were also younger compared to those not reporting these symptoms. At T1, males reported significantly more sweating and arm pain compared to females ( Table 4 ). Compared to males, females reported significantly more palpitations and unusual fatigue. There were no significant sex differences in symptoms at T2. With regards to race, Black patients were significantly more likely to report shoulder pain compared to White patients at both T1 (33.3% versus 12.8%, P = 0.001) and T2 (35.7% versus 20%, P = 0.04). At T2 only, palpitations and chest pain were significantly more common in Blacks than Whites. Table 3: Age differences in study variables, 2-sample t-tests. Mean age and SD with characteristic (symptoms are T1) Mean age and SD without characteristic (symptoms are T1) p-value, T1 Mean age and AD of those with symptom, T2 Mean age and SD of those without symptom, T2 P-value, T2 Chest pressure 60.6 15 65.9 14 0.008 60.6 15 65.1 14.3 0.046 Shoulder pain 57.7 14.8 64.3 14.5 0.02 58.7 15.1 64.4 14.4 0.03 Sweating 60 13.4 64.2 15.1 0.06 62.5 11.1 63.1 15.1 0.86 palpitations 59 14 65 14.7 0.006 57 11.4 64.9 15.2 0.001 Chest discomfort 61.7 15.4 64.7 13.9 0.14 59 15.2 66.3 13.6 0.001 Upper back pain 60.1 15.1 63.8 14.6 0.18 61 14.8 63.6 14.8 0.35 Short of breath 60.3 14 65.5 15 0.01 62.3 11.2 63.4 16.6 0.59 Arm pain 60.7 14.6 63.6 14.8 0.29 60.9 14.5 63.7 14.8 0.28 Unusual fatigue 62.3 16.4 63.4 14 0.66 60.4 14.9 65.1 14.4 0.04 Nausea 59.7 13.6 64.8 15 0.02 60.1 14.2 64.1 14.9 0.11 Lightheaded 59.4 15 65.4 14.2 0.005 58.8 14.2 65.6 14.6 0.003 Chest pain 61 14.1 65.5 15.2 0.03 58.5 14.7 65.7 14.2 0.002 Indigestion 62.7 15.2 63.2 14.7 0.86 64.1 14.3 62.9 14.9 0.71 Elevated initial troponin 66 9.8 63 15 0.32 Elevated peak troponin 68.3 12.3 62.4 15 0.035 Catheterization performed 64.0 11.2 63.0 15.2 0.74 PCI occurrence 63.7 8.5 63.0 15.1 0.80 Adverse event 68.9 13.1 62.5 14.8 0.073 Death 59 63.1 14.8 HF event 68.7 13.3 62.6 (14.8) 0.108 MI after hospital admission 81 63 14.7 Any ACS diagnosis 68.7 12.2 62.1 14.9 0.013 STEMI 66.3 6.2 63 14.9 0.259 NSTEMI 69.3 13.8 62.5 14.7 0.059 Unstable Angina 69.2 12.9 62.9 14.8 0.342 Open in a new tab PCI = percutaneous coronary intervention, HF = heart failure, STEMI = ST-elevation myocardial infarction, NSTEMI = non-ST-elevation myocardial infarction Table 4. Sex differences in symptoms, adverse events, and final hospital diagnoses. n(%) of females with symptom/condition, T1 (n = 112) n(%) of males with symptom/condition, T1 (N = 94) P-value, T1 n(%) of females with symptom, T2 (N = 90) * n(%) of males with symptom, T2 (N = 84) * P-value, T2 n % n % n % Chest pressure 62 55.36 48 51.06 0.54 41 45.56 40 47.62 0.79 Shoulder pain 23 20.54 15 15.96 0.4 21 23.33 22 26.19 0.66 Sweating 23 20.54 32 34.04 0.03 7 7.78 10 11.9 0.36 Palpitations 43 38.39 23 24.47 0.03 22 24.44 19 22.62 0.78 Chest discomfort 57 50.89 53 56.38 0.43 41 45.56 38 45.24 0.97 Upper back pain 24 21.43 15 15.96 0.32 25 27.78 13 15.48 0.05 Short of breath 56 50 40 42.55 0.29 37 41.11 28 33.33 0.29 Arm pain 11 9.82 26 27.66 0.001 20 22.22 23 27.38 0.43 Unusual fatigue 41 36.61 21 22.34 0.03 45 50 34 40.48 0.21 Nausea 41 36.61 28 29.79 0.3 27 30 19 22.62 0.27 Lightheaded 48 42.86 32 34.04 0.2 37 41.11 29 34.52 0.37 Chest pain 62 55.36 51 54.26 0.87 33 36.67 32 38.1 0.85 Indigestion 20 17.86 19 20.21 0.67 12 13.33 10 11.9 0.78 Elevated Initial troponin 6 5.45 7 7.45 0.56 Elevated peak troponin 13 12.04 13 13.98 0.68 Catheterizati on performed 6 5.4 17 18.1 0.004 PCI occurrence 2 1.8 11 11.7 0.007 Adverse event 8 7.14 9 9.57 0.53 Death 0 0 1 1.06 0.27 HF event 8 7.14 7 7.45 0.93 MI after hospital admission 0 0 1 1.06 0.46 Any ACS diagnosis 6 5.36 23 24.47 <.0001 STEMI 0 0 6 6.38 0.01 NSTEMI 6 5.36 12 12.77 0.06 Unstable angina 0 0 5 5.32 0.02 Open in a new tab PCI = percutaneous coronary intervention, HF = heart failure, STEMI = ST-elevation myocardial infarction, NSTEMI = non-ST-elevation myocardial infarction. * n = 32 not included in T2 because of retrospective T2 data collection (n = 18) or because T2 never collected (n = 14). Chi-square for all P-values except when cell counts not all 5 or higher (upper back pain at T1, indigestion at T2), in which case Fisher’s exact was used. We also examined how outcomes (ACS diagnosis and adverse event) varied by age, sex and race. Males were significantly more likely to receive a final diagnosis of ACS compared to females (24.5% versus 5.4%, P<0.0001). Specifically, males had significantly more STEMI (6.4% versus 5.4%, P = 0.01) and unstable angina (5.3% versus 0%, P = 0.02). While there was a trend showing that White patients were more likely to receive an ACS diagnosis compared to Black patients, this difference did not reach statistical significance (P = 0.05). Though power was limited due to the small sample sizes for adverse events, we saw no significant differences by age, sex or race. Individual Symptoms Predictive of ACS and Adverse Events Table 5 shows the prevalence of each symptom at each timepoint, in patients with and without an ACS diagnosis. At T1, the prevalence of sweating and arm pain was significantly higher in the ACS group than in the non-ACS group; at T2, those with ACS conditions reported more chest pressure than those without. Table 5: Prevalence of symptoms in those with and without ACS diagnosis. Symptom Prevalence of symptoms at T1 Prevalence of symptoms at T2 Patients without ACS conditions (n = 177) Patients with ACS conditions (n = 2 9) P-value Patients without ACS conditions (n=148) * Patients with ACS conditions (n = 26) * P-value n % n % n % n % Chest pressure 94 53.1 16 55.2 0.84 64 43.2 17 65.4 0.04 Shoulder pain 32 18.1 6 20.7 0.74 35 23.7 8 30.8 0.44 Sweating 41 23.2 14 48.3 0.0046 12 8.1 5 19.2 0.08 Palpitations 58 32.8 8 27.6 0.58 36 24.3 5 19.2 0.57 Chest discomfort 90 50.9 20 69.0 0.07 64 43.2 15 57.7 0.17 Upper back pain 36 20.3 3 10.3 0.31 33 22.3 5 19.2 0.73 Short of breath 82 46.3 14 48.3 0.85 54 36.5 11 42.3 0.57 Arm pain 28 15.8 9 31.0 0.048 34 23.0 9 34.6 0.20 Unusual fatigue 55 31.1 7 24.1 0.45 63 42.6 16 61.5 0.07 Nausea 62 35.0 7 24.1 0.25 41 27.7 5 19.2 0.37 Lightheaded 73 41.2 7 24.1 0.08 56 37.8 10 38.5 0.95 Chest pain 95 53.7 18 62.1 0.40 55 37.2 10 38.5 0.90 Indigestion 34 19.2 5 17.2 0.80 19 12.8 3 11.5 >0.99 Open in a new tab * n = 32 not included in T2 because of retrospective T2 data collection (n = 18) or because T2 never collected (n = 14). Chi-square for all P-values except when cell counts not all 5 or higher (upper back pain at T1, indigestion at T2), in which case Fisher’s exact was used. Table 6 illustrates odd ratios predicting ACS diagnosis and adverse events from symptoms, after controlling for age, sex, and race. At T1, patients with sweating had 3.24 times the adjusted odds of an ACS diagnosis (P = 0.01) compared to those who did not report sweating. Similarly, at T2, patients with chest pressure had 2.92 times the odds of an ACS diagnosis, those with chest discomfort had 2.59 times the odds of an ACS diagnosis (P<0.05), and those with unusual fatigue were nearly 3 times as likely to have an ACS diagnosis (P = 0.03), compared to patients not reporting these symptoms on ED arrival. As further exploratory analysis, we examined the adjusted ORs comparing symptoms in those with and without an adverse event. Only 17 patients experienced adverse events, so statistical power is low. Patients experiencing shortness of breath at T1 or T2 regardless of an ACS diagnosis had significantly increased odds of adverse events (OR 3.96 and 3.26, respectively). Light-headedness at T1 was associated with reduced odds of a composite adverse event (OR = 0.20). Table 6: Odds ratios predicting ACS diagnosis and adverse events from symptoms, after controlling for age, sex, and race. Symptom Associations between symptoms and ACS diagnosis Associations between symptoms and adverse events Adjusted OR at T1 P-value at T1 Adjusted OR at T2 P-value at T2 Adjusted OR at T1 P-value at T1 Adjusted OR at T2 P-value at T2 Chest pressure 1.24 0.61 2.92 0.03 0.37 0.07 0.61 0.4 Shoulder pain 1.92 0.25 1.92 0.21 1.27 0.73 2.11 0.22 Sweating 3.24 0.01* 3.03 0.08 0.87 0.83 0.65 0.69 Palpitations 1.31 0.59 1.06 0.92 0.29 0.12 - - Chest iscomfort 2.08 0.11 2.59 0.047* 0.74 0.57 0.71 0.56 Upper back pain 0.56 0.39 1.2 0.75 0.28 0.22 0.61 0.53 Shortness of breath 1.48 0.37 1.68 0.27 3.96 0.02* 3.26 0.04* Arm pain 1.94 0.18 2.19 0.12 1.55 0.49 1.34 0.64 Unusual fatigue 0.90 0.84 2.98 0.03* 0.98 0.96 1.1 0.87 Nausea 0.75 0.56 0.79 0.68 0.27 0.09 0.73 0.64 Lightheaded 0.55 0.22 1.37 0.52 0.20 0.04* 0.61 0.43 Chest pain 1.86 0.17 1.35 0.53 0.84 0.73 1.5 0.48 Indigestion 0.76 0.63 1.16 0.84 0.88 0.84 0.51 0.54 Open in a new tab Discussion The primary objective of this study was to determine if patient-reported symptoms in the prehospital period (9-1-1 call to ED arrival) predicted an ACS diagnosis and/or adverse patient outcomes. To our knowledge, we are the first to use the validated ACS Symptom Checklist to collect patient-reported symptoms, as they were occurring, during the prehospital period. We prospectively collected data during a highly vulnerable time when individuals are evaluated for ACS. As expected, our key findings revealed that chest symptoms, including chest pain and chest pressure, were the most frequently reported symptoms during both the initial assessment (T1) and follow-up (T2) periods. Notably, chest pain emerged as the most common chief complaint at both time points. This may partially be explained by the fact that these patients or their surrogates called 9-1-1. Patients reporting diaphoresis in the prehospital period had over 3 times the odds of an ACS diagnoses. Patients with ACS reported sweating and arm pain at T1 significantly more than those without ACS; at T2, patients with ACS reported more chest pressure compared to non-ACS patients. These findings align with the classic symptoms of acute coronary syndrome (ACS) as recognized by leading professional societies including the American Heart Association (AHA) and the American College of Cardiology (ACC). These findings are consistent with prior work by DeVon et al. (2014) who evaluated patients presenting to the ED with suspicion for ACS. 15 Among 736 patients admitted to 5 different EDs and assessed with the ACS Symptom Checklist, chest pain was the most prevalent symptom reported in the ED setting. Like DeVon et al., we assessed symptoms at ED arrival but unlike DeVon et al, we also measured symptoms in the prehospital period. We found that younger individuals experienced more chest pain/pressure whereas older individuals presented with more sweating, shortness of breath, and nausea. It has been previously reported that older adults are more likely than younger ones to manifest non-chest symptomatology including shortness of breath, syncope, or sudden confusion. 21 In a study by Grosmaitre et al, of >3000 patients, ≥75 years hospitalized with acute MI; 44% did not report chest pain as a primary symptom. 22 Males reported sweating at T1 and were diagnosed with STEMI significantly more than females, which corroborates prior research. 15 This suggests potential connection between the sympathetic nervous system innervating sweat glands and myocardial pain fibers, which have the same origin in the thoracolumbar region. 22 Sweating in association with typical or atypical angina predicts STEMI. 23 Our findings differ from a recent study by DeBarmore et al., who found no differences in ischemic pain presentation by age, sex, or race among a large cohort of patients with AMI in the ARIC study. 24 Symptom data were collected from medical record data hence data were retrospective. 24 Symptoms were dynamic between T1 and T2 with most symptoms decreasing on ED arrival. Unusual fatigue was the only symptom that significantly increased between T1 and T2 intervals. Fatigue has been previously reported as an important symptom in the context of ACS. In the study by DeVon et al., unusual fatigue was 1 of the most prevalent symptoms that prompted patients to seek care. 17 ACS patients presenting with unusual fatigue, however, experienced significantly longer prehospital delay than those without this symptom. 25 In our study, patients were under the care of EMS and receiving treatment. It might be expected that patients felt less fatigued while resting on a gurney and under treatment, but the fatigue was a manifestation of the psychological and emotional stress of acute illness and transport to the hospital. Chest pain, moreover, may have been alleviated and fatigue unmasked. This underscores the importance of educating individuals about non-chest symptoms such as serious and debilitating fatigue. Prior research demonstrated that patients with non-chest symptoms were significantly less likely to receive a prehospital ECG and therefore disadvantaged for early triage, risk stratification, and intervention. 14 , 26 Among patients with dynamic symptoms between T1 and T2, those with fewer symptom changes had significantly higher rates of adverse outcomes. The dynamic nature observed may be explained by: 1) underlying thrombotic occlusive coronary stenosis in Type 1 acute myocardial ischemia/infarction for those who were ruled-in for ACS, and 2) medical therapy administered in the prehospital period including cardiac medications (e.g., nitrates, morphine) that increase cardiac perfusion and mitigate symptoms. 27 The underlying mechanism of plaque rupture in ACS may further account for these differences in symptom characteristics and warrants further study. Chest pressure, moreover, did not decrease between T1 and T2 (53.4% to 46.6%). We expected pain to be better controlled presumably due to treatment with analgesics or nitrates in the ambulance, but pain may have persisted due to acute myocardial ischemia, anxiety, and psychological stress. 28 , 29 We also discovered that at T2, younger patients reported more unusual fatigue. This may be explained by younger patients with ACS reporting more symptoms that older patients or that the younger patients are more sensitive to pain or stress. 30 Prehospital symptoms predicted a final hospital diagnosis of ACS and were associated with increased adverse events (death, heart failure event or reinfarction). Sweating reported at T1 in the ambulance was associated with over 3 times the odds of a final hospital diagnosis of ACS; whereas chest discomfort and unusual fatigue reported at T2 in the ED were 2.5 and nearly 3 times as likely to have an ACS diagnosis. Shortness of breath at T1 or T2 was significantly associated with adverse outcomes. Anderrson et al (2017) conducted a retrospective study in Sweden that explored dyspnea, nausea and/or vomiting among patients transported by ambulance for suspected ACS. 31 They found approximately 1 in 3 patients had dyspnea and 1 in 4 had nausea and/or vomiting. 31 Patients with nausea and/or vomiting had significantly increased likelihood of a final ACS diagnosis; and those with dyspnea had increased risk of death within 1 year. These data, however, were retrospective from hospital records whereas our data were acquired using the ACS Symptom Checklist tool in a prospective approach. In an older study by Herlitz et al, 32 emergency medical dispatch operators administered a questionnaire, like the ACS Symptom Checklist, about the presence of symptoms. Patients transported by ambulance with severe chest pain had significantly higher diagnoses of acute myocardial infarction compared to those with vague chest pain. Dyspnea, nausea, vertigo, cold sweat or syncope were also associated with a higher infarction rate compared to absence of these symptoms. Non-chest symptoms may serve to confuse patients and clinicians alike since both have been advised appropriately that chest pain is the most common symptom of ACS. Shortness of breath and fatigue symptoms are common regardless of patient sex. 33 Strengths & Limitations Our study had several strengths. Prospective data collection in the prehospital setting is complex due to the time-sensitive nature of patients with suspected ACS and the prioritization of emergency care. We partnered with our large EMS network to collect data from patients in the ambulance and in the ED, providing the spectrum of emergency cardiac care. Conducting prehospital and out-of-hospital research requires tracking patients across multiple phases of care and using multiple sources of data, thus making the logistics and strategies distinct from other types of clinical research. 34 The study also had limitations. We did not collect data about prehospital analgesics (e.g., nitroglycerin, etc.), therefore are unable to fully interpret the causes of symptom changes between T1 and T2. Patients with suspected STEMI or hemodynamically unstable on arrival to the ED were directly transported to the cardiac catheterization laboratory. Trained research personnel approached patients for consent after intervention, thus symptom recall bias needs to be considered. Beginning in March 2020 with the onset of the COVID-19 pandemic, our consent and data collection processes were impacted by restrictions on the number and types of personnel allowed in the ED. We pivoted to a telephone-based approach to screen, consent, and administer the ACS Symptom Checklist to eligible patients who were not able to be approached in person. This approach created additional potential for recall bias because potential participants (n = 43) were contacted by phone after discharge. We had a small number of adverse events thus power was limited to detect differences in ACS outcome and adverse events, though the estimated odds ratios are useful for effect size estimation. In addition, we present many statistical comparisons, and single significant results that do not align with other results or have scientific plausibility should be suspected as possibly spurious. Lastly, this was a single-site study, and our sample was limited to English-speaking participants thus may not be generalizable to other populations. Implications for Emergency Nurses Early detection and diagnosis of ACS are critical because of time-sensitive interventions for myocardium salvaging care. Findings have direct implications for emergency nursing triage and risk stratification. Emergency nurses need to assess both chest and non-chest symptoms when evaluating patients for ACS. Chest pain is a common symptom, though fatigue, shortness of breath, and other non-chest symptoms may be equally predictive of ACS. Emergency nurses should continue to educate patients about the variety of ACS symptoms, emphasizing the importance of seeking timely medical attention (i.e., 9-1-1) to reduce diagnostic delays and ensure timely treatment. Conclusion This study addresses the very early period of evolving acute ischemia/infarction, when an individual first seeks medical contact (ie, 9-1-1). Most individuals reported chest symptoms between T1 and T2 time intervals and several non-chest symptoms, including fatigue, were predictive of ACS. Age and sex are significant factors for emergency nurses to consider when assessing patient’s symptoms for potential ACS; particularly since younger age is associated with a lower risk for ACS. In addition to chest symptoms, SOB should trigger concern for ACS by clinicians. Future research may focus on better understanding of non-chest symptoms and educating patients to recognize and act promptly (e.g., call 911) to reduce prehospital delay. Implementation of the ACS Symptom Checklist tool into emergency care practice warrants further study. Acknowledgement We would like to thank the EMS clinicians and staff at Orange County Emergency Services for their support and patients for their participation in the study. We also thank Alec Tolentino and the emergency department research specialists for their assistance in the study Funding: This work was supported by the National Institutes of Health, National Institute of Nursing Research [Grant K23NR017896, PI Zègre-Hemsey]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. CRediT author statement Jessica K. Zègre-Hemsey:Conceptualization, Methodology, Writing Eugenia Wong: Software, Data Management, Writing Jamie Crandell: Software, Data Analysis, Writing-Reviewing and Editing Kevin Chronowski: Data Management, Writing-Reviewing and Editing Kyle Ronn: Writing-Reviewing and Editing Wayne Rosamond: Conceptualization, Writing-Reviewing and Editing Jane H. Brice: Writing-Reviewing and Editing Joseph Grover: Writing-Reviewing and Editing Victoria Vaughan Dickson: Writing-Reviewing and Editing Debra Moser: Writing-Reviewing and Editing Holli A. DeVon: Conceptualization, Writing-Reviewing and Editing Disclosures: None References 1. Kolansky DM. Acute coronary syndromes: morbidity, mortality, and pharmacoeconomic burden. Am J Manag Care. 2009;15(2 Suppl):S36–41. [ PubMed ] [ Google Scholar ] 2. Tsao CW, Aday AW, Almarzooq ZI, et al. Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association. Circulation. 2023;0(0). 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