Abstract
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.
Keywords: acute coronary syndrome, prehospital delay, healthcare utilization, outcomes
Introduction
A diagnosis of acute coronary syndrome (ACS) is often linked to adverse outcomes following hospital discharge. ACS is frequently associated with impaired functional status,1,2 repeated Emergency Department (ED) visits,3 and frequent healthcare utilization.4 Furthermore, delayed treatment for ACS increases patient morbidity and mortality5 prior to or during hospitalization. Prehospital delay is the largest factor in delayed treatment for ACS6. While population-based studies such as GRACE7 and the Worcester Heart Attack Study8 found median delay times ranging from 2.2–4.6 hours, clinical studies have shown median delay times in presentation to the ED ranging from 1–24.4 hours.9–12 In addition, mortality increases by 7.5% for each 30-minute delay in treatment.13–16 Despite educational and media campaigns, median prehospital delay did not decrease from 2001 and 2011.17 Data from Worcester, MA, indicate that the median time from symptom onset to hospital arrival has not improved.18,
Many patients face high healthcare utilization and persistent debilitating symptoms post ACS. High healthcare utilization following evaluation for ACS may include hospital readmission, ED visits, and outpatient follow-up. According to national Medicare data, the median risk standardized 30-day readmission rate for myocardial infarction (MI) was 15.8%.19 In one study, 20% of ACS patients were re-hospitalized within the first year after ACS, while 14% visited the ED within the first 30 days.20 In another study, nearly 25% of patients with ACS were readmitted to the hospital within two years.21 In addition, patients with multiple comorbidities face higher readmission rates 30-days post hospital discharge compared to patients without multiple comorbidities (21.5% vs 14.5%, respectively), adding to the potential strain on both patients and the healthcare system.22 These hospital readmissions are costly, as rehospitalization alone accounts for over 40% of total healthcare costs in ACS patients at one year following discharge for the index hospitalization.23 Conversely, outpatient follow up is associated with lower rates of repeat MI and death.24 In a Canadian study however, the authors found that one in four patients did not receive any physician follow-up after seeking care in the ED for chest pain.24 Having an established relationship with a provider was the factor most strongly associated with receiving follow-up care.24 Finally, patients with ACS have reported persistent symptoms following hospital discharge despite receiving guideline directed care.17 Fatigue, pain, anxiety, depression, and reduced mobility heavily burden patients and impair their quality of life.17
Although prehospital delay remains a significant barrier in obtaining timely treatment for potential ACS, delayed treatment is associated with poorer outcomes. In addition, frequent healthcare utilization is a significant consequence of ED evaluation for ACS. Finally, most patients presenting to the ED and evaluated for ACS are undiagnosed on arrival and final diagnosed isn’t established until a thorough work-up has been completed.25 Approximately 10–20% of individuals presenting to the ED with cardiac-type symptoms are confirmed to have ACS.25 Symptom differences also do not vary substantially between those who rule-in for ACS and those who rule-out for ACS.26 Hence, the inclusion of both groups in the study of prehospital delay and subsequent healthcare utilization is important.
In summary, studies indicate that delayed treatment is associated with increased morbidity and mortality; prehospital delay is the largest component of delayed treatment; and healthcare utilization is related to indicators/predictors of morbidity and mortality. Thus, characterizing these relationships is valuable because it could inform clinicians in guiding patients in symptom management and when to seek timely care for symptoms. Therefore, we hypothesized that prehospital delay would be more likely to be associated with persistent symptoms and healthcare utilization at 30 days and 6 months post hospital discharge. Specific aims of this study were to determine if: 1) if there was a relationship between prehospital delay and persistent symptoms; and 2) if there was an association between prehospital delay for symptoms suggestive of ACS and subsequent healthcare utilization 30-days and 6-months post hospital discharge.
Methods
Design, Sample, and Setting
This was a secondary analysis of data from a study whose primary aim was to determine if there were sex differences in ACS symptoms. The dataset consisted of 1,064 patients presenting to one of five EDs in the Western, Southwestern, and Midwestern United States (U.S.) with symptoms that triggered a cardiac evaluation for suspected ACS. Patients (n=722) with complete data on all variables were included in this analysis. Patients were eligible to participate if they experienced symptoms that prompted them to go to the ED and received a cardiac evaluation in the ED. Patients whose condition was initially unstable and who went directly to the cardiac catheterization lab were included in the study but were enrolled following stabilization and cardiac interventions. Final diagnosis (ACS versus non-ACS) was determined by the attending physician. The cohort of patients presenting with ACS like symptoms who were ruled-out for ACS served as an important comparison group. Four hundred seventy-four patients had a confirmed diagnosis of ACS, and 590 patients had a non-ACS diagnosis. The dataset included 400 women and 664 men.
Procedure
Institutional Review Board approval was obtained from all clinical sites and the sponsoring institution for the parent study. This study was deemed exempt by the IRB of the sponsoring institution as all data were de-identified. Following informed consent, symptom data were collected in the ED in face-to-face interviews.27 Prehospital delay time was calculated by subtracting the time of symptom onset from the time of registration in the ED. For those patients transferred from another hospital, delay time was calculated from symptom onset to arrival at the first hospital. If the patient could not recall the time of symptom onset, it was abstracted from the medical record. Time was rounded to the nearest quarter hour. Symptoms and healthcare utilization data were collected in phone calls 30-days and 6-months post discharge.
Measures
ACS Symptoms
Symptoms were measured with the ACS Symptom Checklist, a 13-item empirically derived instrument that measures the symptoms of ACS.26 The 13 symptoms include: chest pressure, shoulder pain, sweating, palpitations, chest discomfort, upper back pain, shortness of breath, arm pain, unusual fatigue, nausea, lightheaded, chest pain, indigestion, and an option for other in that order. The ACS Symptom Checklist has demonstrated reliability and validity in prior studies.28 Participants indicate whether the symptom is present or absent. Other symptoms can be recorded in a blank space marked “other.” Each symptom is analyzed individually, and there is no summary score. Persistent symptoms were defined as any symptoms reported following hospital discharge.
Healthcare Utilization
Froelicher’s Health Services Utilization Questionnaire-Revised29 was used to measure subsequent clinic visits, calls to an MD/NP , visits to the ED, admissions to the hospital, myocardial infarction, and stroke. The instrument is a telephone survey and has demonstrated initial reliability and validity in Froelicher et al.’s follow-up survey of health care utilization29 in women with cardiovascular disease. Study designed calendars marked with 30-day and 6-month time from enrollment were provided to all participants so they could easily record healthcare utilization information following hospital discharge and know when to expect a call from the investigators. Patients received a $10 gift card for their participation.
Data Analysis
Descriptive statistics were used to analyze sample demographic and clinical characteristics. Mann Whitney, t-test, or chi-squared tests were used as indicated in the analyses. Logistic regression was used to estimate associations between prehospital delay time and symptom variables, and healthcare utilization variables 30-days and 6-months following hospital discharge. A separate model was run with each symptom and healthcare utilization variable as dependent variables. The following covariates were included in the models: sex, age, race/ethnicity, income, insurance status, diabetes status, and final diagnosis (ACS vs. non-ACS). Models were also estimated for the ACS and non-ACS groups separately to elucidate potential symptom/delay time associations. A separate model was estimated for each symptom and healthcare utilization variable as a dependent variable with delay time as the predictor of interest. Models were adjusted for the same covariates except diagnosis. Analyses were run using R version 4.0.3.30
There were few missing data for demographic variables except for income. Patients with missing data on covariates or the specific symptom or healthcare utilization variable for each logistic regression model were excluded from analyses. To determine if there were differences between the patients with complete data compared to those with missing data, we conducted sensitivity analyses. At the 30-day follow-up, while those with missing income tended to be older, they did not differ from those who provided income data in either symptoms or healthcare utilization at 30-day follow-up. At 6-month follow-up, the pattern was more complex with those missing income (and thus not included in the logistic regression models) remaining older, but with some differences in utilization (more likely to have had heart-related calls to MD/NP) and symptoms (more likely to have shoulder or back pain or fatigue and less likely to have shortness of breath than those with income data).
Results
Characteristics of the Sample
Participants were 70.6% White/Caucasian, predominately male (62.3%) and ages ranged from 29–98 years (mean 60.64). Income data were missing for 68/722 (9.4%) participants. The majority of the sample reported an income below $50,000 (65.1%), with 30.7% having an income below $20,000 (Table 1). At 6-months, 32.6% of participants were lost to follow-up (n=149, 31.4% with ACS, n=193, 32.7% without ACS). No significant differences in baseline demographic and clinical characteristics were found between those in the 30-day follow-up sample and those at baseline who did not have 30-day follow-up data. However, by 6-month follow-up, some differences appeared; specifically, the total 6-month sample was slightly older, with a higher percentage of White/Caucasian participants, with higher reported income, and who were more likely to have health insurance than those who were lost to follow-up (Table 1).
Table 1.
Sample Characteristics at 30-day and 6-Month Follow-up by Cohort Status
| Variable median (interquartile range), mean (SD) or frequency (%1) | 30-days Following Discharge | 6-months Following Discharge | ||||
|---|---|---|---|---|---|---|
| Total Cohort (n=722)2 | ACS (n=325) | No ACS (n=397) | Total Cohort (n=633)2 | ACS (n=284) | No ACS (n=349) | |
| Delay time in hours median (Interquartile range)3 | 6.77 (23.33) |
7.15 (23.18) | 6.75 (24.87) | 6.30 (22.17) |
6.19 (22.41) |
6.40 (22.60) |
| Age in years, mean (SD)4 | 60.64 (13.84) |
62.28 (11.77)** | 59.3 (15.21)** |
61.02
(13.86)* |
62.00 (11.98) | 60.22 (15.19)* |
| Sex | ||||||
| Male | 450 (62.3%) |
232 (71.4%)** | 218 (54.1%)** | 396 (62.6%) |
202 (71.1%)** | 194 (55.6%)** |
| Female | 272 (37.7%) |
93 (28.6%)** | 179 (45.1%)** | 237 (37.4%) |
82
(28.9%)** |
155 (44.4%)** |
| Race/ethnicity | ||||||
| White or Caucasian | 509 (70.6%) |
225 (69.2%) | 284 (71.7%) | 471 (74.5%) |
208 (73.5%) |
263 (75.4%) |
| Black or African American | 95 (13.2%) |
41 (12.6%) | 54 (13.6%) |
68 (10.8%) |
31 (11.0%) |
37 (10.6%) |
| Hispanic | 54 (7.5%) |
30 (9.2%) |
24 (6.1%) |
38 (6.0%) |
22 (7.8%) |
16 (4.6%) |
| Other | 63 (8.7%) |
29 (8.9%) |
34 (8.6%) |
55 (8.7%) |
22 (7.8%) |
33 (9.5%) |
| Health Insurance | ||||||
| Private from employer | 239 (33.6%) |
109 (34.4%) | 130 (33.0%) | 215 (34.4%) |
97 (34.9%) |
118 (34.0%) |
| Private paid by patient | 65 (9.1%) |
28 (8.8%) |
37 (9.4%) |
65 (10.4%) |
30 (10.8%) |
35 (10.1%) |
| Medicare | 239 (33.1%) |
103 (32.5%) | 136 (34.5%) | 212 (33.5%) |
97 (32.0%) |
118 (35.4%) |
| Government, or other, insurance | 84 (11.8%) |
33 (10.4%) | 51 (12.9%) |
68 (10.7%) |
30 (10.8%) |
38 (11.0%) |
| Not Insured | 84 (11.8%) |
44 (13.9%) | 40 (10.2%) |
65 (10.3%) |
32 (11.5%) |
33 (9.5%) |
| Income | ||||||
| <$20,000 | 201 (30.7%) |
69 (24.2%)** | 132 (35.8%)** | 173 (30.0%) |
64 (22.5%) |
109 (33.7%) |
| $20,000 - $50,000 | 225 (34.4%) |
103 (36.1%)** | 122 (33.1%)** | 193 (33.5%) |
86 (30.3%) |
107 (33.1%) |
| $50,000 - $100,000 | 134 (20.5%) |
77 (27.0%)** | 57 (15.5%)** | 118 (20.5%) |
63 (22.2%) |
55 (17.0%) |
| $100,000+ | 94 (14.4%) |
36 (12.6%)** | 58 (15.7%)** | 92 (16.0%) |
40 (14.1%) |
52 (16.1%) |
| Diabetes | 212 (29.4%) |
99 (30.5%) | 113 (28.5%) | 184 (29.0%) |
80 (28.3%) |
104 (29.9%) |
| Mode of Arrival to ED | ||||||
| Self-Transport | 383 (53.2%) |
170 (52.5%)* | 213 (53.8%)* | 349 (55.1%) |
151 (53.2%) | 198 (56.9%) |
| EMS | 319 (44.3%) |
142 (43.8%)* | 177 (44.7%)* | 269 (42.5%) |
122 (43.0%) | 147 (42.2%) |
| Transfer from another facility | 18 (2.5%) |
12 (3.7%)* |
6 (1.5%)* |
14 (2.2%) |
11 (3.9%) |
3 (0.9%) |
Notes.
ACS is acute coronary syndrome. SD is standard deviation. ED is emergency department.
Percentages calculated from cases with non-missing data for specific variable
n=0–11 cases in the total sample with missing data on sample characteristics. For 30-day follow-up, n=68 in the total sample had data missing for income (40 for ACS and 28 for no ACS). Missing for other variables in the total sample ranged from 0 to 21 (0–9 in ACS, 0–12 in no ACS). For 6-month, n=57 in the total sample had data missing for income (31 for ACS and 26 for no ACS). Missing for other variables in the total sample ranged from 0 to 19 (0–6 in ACS, 0–13 for no ACS)
Mann-Whitney test used because of substantial non-normality
t-test for comparison (other variables, unless noted, used chi square)
p<.01
p<.05; bold type
Prehospital Delay and Persistent Symptoms
Frequency of persistent symptoms was examined for the entire cohort (patients evaluated for ACS in the ED) and by final diagnosis (ACS vs. non-ACS). For patients with ACS, 68.6% reported at least one symptom at 30 days and 55.6% at 6 months. For the no ACS group, 79.1% reported at least one symptom at 30 days and 72.5% at 6 months.
At 30-day follow up, the most frequently reported symptoms were shortness of breath , lightheadedness, unusual fatigue, chest discomfort, and shoulder pain. At 6-month follow up, the most frequently reported symptoms were shortness of breath, unusual fatigue, lightheadedness, chest discomfort, and shoulder pain (Table 2).
Table 2.
Frequencies of Symptoms and Healthcare Utilization at 30-days and 6-months Follow-up by Cohort Status
| Variable Frequency (%1) |
30-days Following Discharge | 6-months Following Discharge | ||||
|---|---|---|---|---|---|---|
| Total Cohort (n=722)2 | ACS (n=325) |
No ACS (n=397) |
Total Cohort (n=633)2 | ACS (n=284) |
No ACS (n=349) | |
| Symptoms | ||||||
| Arm Pain | 143 (20.2%) |
54 (17.3%) |
89 (22.6%) |
112 (18.0%) |
35 (12.6%)** | 77 (22.3%)** |
| Chest Discomfort | 223 (31.4%) |
85 (26.9%)** |
138 (35.0%)** |
150 (24.0%) |
52 (18.6%)** | 98 (28.3%)** |
| Chest Pain | 161 (22.8%) |
54 (17.2%)** |
107 (27.2%)** |
114 (18.2%) |
42 (15.1%) |
72 (20.8%) |
| Chest Pressure | 171 (24.1%) |
57 (18.1%)** |
114 (28.9%)** |
124 (19.8%) |
42 (15.1%)** | 82 (23.7%)** |
| Indigestion | 150 (21.3%) |
58 (18.6%) |
92 (23.4%) |
140 (22.5%) |
35 (12.6%)** |
105 (30.5%)** |
| Lightheadedness | 224 (31.9%) |
90 (28.7%) |
134 (34.0%) |
155 (24.9%) |
54 (19.4%)** | 101 (29.3%)** |
| Nausea | 113 (16.1%) |
27 (8.6%)** |
86 (22.0%)** |
104 (16.6%) |
26 (9.3%)** |
78 (22.5%)** |
| Palpitations | 130 (18.4%) |
49 (15.7%) |
81 (20.6%) |
109 (17.5%) |
35 (12.5%)** |
74 (21.4%)** |
| Shortness of Breath | 252 (35.7%) |
90 (28.6%)** |
162 (41.4%)** |
176 (28.2%) |
59 (21.4%)** |
117 (33.9%)** |
| Shoulder Pain | 179 (23.3%) |
62 (19.7%)** |
117 (29.8%)** |
143 (22.9%) |
45 (16.1%)** | 98 (28.4%)** |
| Sweating | 160 (22.5%) |
50 (15.8%)** |
110 (27.9%)** |
105 (16.8%) |
36 (12.9%)** | 69 (20.0%)** |
| Unusual Fatigue | 224 (31.0%) |
86 (27.3%)** |
138 (35.0%)* |
166 (26.6%) |
65 (23.3%) |
101 (29.2%) |
| Upper Back Pain | 149 (21.0%) |
51 (16.2%)** |
98 (24.8%)** |
119 (19.0%) |
35 (12.5%)** | 84 (24.3%)** |
| Healthcare Utilization 2 | ||||||
| MD/NP visits-any reason | 586 (81.5%) |
277 (85.8%)** |
309 (78.0%)** |
552 (87.5%) |
241 (85.5%) |
311 (89.1%) |
| MD/NP visits for chest symptoms | 387 (53.9%) |
212 (65.8%)** |
175 (44.2%)** |
317 (50.3%) |
177 (63.0%)** | 140 (40.1%)** |
| MD/NP calls-any reason | 267 (37.2%) |
118 (38.8%) |
149 (37.6%) |
248 (39.4%) |
95 (33.8%)** | 153 (43.8%)** |
| MD/NP calls for chest symptoms | 132 (18.4%) |
62 (19.4%) |
70 (17.7%) |
121 (19.2%) |
59 (21.1%) |
62 (17.8%) |
| 911 calls-any reason | 47 (6.6%) |
22 (6.9%) |
25 (6.3%) |
89 (14.1%) |
35 (12.4%) |
54 (15.5%) |
| 911 calls for chest symptoms | 34 (4.8%) |
14 (4.4%) |
20 (5.1%) |
57 (9.1%) |
28 (10.0%) |
29 (8.3%) |
| ER visits-any reason | 121 (16.8%) |
57 (17.6%) |
64 (16.2%) |
197 (31.2%) |
87 (30.7%) |
110 (31.5%) |
| ER visits for chest symptoms | 72 (10.1%) |
35 (11.0%) |
37 (9.3%) |
105 (16.7%) |
50 (17.7%) |
55 (15.8%) |
| Overnight hospital stay | 83 (11.5%) |
34 (10.5%) |
49 (12.3%) |
106 (16.8%) |
43 (15.2%) |
63 (18.1%) |
Notes.
Percentages calculated from 30-day sample cases with non-missing data
n=2–18 cases in the total sample with missing data on symptoms and health utilization variables; similar missing data pattern for ACS and no ACS subgroups.
p<0.05
p< 0.01
In patients with ACS, there was no association between prehospital delay time and symptoms at 30 days or 6 months following discharge. For non-ACS patients, longer prehospital delay was associated with higher odds of experiencing chest pressure (OR, 1.009; CI, 1.001–1.017; p = 0.029) and chest discomfort (OR, 1.008; CI, 1.000, 1.016; p = 0.049) 30 days following discharge (n =397). These results indicate that for each 1-hour increase in prehospital delay, we would expect the odds of experiencing chest pressure at 30 days following discharge to increase by a factor of 1.008. For non-ACS patients at 6 months, longer prehospital delay was associated with higher odds of experiencing upper back pain (OR, 1.013; CI, 1.003–1.023; p = 0.009), palpitations (OR 0.014; CI 1.003–1.024, p = 0.006), and indigestion (OR, 1.010; CI, 1.001, 1.019; p = 0.035) (n = 349). All results were adjusted for age, sex, race, income, insurance, and diabetes.
Prehospital Delay and Healthcare Utilization
For the total cohort, the most common forms of healthcare utilization at 30 days were MD/NP office visits for any reason (n=586, 81.5%), MD/NP office visits related to chest symptoms (n=387, 53.9%), and MD/NP calls (n=267, 37.2%) (Table 3). At 6 months, MD/NP office visits for any reason (n=552, 87.5%), MD/NP office visits related to chest symptoms (n=317, 50.3%), and MD/NP calls (n=248, 39.4%) remained the most common forms of healthcare utilization (Table 4).
Table 3:
Relationships among delay time with 30-day follow-up symptoms and healthcare utilization outcomes (adjusted for covariates)1,2
| Total Sample | ACS (n=325) | No ACS (n=397) | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) |
p | OR (95% CI) |
p | OR (95% CI) |
p | |
| Symptom 3 | ||||||
| Arm Pain | 1.001 (0.993, 1.008) |
0.878 | 0.994 (0.980, 1.009) |
0.444 | 1.004 (0.995, 1.012) |
0.424 |
| Chest Discomfort | 1.002 (0.996, 1.009) |
0.441 | 0.992 (0.981, 1.004) |
0.216 | 1.008 (1.000, 1.016) |
0.049* |
| Chest Pain | 1.003 (0.996, 1.009) |
0.426 | 0.996 (0.982, 1.009) |
0.537 | 1.005 (0.997, 1.013) |
0.222 |
| Chest Pressure | 1.005 (0.998, 1.012) |
0.137 | 0.993 (0.980, 1.007) |
0.317 | 1.009 (1.001, 1.017) |
0.029* |
| Indigestion | 1.004 (0.997, 1.011) |
0.279 | 0.996 (0.983, 1.010) |
0.572 | 1.007 (0.999, 1.015) |
0.107 |
| Lightheadedness | 1.000 (0.993, 1.006) |
0.883 | 0.998 (0.987, 1.010) |
0.781 | 0.999 (0.991, 1.007) |
0.856 |
| Nausea | 1.006 0.999, 1.014) |
0.093 | 1.009 (0.994, 1.025) |
0.223 | 1.005 (0.996, 1.014) |
0.260 |
| Palpitations | 1.002 (0.995, 1.009) |
0.492 | 1.008 (0.996, 1.020) |
0.185 | 0.999 (0.990, 1.008) |
0.823 |
| Shortness of Breath | 1.002 (0.995, 1.008) |
0.576 | 0.998 (0.987, 1.010) |
0.770 | 1.004 (0.996, 1.012) |
0.314 |
| Shoulder Pain | 0.999 (0.992, 1.006) |
0.832 | 0.995 (0.981, 1.008) |
0.441 | 1.001 (0.993, 1.010) |
0.743 |
| Sweating | 1.003 (0.995, 1.010) |
0.477 | 0.997 (0.983, 1.011) |
0.656 | 1.006 (0.997, 1.014) |
0.214 |
| Unusual Fatigue | 0.998 (0.992, 1.004) |
0.580 | 0.994 (0.982, 1.005) |
0.289 | 1.001 (0.993, 1.008) |
0.896 |
| Upper Back Pain | 1.003 (0.995, 1.009) |
0.478 | 1.000 (0.987, 1.013) |
0.993 | 1.003 (0.995, 1.012) |
0.436 |
| Healthcare Utilization 4 | ||||||
| MD/NP visits | 0.994 (0.987, 1.001) |
0.087 | 0.986 (0.974, 0.997) | 0.015* | 0.998 (0.989, 1.007) |
0.713 |
| Visits for heart symptoms | 0.998 (0.992, 1.004) |
0.505 | 0.990 (0.981, 1.000) | 0.055 | 1.002 (0.995, 1.010) |
0.513 |
| MD/NP calls | 1.003 (0.997, 1.009) |
0.345 | 1.004 (0.994, 1.014) | 0.427 | 1.002 (0.994, 1.010) |
0.604 |
| Calls for heart symptoms | 1.002 (0.997, 1.009) |
0.622 | 1.007 (0.996, 1.018) | 0.235 | 0.999 (0.989, 1.009) |
0.835 |
| 911 calls-any reason | 0.993 (0.978, 1.005) | 0.314 | 0.996 (0.976, 1.018) | 0.743 | 0.990 (0.972, 1.009) |
0.302 |
| 911 calls for heart symptoms | 0.999 (0.969, 1.005) | 0.219 | 0.978 (0.940, 1.017) | 0.268 | 0.993 (0.973, 1.013) |
0.480 |
| ER visits | 0.999 (0.971, 1.007) | 0.822 | 1.001 (0.989, 1.013) | 0.915 | 0.998 (0.988, 1.009) |
0.769 |
| ER visits for heart symptoms | 0.999 (0.988, 1.008) |
0.790 | 1.001 (0.985, 1.017) |
0.921 | 0.999 (0.985, 1.013) |
0.839 |
| Overnight hospital stay | 1.000 (0.991, 1.009) |
0.978 | 1.009 (0.996, 1.022) |
0.186 | 0.994 (0.981, 1.006) |
0.335 |
Notes.
Analysis sample sizes: n=266–276 for ACS; n=359–365 for no ACS; n=625–641 for total sample (sample sizes differ across models because of missing data)
Covariates: ACS status (reference category: no), age, gender (ref: male), race (ref: white), income (ref: <$20,000), insurance (ref: no insurance), diabetes (ref :no)
Reference category for symptom outcomes is “symptom not reported [severity=0]”
Reference category for utilization outcomes is zero visits, zero calls, no overnight stay
p<.05, bold type
Table 4:
Relationships among delay time with 6-month follow-up symptoms and healthcare utilization outcomes (results from logistic regression with covariates)1,2
| Total Sample | ACS | No ACS | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) |
p | OR (95% CI) |
p | OR (95% CI) |
p | |
| Symptom | ||||||
| Arm Pain | 1.000 (0.991,1.009) |
0.939 | 0.993 (0.976,1.010) |
0.420 | 1.004 (0.993, 1.014) |
0.503 |
| Chest Discomfort | 1.006 (0.998, 1.013) |
0.146 | 1.004 (0.992, 1.017) |
0.504 | 1.007 (0.997, 1.016) |
0.175 |
| Chest Pain | 1.005 (0.997, 1.013) |
0.236 | 1.001 (0.986, 1.015) |
0.944 | 1.008 (0.998, 1.019) |
0.122 |
| Chest Pressure | 1.001 (0.991, 1.008) |
0.912 | 0.992 (0.977, 1.008) |
0.344 | 1.003 (0.993, 1.013) |
0.572 |
| Indigestion | 1.008 (1.000, 1.015) |
0.044* | 1.005 (0.990, 1.020) |
0.537 | 1.010 (1.001, 1.019) |
0.035* |
| Lightheadedness | 1.002 (0.995, 1.009) |
0.554 | 0.994 (0.980, 1.008) |
0.383 | 1.005 (0.996, 1.015) |
0.249 |
| Nausea | 1.007 (0.999, 1.016) |
0.079 | 1.014 (0.999, 1.029) |
0.076 | 1.006 (0.995, 1.016) |
0.277 |
| Palpitations | 1.011 (1.003, 1.019) |
0.006* | 1.006 (0.993, 1.020) |
0.377 | 1.014 (1.003, 1.024) |
0.009* |
| Shortness of Breath | 1.000 (0.993, 1.008) |
0.924 | 0.995 (0.981, 1.008) |
0.439 | 1.004 (0.994, 1.013) |
0.453 |
| Shoulder Pain | 0.999 (0.990, 1.007) |
0.784 | 0.994 (0.979, 1.010) |
0.490 | 1.000 (0.991, 1.010) |
0.932 |
| Sweating | 1.002 (0.993, 1.010) |
0.688 | 1.000 (0.984, 1.015) |
0.959 | 1.002 (0.991, 1.013) |
0.738 |
| Unusual Fatigue | 1.003 (0.996, 1.011) |
0.353 | 1.003 (0.991, 1.015) |
0.660 | 1.005 (0.995, 1.014) |
0.334 |
| Upper Back Pain | 1.008 (1.000, 1.015) |
0.054 | 0.998 (0.983, 1.014) |
0.802 | 1.013 (1.003, 1.023) |
0.009* |
| Healthcare Utilization | ||||||
| MD/NP visits-any reason | 1.009 (0.998, 1.021) |
0.141 | 1.006 (0.990, 1.022) |
0.463 | 1.014 (0.996, 1.033) |
0.122 |
| Visits for chest symptoms | 1.005 (0.998, 1.011) |
0.170 | 1.010 (0.998, 1.021) |
0.112 | 1.003 (0.994, 1.011) |
0.559 |
| MD/NP calls-any reason | 1.004 (0.997, 1.010) |
0.281 | 1.007 (0.996, 1.018) |
0.196 | 1.003 (0.994, 1.012) |
0.492 |
| Calls for heart symptoms | 1.011 (1.003, 1.018) |
0.006* | 1.008 (0.997, 1.020) |
0.161 | 1.014 (1.004, 1.025) |
0.006* |
| 911 calls-any reason | 1.001 (0.992, 1.011) |
0.759 | 1.015 (1.001, 1.030) |
0.038* | 0.993 (0.980, 1.007) |
0.335 |
| 911calls for chest symptoms | 1.006 (0.995, 1.016) |
0.236 | 1.016 (1.001, 1.031) |
0.042* | 1.000 (0.984, 1.016) |
0.990 |
| ER visits-any reason | 0.996 (0.988, 1.003) |
0.252 | 0.997 (0.986, 1.009) |
0.646 | 0.994 (0.984, 1.004) |
0.255 |
| ER visits for chest symptoms | 0.998 (0.989, 1.007) |
0.674 | 0.998 (0.984, 1.012) |
0.769 | 0.998 (0.986, 1.011) |
0.796 |
| Overnight hospital stay | 1.001 (0.992, 1.009) |
0.827 | 0.991 (0.974, 1.008) |
0.294 | 1.007 (0.996, 1.018) |
0.213 |
Notes.
Analysis sample sizes: n=240–245 for ACS; n=316–320 for no ACS; n=556–565 for total sample (sample sizes differ across models because of missing data)
Covariates: ACS status (reference category: no), age, gender (ref: male), race (ref: white), income (reference: <$20,000), insurance (reference: no insurance), diabetes (reference: no diabetes)
p<0.05
For patients with ACS (n = 325), longer prehospital delay was associated with fewer MD/NP office visits (OR, 0.986; CI, 0.974– 0.997; p = 0.015) at 30 days. Longer prehospital delay was also associated with higher odds of calling 911 for any reason (OR, 1.015; CI, 1.001–1.030; p = 0.038) and calling 911 for chest symptoms (OR, 1.016; CI, 1.001, 1.031; p = 0.042) at 6 months. For non-ACS patients, there was no association between prehospital delay time and healthcare utilization at 30 days following discharge. Longer prehospital delay was associated with higher odds of calls to the MD/NP for chest symptoms (OR, 1.014; CI, 1.004–1.025; p = 0.006) at 6 months. All results were adjusted for age, sex, race, income, insurance, and diabetes.
Discussion
The purpose of the study was to determine if there was an association between prehospital delay for symptoms suggestive of ACS, persistent symptoms, and healthcare utilization at 30-days and 6-months following hospital discharge. Little is known about these relationships. We found that a high proportion of patients had persistent symptoms and the cause of the symptoms require further study.
Delay time is an important predictor of patient morbidity and mortality in patients with ACS,31 thus we hypothesized that prehospital delay would be more likely to be associated with persistent symptoms at 30 days and 6 months post hospital discharge. There could be several plausible explanations for our findings that there were few associations among prehospital delay, symptoms, or healthcare utilization in ACS patients. First, patients who delayed seeking treatment frequently had mild, vague, or non-traditional symptoms of ACS (i.e. symptoms other than chest pain)32 which led to their seeking treatment. Non-traditional symptoms can confuse patients, lead to consultations with family or acquaintances, or treating with home remedies further delaying time-dependent therapies. Second, all participants in the study received medical therapy including cardiac medications (e.g., beta-blockers, ACE inhibitors, calcium channel blockers, nitrates, anti-platelet agents) that would increase cardiac perfusion and mitigate symptoms. For example, beta-blockers reduce oxygen consumption by lowering heart rate. Anti-platelets reduce the risk of reocclusion leading to symptoms from ischemia. It is also possible that there was symptom overlap with other conditions. For instance, for patients with chest pain/pressure caused by indigestion or including gastroesophageal reflux disease (GERD), nitrates are not likely to be effective for non-ischemic pain. Medications may reduce persistent symptoms following ACS if the symptoms are related to ischemia but may have no effect if symptoms are caused by a non-cardiac condition. Finally, patients who are discharged with a diagnosis of ACS would be specifically directed to seek follow-up care and would receive guidance on how to respond to and manage ongoing symptoms or other health concerns.31
For non-ACS patients, longer delay was associated with persistent symptoms of chest pressure and chest discomfort at 30-days and indigestion, palpitations, and upper back pain at 6-months. There may be several plausible explanations for this. Non-ACS patients may have delayed seeking care in the ED as they tried to attribute a cause to their symptoms yet continued to suffer from these ongoing symptoms following discharge. More than eighty percent of patients evaluated in the ED for ACS will be ruled-out and may be experiencing musculoskeletal, rheumatologic, gastrointestinal, respiratory, psychosomatic, or other causes.32–34 For example, gastroesophageal disorders, including GERD, are the most common causes of non-cardiac chest pain.35 In patients with non-cardiac chest pain, GERD is present in 50–60% of cases.36 In a retrospective study, one group of authors enrolled patients who presented to the ED complaining of chest pain as a primary symptom and experienced pain at least twice a year.37 They found that frequent ED visits were less likely to have an underlying cardiac, musculoskeletal, or rheumatologic cause but were more likely to represent a psychosomatic disorder (33.9% vs. 9.9%).
We also found that longer prehospital delay in patients with ACS was associated with fewer MD/NP clinic visits at 30-days, and more general calls to the MD/NP, and calls related to chest symptoms at 6-months following discharge. The American College of Cardiology/American Heart Association (ACC/AHA) guidelines for ST-elevation myocardial infarction (STEMI) emphasizes timely follow up with the healthcare team after an ACS hospitalization.31 In addition, outpatient appointments should be scheduled to reflect the goals set for each patient in accordance with their clinical condition and needs. The ACC/AHA guidelines recommend follow-up visits to assess symptoms, to evaluate psychological status, and to ensure appropriate medication use. Similarly, for patients with non-STEMI, ACC/AHA guidelines recommend outpatient follow-up for low-risk patients and patients who have undergone revascularization within 2 to 6 weeks, and earlier follow-up for higher-risk patients.7 Therefore, we would expect that patients with ACS would have fewer clinic visits and more general calls to the MD/NP at 30 days post discharge and this is supported by our data.
For non-ACS patients, longer prehospital delay was associated with more calls to the NP/MD for chest symptoms at 6 months. Indigestion, palpitations, and upper back pain may have been interpreted by the participants as being of potential cardiac origin. Additionally, more calls at 6-months may be related to persistent symptoms or to underlying comorbid conditions. MD/NPs may also use patient-initiated calls as an opportunity to evaluate clinical symptoms; provide patient education; assess medication adherence, evaluate complications; make appointments; and schedule tests.38,39 Finally, it is possible that those who delay seeking care for the index symptoms also utilize fewer clinic services. This may reflect a subset of patients who are more likely to use the ED for primary or non-emergent care.40 Those patients may delay seeking care in the ED because they seldom utilize outpatient medical care or because they wait until symptoms become severe. Our sample included a high proportion of patients in low income groups (65% earned ≤$50,000). Low income has been associated with the lack of primary care provider41,42 and more frequent ED visits.43 Individuals who are economically disadvantaged should receive transitional care and be monitored closely for follow-up care following admission to the ED for evaluation of ACS.
Patients without ACS represent a significant population of patients with high healthcare utilization44 and frequent readmissions19,34,45 resulting in high health care and societal costs.46 Most patients presenting to the ED, ruled-out for ACS, will be diagnosed with noncardiac chest pain. One challenge in the ED is that more than 5.5 million individuals present with chest pain but only 13% are diagnoses with ACS.47 The question then becomes, what happens next for the 87% ruled-out for ACS? If these patients do not get follow-up care to assess the source of their symptoms, they may continue to have symptoms leading to more ED visits, 911 calls, or extensive healthcare utilization. This is one of the failures of the US healthcare system, which tends focus on acute illness and disease but avoid ongoing care to address continuing symptoms. The unique aspect of our data is that we have highlighted the challenges posed by patients ruled-out for ACS but for whom the etiology of symptoms may remain undiagnosed.
Data were also collected prior to the widespread use of high sensitivity (hs) troponins.48 The newly published 2021 Guidelines for the Diagnosis and Management of Chest Pain34 suggest that patients who have been ruled-out for ACS, with hs troponins, yet have frequent healthcare utilization, should undergo extensive testing to determine the cause of their symptoms. Unresolved symptoms up to 6 months following hospital discharge may also represent an unmet need for symptom relief interventions. Future studies may target this group to reduce unplanned hospital readmissions and high healthcare utilization. We recommend that healthcare providers include counseling at discharge on the need to seek urgent care for symptoms suggestive of ACS and that they call 911 if they have a history of ischemic heart disease or significant chest symptoms and shortness of breath.
Strengths
The study included a large, diverse, and heterogeneous sample enrolled at 5 sites. Patients were given calendars to mark the time to expect a follow-up call as well as a copy of the Froelicher instrument to record their healthcare utilization. Patients received three phone calls prior to being identified as lost to follow-up.
Limitations
There were several limitations to the study: the data were originally collected for another purpose; symptoms and healthcare utilization were self-reported; we do not know if follow-up visits were planned or unplanned; we do not know the outcomes of the follow-up visits; and nearly 1 in 3 patients were lost to follow-up for the 6-month HCU data. This is important since the 6-month sample was slightly older, had higher income, more likely to be insured, and with a higher percentage of White/Caucasian participants than those who were lost to follow-up.
Conclusion
A high proportion of patients presenting to the ED with symptoms suggestive of ACS have persistent symptoms for at least 6 months following hospital discharge, demonstrating the need for improved understanding of why patients who had access to high quality care emergency care have other ongoing complaints.49,50 There were few associations between prehospital delay time, ongoing symptoms at 30-days following discharge, or subsequent healthcare utilization and those associations that were statistically significant had very small odds ratios. At 6-months, some associations began to emerge. Longer delay time was associated with greater likelihood of indigestion, palpitations, and upper back pain and with having made heart related calls to MD/NP. Evaluation of persistent symptoms in those ruled-out for ACS in the ED is warranted given their high health care utilization. More research is needed to determine if there is a link between prehospital delay and unscheduled healthcare utilization following hospital discharge in patients evaluated for ACS, particularly for those patients ruled- out for ACS. This knowledge could guide clinicians in helping patients to evaluate the quality and significance of future symptoms.
Highlights.
Patients ruled-out for ACS continue to have symptoms leading to more emergency department visits, 911 calls, and healthcare utilization
The unique aspect of our data is that we have highlighted the challenges posed by patients ruled-out for ACS but for whom the etiology of symptoms may remain undiagnosed
More research is needed to determine if there is a link between prehospital delay and unscheduled healthcare utilization following hospital discharge in patients evaluated for ACS
Funding:
This work was supported by the by the National Heart, Lung, and Blood Institute (grant number R21HL140277)
Footnotes
Competing Interest: none
References
- 1.Christiansen EC, Wickstrom KK, Henry TD, et al. Comparison of functional recovery following percutaneous coronary intervention for ST elevation myocardial infarction in three age groups (<70, 70 to 79, and ≥80 years). Am J Cardiol 2013;112(3):330–335. [DOI] [PubMed] [Google Scholar]
- 2.Lamberti M, Ratti G, Gerardi D, et al. Work-related outcome after acute coronary syndrome: Implications of complex cardiac rehabilitation in occupational medicine. Int J Occup Med Environ Health 2016;29(4):649–657. [DOI] [PubMed] [Google Scholar]
- 3.Napoli AM, Baird J, Tran S, Wang J. Low Adverse Event Rates But High Emergency Department Utilization in Chest Pain Patients Treated in an Emergency Department Observation Unit. Critical Pathways in Cardiology 2017;16(1). [DOI] [PubMed] [Google Scholar]
- 4.DeVon HA, Burke LA, Nelson H, Zerwic JJ, Riley B. Disparities in patients presenting to the emergency department with potential acute coronary syndrome: It matters if you are Black or White. Heart Lung 2014;43(4):270–277. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ibanez B, James S, Agewall S, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). European Heart Journal 2018;39(2):119–177. [DOI] [PubMed] [Google Scholar]
- 6.Moser DK, Kimble LP, Alberts MJ, et al. Reducing Delay in Seeking Treatment by Patients With Acute Coronary Syndrome and Stroke. Circulation 2006;114(2):168–182. [DOI] [PubMed] [Google Scholar]
- 7.Goldberg RJ, Spencer FA, Fox KA, et al. Prehospital Delay in Patients With Acute Coronary Syndromes (from the Global Registry of Acute Coronary Events [GRACE]). Am J Cardiol 2009;103(5):598–603. [DOI] [PubMed] [Google Scholar]
- 8.Saczynski JS, Yarzebski J, Lessard D, et al. Trends in prehospital delay in patients with acute myocardial infarction (from the Worcester Heart Attack Study). Am J Cardiol 2008;102(12):1589–1594. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rosenfeld AG. Treatment-seeking delay among women with acute myocardial infarction: decision trajectories and their predictors. Nurs Res 2004;53(4):225–236. [DOI] [PubMed] [Google Scholar]
- 10.Banks AD, Dracup K. Are there gender differences in the reasons why African Americans delay in seeking medical help for symptoms of an acute myocardial infarction? Ethn Dis 2007;17(2):221–227. [PubMed] [Google Scholar]
- 11.DeVon HA, Hogan N, Ochs AL, Shapiro M. Time to treatment for acute coronary syndromes: the cost of indecision. J Cardiovasc Nurs 2010;25(2):106–114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gorelik O, Almoznino-Sarafian D, Yarovoi I, et al. Patient-dependent variables affecting treatment and prediction of acute coronary syndrome are age-related: A study performed in Israel. Int J Cardiol 2007;121(2):163–170. [DOI] [PubMed] [Google Scholar]
- 13.De Luca G, Suryapranata H, Ottervanger JP, Antman EM. Time Delay to Treatment and Mortality in Primary Angioplasty for Acute Myocardial Infarction. Circulation 2004;109(10):1223–1225. [DOI] [PubMed] [Google Scholar]
- 14.McKee G, Mooney M, O’Donnell S, O’Brien F, Biddle MJ, Moser DK. Multivariate analysis of predictors of pre-hospital delay in acute coronary syndrome. International Journal of Cardiology 2013;168(3):2706–2713. [DOI] [PubMed] [Google Scholar]
- 15.Mackay MH, Ratner PA, Nguyen M, Percy M, Galdas P, Grunau G. Inconsistent measurement of acute coronary syndrome patients’ pre-hospital delay in research: A review of the literature. European Journal of Cardiovascular Nursing 2014;13(6):483–493. [DOI] [PubMed] [Google Scholar]
- 16.Levine GN, Bates ER, Blankenship JC, et al. 2015 ACC/AHA/SCAI Focused Update on Primary Percutaneous Coronary Intervention for Patients With ST-Elevation Myocardial Infarction: An Update of the 2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention and the 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Society for Cardiovascular Angiography and Interventions 2015. (1524–4539 (Electronic)). [Google Scholar]
- 17.Virani SS, Alonso A, Aparicio HJ, et al. Heart disease and stroke statistics—2021 update: a report from the American Heart Association. Circulation 2021;143(8):e254–e743. [DOI] [PubMed] [Google Scholar]
- 18.Makam RP, Erskine N, Yarzebski J, et al. Decade long trends (2001–2011) in duration of pre‐hospital delay among elderly patients hospitalized for an acute myocardial infarction. Journal of the American Heart Association 2016;5(4):e002664. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Center for Medicare and Medicaid Services. Medicare Hospital Quality 2017 Chartbook: Performance Report on Outcome Measures https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/OutcomeMeasures. Published 2017. Accessed December 11, 2021.
- 20.Southern DA, Ngo J, Martin BC, et al. Characterizing Types of Readmission After Acute Coronary Syndrome Hospitalization: Implications for Quality Reporting. Journal of the American Heart Association 2014;3(5):e001046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Alnasser SMA, Huang W, Gore JM, et al. Late Consequences of Acute Coronary Syndromes: Global Registry of Acute Coronary Events (GRACE) Follow-up. The American Journal of Medicine 2015;128(7):766–775. [DOI] [PubMed] [Google Scholar]
- 22.Breen K, Finnegan L, Vuckovic K, Fink A, Rosamond W, DeVon HA. Multimorbidity in Patients With Acute Coronary Syndrome Is Associated With Greater Mortality, Higher Readmission Rates, and Increased Length of Stay: A Systematic Review. Journal of Cardiovascular Nursing 2020;35(6). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Zhao Z, Winget M. Economic burden of illness of acute coronary syndromes: medical and productivity costs. BMC Health Services Research 2011;11(1):35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wong MK, Wang JT, Czarnecki A, et al. Factors associated with physician follow-up among patients with chest pain discharged from the emergency department. Canadian Medical Association Journal 2015(1488–2329 (Electronic)). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Fernando SM, Tran A, Cheng W, et al. Prognostic accuracy of the HEART score for prediction of major adverse cardiac events in patients presenting with chest pain: a systematic review and meta‐analysis. Acad Emerg Med 2019;26(2):140–151. [DOI] [PubMed] [Google Scholar]
- 26.DeVon HA, Rosenfeld A, Steffen AD, Daya M. Sensitivity, specificity, and sex differences in symptoms reported on the 13-item Acute Coronary Syndrome Checklist. J Am Heart Assoc 2014;3(2):e000586. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.DeVon HA, Burke LA, Vuckovic KM, et al. Symptoms Suggestive of Acute Coronary Syndrome: When Is Sex Important? J Cardiovasc Nurs 2016. [DOI] [PubMed] [Google Scholar]
- 28.DeVon HA, Ryan CJ, Ochs AL, Shapiro M. Symptoms across the continuum of acute coronary syndromes: Differences between women and men. Am J Crit Care 2008;17(1):14–24; quiz 25. [PMC free article] [PubMed] [Google Scholar]
- 29.Froelicher ES, Sohn M, Max W, Bacchetti P. Women’s Initiative for Nonsmoking-VII: Evaluation of health service utilization and costs among women smokers with cardiovascular disease. J Cardiopulm Rehabil 2004;24(4):218–228. [DOI] [PubMed] [Google Scholar]
- 30.R Core Team. R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Published 2013. Accessed August 15, 2021. [Google Scholar]
- 31.O’Gara PT, Kushner FG, Ascheim DD, et al. 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction. Circulation 2013;127(4):e362–e425. [DOI] [PubMed] [Google Scholar]
- 32.Moeller A, Mills E, Gnesin F, et al. Symptom presentation of acute myocardial infarction–can we correctly identify patients with atypical symptoms of myocardial infarctions over the phone? Eur Heart J 2020;41(Supplement_2):ehaa946. 1830. [Google Scholar]
- 33.O’Gara PT, Kushner FG, Ascheim DD, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;61(4):e78–e140. [DOI] [PubMed] [Google Scholar]
- 34.Gulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2021;144(22):e368–e454. [DOI] [PubMed] [Google Scholar]
- 35.Fass R, Achem SR. Noncardiac chest pain: epidemiology, natural course and pathogenesis. J Neurogastroenterol Motil 2011;17(2):110–123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Frieling T. Non-Cardiac Chest Pain. Visc Med 2018;34(2):92–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Slankamenac K, Stucki M, Keller DI. Chest pain in repeated emergency department visitors. Journal of Clinical Cardiology 2020;1(1). [Google Scholar]
- 38.Tang L, Li K, Wu C-J. Thirty-day readmission, length of stay and self-management behaviour among patients with acute coronary syndrome and type 2 diabetes mellitus: A scoping review. Journal of Clinical Nursing 2020;29(3–4):320–329. [DOI] [PubMed] [Google Scholar]
- 39.Guo P, Harris R. The effectiveness and experience of self-management following acute coronary syndrome: A review of the literature. International Journal of Nursing Studies 2016;61:29–51. [DOI] [PubMed] [Google Scholar]
- 40.Hardy M, Cho A, Stavig A, et al. Understanding frequent emergency department use among primary care patients. Population health management 2018;21(1):24–31. [DOI] [PubMed] [Google Scholar]
- 41.Sommers AS, Boukus ER, Carrier E. Dispelling myths about emergency department use: majority of Medicaid visits are for urgent or more serious symptoms Vol 23: Center for Studying Health System Change Washington (DC); 2012. [PubMed] [Google Scholar]
- 42.Kellermann AL, Weinick RM. Emergency departments, Medicaid costs, and access to primary care—understanding the link. N Engl J Med 2012;366(23):2141–2143. [DOI] [PubMed] [Google Scholar]
- 43.Rising KL, White LF, Fernandez WG, Boutwell AE. Editor’s Capsule Summary. Ann Emerg Med 2013;2(62):145–150. [DOI] [PubMed] [Google Scholar]
- 44.Mol KA, Smoczynska A, Rahel BM, et al. Non-cardiac chest pain: prognosis and secondary healthcare utilisation. Open Heart 2018;5(2):e000859. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Ruddox V, Mathisen M, Otterstad JE. Prevalence and prognosis of non-specific chest pain among patients hospitalized for suspected acute coronary syndrome - a systematic literature search. BMC Med 2012;10:58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Mourad G, Alwin J, Stromberg A, Jaarsma T. Societal costs of non-cardiac chest pain compared with ischemic heart disease--a longitudinal study. BMC Health Serv Res 2013;13:403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Bhuiya FA, Pitts SR, McCaig LF. Emergency department visits for chest pain and abdominal pain: United States, 1999–2008. NCHS data brief 2010(43):1–8. [PubMed] [Google Scholar]
- 48.Greenslade JH, Carlton EW, Van Hise C, et al. Diagnostic accuracy of a new high-sensitivity troponin I assay and five accelerated diagnostic pathways for ruling out acute myocardial infarction and acute coronary syndrome. Ann Emerg Med 2018;71(4):439–451. e433. [DOI] [PubMed] [Google Scholar]
- 49.Erskine NA, Waring ME, McManus DD, Lessard D, Kiefe CI, Goldberg RJ. Barriers to healthcare access and long-term survival after an acute coronary syndrome. J Gen kern [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Kern DM, Mellström C, Hunt PR, et al. Long-term cardiovascular risk and costs for myocardial infarction survivors in a US commercially insured population. Curr Med Res Opin 2016;32(4):703–711. [DOI] [PubMed] [Google Scholar]