Factors Associated With Prehospital Delay Among Patients With Acute Myocardial Infarction in the Era of Percutaneous Coronary Intervention　― Insights From the OACIS Registry ―

Akihiro Ogushi; Shungo Hikoso; Tetsuhisa Kitamura; Daisaku Nakatani; Hiroya Mizuno; Shinichiro Suna; Katsuki Okada; Tomoharu Dohi; Yohei Sotomi; Hirota Kida; Akihiro Sunaga; Bolrathanak Oeun; Taiki Sato; Yasuhiko Sakata; Hiroshi Sato; Masatsugu Hori; Issei Komuro; Hiroyasu Iso; Yasushi Sakata; on behalf of the Osaka Acute Coronary Insufficiency Study (OACIS) Group

doi:10.1253/circj.CJ-21-0777

Abstract

Background: The Japan Circulation Society launched the STOP-MI campaign in 2014, focusing on immediate hospital arrival for acute myocardial infarction (AMI) treatment. This study aimed to determine the factors influencing longer prehospital time among patients with AMI in Japan.

Methods and Results: This study analyzed a total of 4,625 AMI patients enrolled in the Osaka Acute Coronary Insufficiency Study registry from 1998 to 2014. The prehospital time delay was defined as the time interval from the onset of initial symptoms to hospital arrival time ≥2 h. Among eligible patients, 2,927 (63.3%) had a prehospital time ≥2 h. In multivariable analyses, age 65–79 years (adjusted odds ratio [AOR] 1.19, 95% confidence interval [CI] 1.02–1.39), age ≥80 years (AOR 1.42, 95% CI 1.13–1.79), diabetes mellitus (AOR 1.33, 95% CI 1.16–1.52), and onset time of 0:00–5:59 h (AOR 1.63, 95% CI 1.37–1.95) were positively associated with prehospital time ≥2 h, whereas smoking (AOR 0.78, 95% CI 0.68–0.90) and ambulance use (AOR 0.37, 95% CI 0.32-0.43) were negatively associated with prehospital time ≥2 h.

Conclusions: Older age, diabetes mellitus, and nighttime onset were associated with prehospital time delay for AMI patients, whereas smoking and ambulance use were associated with no prehospital time delay. Healthcare providers and patients could help reduce the time to get to a medical facility by being aware of these findings.

Acute myocardial infarction (AMI) is an important public health problem in the industrialized world.¹^,² Approximately 33,000 patients die of AMI in Japan every year.³ Since the 2000s, definitive interventions, such as primary percutaneous coronary intervention (PCI), including balloon angioplasty and stent implantation, have improved clinical outcomes for patients with AMI, but the benefits to morbidity and mortality of these interventions are time-dependent.⁴^–⁶ The American College of Cardiology/American Heart Association guidelines for AMI recommend primary PCI within 90 min of first medical contact, or door-to-balloon time, as a class 1 recommendation, and a total ischemic time within 120 min.⁷^,⁸ Despite the importance of timely care-seeking, many patients experience a delay from symptom onset to hospital arrival time,⁹ leading to increased mortality because treatments are not given in a timely manner.¹⁰ To minimize total ischemic time, many organizations have launched awareness campaigns to educate the public. The Japan Circulation Society launched the STOP-MI campaign in 2014. This campaign includes educational activities focusing on recognizing warning signs and symptoms, knowing what to do when experiencing these warning signs, and arriving at a hospital immediately.¹¹

Editorial p ????

Previous studies have shown that longer prehospital time was associated with socio-demographic factors such as old age, female sex, and clinical factors such as diabetes mellitus (DM), AMI history, and heart failure;¹²^–¹⁴ however, these studies concerned with prehospital time delay were conducted in foreign countries. Very few studies in Japan have reported on the time delay from AMI onset to hospital arrival. From 1988 to 2006, a small population-based AMI registry reported that a prehospital time delay >2 h was associated with a history of hypertension, angina, syncope, and nighttime onset.¹⁵ However, the relationship between the patients’ characteristics and prehospital time delay has not been assessed sufficiently in the recent PCI era.

The Osaka Acute Coronary Insufficiency Study (OACIS) registry is a multicenter study of patients with AMI. Demographic, procedural, and outcome data on AMI patients at 25 collaborating hospitals in Osaka, Japan, were collected from 1998 to 2014. Using this database, the aim of the present study was to determine the factors influencing longer prehospital time of patients with AMI.

Methods

The OACIS Registry and Patients

The OACIS is a multicenter, prospective, observational registry of consecutive patients with AMI at 25 collaborating hospitals located in Osaka, Japan, and is registered with the University Hospital Medical Information Network Clinical Trials Registry in Japan (ID: UMIN000004575). It is designed to assess patient demographics, therapeutic procedures, and subsequent clinical events in patients with AMI. All data were retrospectively obtained from the patients’ medical records, and the completed case report forms were transferred to the Osaka University Graduate School of Medicine through a secure virtual private network. The study protocol was approved by the ethics committee of each participating hospital, and each patient provided written informed consent. A detailed description of the OACIS has been published elsewhere.¹⁶^,¹⁷

Data Collection and Definitions

A diagnosis of AMI was made if the patient fulfilled at least 2 of the following 3 criteria: (1) history of central chest pressure, pain, or tightness lasting 30 min; (2) typical electrocardiogram (ECG) changes (i.e., ST-segment elevation ≥0.1 mV in 1 standard limb lead or 2 precordial leads, ST-segment depression ≥0.1 mV in 2 leads, abnormal Q waves, or T-wave inversion in 2 leads); and (3) an increase in serum creatine kinase levels of twice the upper normal limit in each hospital.¹⁶^,¹⁸ Demographic characteristics, medical history, and in-hospital information about patients were prospectively collected by determined cardiologists and research coordinators during the period of hospitalization. Data about the patients’ clinical characteristics included the following: age; sex; presence of living together; employment status; a history of acute coronary syndrome (ACS [angina and/or myocardial infarction]); DM; hypertension; dyslipidemia; smoking and drinking habits; ambulance use; onset time information such as hour, day of the week, season, and year; and in-hospital information such as symptoms at onset (typical [chest pain pressure or burning] or not), whether PCI was performed during index admission in the OACIS registry, Killip classification, peak creatine kinase (≥3,000 IU/L or not), AMI type (Q wave or not), ST-segment (elevation or not), and death during hospitalization.

Key Group Definition

Prehospital time was defined as the interval from the onset of initial symptoms to the time of hospital arrival. The cut-off of 2 h was chosen to distinguish early (<2 h) from late (≥2 h) responders to their symptoms of AMI based on previous studies and guidelines.⁸^,¹⁴^,¹⁵^,¹⁹ Guidelines recommend that primary PCI should be performed in patients with AMI and ischemic symptoms of <12 h duration.¹⁹ We focused on patients with symptoms ≤12 h from AMI onset in this study.

Statistical Analyses

First, we assessed the distribution of prehospital time every 30 min. Next, we assessed the trend in prehospital time from 1998 to 2014. The Cochran-Armitage trend test for categorical variables and the Jonckheere-Terpstra trend test for continuous variables were used to examine trends. Patient characteristics, medical history, onset time information, and in-hospital information were compared between prehospital time <2 h and ≥2 h. Continuous data were expressed as means and standard deviations or medians and interquartile ranges (IQRs), and categorical data were expressed as absolute numbers and percentages. Continuous variables were compared using the Student’s t-test. Proportions were compared between groups using the chi-squared test. Logistic regression analyses were used to examine factors associated with prehospital time ≥2 h. Potential confounding factors before hospital arrival based on previous studies were included in the univariable and multivariable analyses.¹²^,¹⁴^,¹⁵ These variables were as follows: age (<65 years, 65–79 years, ≥80 years); sex (male, female); existence of living together (alone, not); employment status (unemployed, not); history of ACS (yes, no); DM (yes, no); hypertension (yes, no); dyslipidemia (yes, no); smoking and drinking habits (yes, no); ambulance use (yes, no); onset time (daytime, nighttime); day of the week (weekday, weekend); season (spring, summer, autumn, winter); and year (1998–2003, 2004–2008, 2009–2014). In a sensitivity analysis, we also examined prehospital time of ≥4 h and ≥6 h based on a previous study.¹⁴^,¹⁵ Results were presented as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). In addition, differences in prehospital time were assessed for each prehospital factor. The significance level was set at P<0.05. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

Results

Study Population

Among 12,093 patients registered in the OACIS registry from April 1998 to April 2014, 7,948 patients with a prehospital time ≤12 h from AMI onset were enrolled in this study. Patients who developed AMI in hospital (n=25) and were transferred from other medical facilities (n=3,258) or had unknown transfer information (n=40) were excluded from this study. Finally, we analyzed 4,625 eligible patients with AMI (Figure 1).

Figure 1.

Flowchart detailing patient selection for this study.

Among these patients, 2,927 (63.3%) arrived at the hospital within 2 h. The median prehospital time was 81 min (IQR 47–165 min). The category with the largest number of patients was 30–59 min (n=1,164), accounting for 25.2% of all patients (Figure 2).

Figure 2.

Distribution of symptom onset to hospital arrival time.

As shown in Figure 3, the proportion of patients with prehospital time ≥2 h decreased significantly from 42.8% in 1998 to 31.8% in 2014 (P for trend <0.001). The median time also decreased significantly from 90 min in 1998 to 74.5 min in 2014 (P for trend <0.001).

Figure 3.

Temporal trend in the prehospital time from 1998 to 2014.

Table 1 shows the characteristics of eligible patients with prehospital time <2 h and ≥2 h. Age was significantly higher in the group with prehospital time ≥2 h than in the group with prehospital time <2 h (mean, 66.7±11.9 vs. 64.2±12.1, P<0.001). The proportion of females and unemployed patients in the group with prehospital time ≥2 h was also significantly higher than in the group with prehospital time <2 h. The group with prehospital time ≥2 h was more likely to have ACS, DM, and hypertension, but less likely to smoking and use an ambulance than the group with prehospital time <2 h. As for in-hospital information, the group with prehospital time ≥2 h was more likely to have typical symptoms at onset, but less likely to have PCI, Killip class ≥II, STEMI, and die during hospitalization than the group with prehospital time <2 h.

Table 1. Clinical Characteristics of Patients With AMI According to Prehospital Time

	Prehospital time
	Missing, n	<2 h (n=2,927)	≥2 h (n=1,698)	P value
Baseline characteristics
Age, years, mean (SD)	17	64.2 (12.1)	66.7 (11.9)	<0.001
<65 years, n (%)		1,488 (51.0)	709 (42.0)	<0.001
65–79 years, n (%)		1,114 (38.2)	738 (43.7)
≥80 years, n (%)		316 (10.8)	243 (14.4)
Female, n (%)	15	556 (19.1)	409 (24.2)	<0.001
Living alone, n (%)	1,199	382 (17.2)	214 (17.7)	0.692
Unemployed, n (%)	897	1,160 (50.1)	827 (58.5)	<0.001
Medical history, n (%)
ACS	201	828 (29.6)	541 (33.4)	0.008
Diabetes mellitus	186	860 (30.8)	609 (36.9)	<0.001
Hypertension	212	1,643 (59.1)	1,027 (63.0)	0.010
Dyslipidemia	278	1,231 (44.8)	756 (47.3)	0.120
Smoking	168	1,524 (54.3)	739 (44.7)	<0.001
Drinking	359	1,246 (46.7)	717 (44.9)	0.257
Ambulance use, n (%)	124	2,392 (84.2)	1,117 (67.3)	<0.001
Onset time, h, n (%)	0			<0.001
06:00–11:59		831 (28.4)	491 (28.9)
12:00–17:59		732 (25.0)	341 (20.1)
18:00–23:59		844 (28.8)	398 (23.4)
00:00–05:59		520 (17.8)	468 (27.6)
Day of week
Weekend (Saturday–Sunday), n (%)	0	859 (29.4)	523 (30.8)	0.298
Season, n (%)	0			0.101
Spring (March–May)		754 (25.8)	422 (24.9)
Summer (June–August)		647 (22.1)	414 (24.4)
Autumn (September–November)		767 (26.2)	401 (23.6)
Winter (December–February)		759 (25.9)	461 (27.2)
Year, n (%)	0			<0.001
1998–2003		1,014 (34.6)	671 (39.5)
2004–2008		872 (29.8)	557 (32.8)
2009–2014		1,041 (35.6)	470 (27.7)
Inhospital information, n (%)
Typical symptoms at onset	649	2,349 (94.3)	1,440 (97.0)	<0.001
PCI	145	2,677 (94.2)	1,505 (91.9)	0.004
Killip class ≥II	217	689 (24.7)	316 (19.6)	<0.001
Peak CK ≥3,000 IU/L	121	1,098 (38.5)	592 (35.8)	0.075
Q wave	220	1,810 (65.1)	1,082 (66.5)	0.341
STEMI	125	2,508 (88.7)	1,380 (82.6)	<0.001
Death during hospitalization	1	279 (9.5)	132 (7.8)	0.043

ACS, acute coronary syndrome; AMI, acute myocardial infarction; CK, creatine kinase; PCI, percutaneous coronary intervention; SD, standard deviation; STEMI, ST-elevation myocardial infarction.

Multivariable Analyses

Factors associated with prehospital time ≥2 h are shown in Table 2, with age 65–79 years (AOR 1.19, 95% CI 1.02–1.39), age ≥80 years (AOR 1.42, 95% CI 1.13–1.79), unemployment (AOR 1.32, 95% CI 1.11–1.55), DM (AOR 1.33, 95% CI 1.16–1.52), and onset time of 0:00–5:59 h (AOR 1.63, 95% CI 1.37–1.95) positively associated with prehospital time ≥2 h. Conversely, factors such as smoking (AOR 0.78, 95% CI 0.68–0.90), ambulance use (AOR 0.37, 95% CI 0.32–0.43), and onset time of 12:00–17:59 h (AOR 0.80, 95% CI 0.67–0.95) and 18:00–23:59 h (AOR 0.84, 95% CI 0.71–0.99) were negatively associated with prehospital time ≥2 h. Supplementary Table shows differences in prehospital time for each prehospital factor. Prehospital time was 151.4 min for the group aged ≥80 years and 134.0 min for the group aged <80 years; 146.3 min for those patients who were unemployed and 131.1 min for patients who were employed; 148.7 min in patients with DM and 131.8 min in patients who did not have DM; and 176.2 min for patients who experienced nighttime AMI and 125.3 min for patients who experienced non-nighttime AMI. In a sensitive analysis of prehospital time ≥4 h and ≥6 h, factors associated with prehospital time delay were similar to those in prehospital time ≥2 h (Table 3).

Table 2. Univariable and Multivariable Logistic Regression of Factors Associated With the Prehospital Time ≥2 h

	Univariable analyses	P value	Multivariable analyses	P value
	Crude OR (95% CI)	P value	Adjusted OR (95% CI)	P value
Baseline characteristics
Age (years)
<65	Ref.		Ref.
65–79	1.39 (1.22–1.58)	<0.001	1.19 (1.02–1.39)	0.023
≥80	1.61 (1.34–1.95)	<0.001	1.42 (1.13–1.79)	0.003
Female	1.36 (1.17–1.57)	<0.001	1.09 (0.92–1.29)	0.328
Living alone	1.04 (0.86–1.25)	0.692	1.13 (0.93–1.37)	0.225
Unemployed	1.40 (1.23–1.60)	<0.001	1.32 (1.11–1.55)	0.001
Medical history
ACS	1.19 (1.05–1.36)	0.008	1.03 (0.89–1.19)	0.668
Diabetes mellitus	1.31 (1.16–1.49)	<0.001	1.33 (1.16–1.52)	<0.001
Hypertension	1.18 (1.04–1.34)	0.010	1.06 (0.93–1.22)	0.386
Dyslipidemia	1.10 (0.97–1.25)	0.120	1.07 (0.94–1.23)	0.294
Smoking	0.68 (0.60–0.77)	<0.001	0.78 (0.68–0.90)	0.001
Drinking	0.93 (0.82–1.05)	0.257	1.06 (0.92–1.22)	0.402
Ambulance use	0.39 (0.33–0.45)	<0.001	0.37 (0.32–0.43)	<0.001
Onset time, h
06:00–11:59	Ref.		Ref.
12:00–17:59	0.79 (0.67–0.94)	0.006	0.80 (0.67–0.95)	0.012
18:00–23:59	0.80 (0.68–0.94)	0.007	0.84 (0.71–0.99)	0.040
00:00–05:59	1.52 (1.29–1.80)	<0.001	1.63 (1.37–1.95)	<0.001
Day of week
Weekend (Saturday–Sunday)	1.07 (0.94–1.22)	0.298	1.11 (0.97–1.27)	0.129
Season
Spring (March–May)	Ref.		Ref.
Summer (June–August)	1.14 (0.96–1.36)	0.126	1.11 (0.92–1.32)	0.278
Autumn (September–November)	0.93 (0.79–1.11)	0.431	0.92 (0.77–1.09)	0.328
Winter (December–February)	1.09 (0.92–1.28)	0.335	1.07 (0.90–1.28)	0.426
Year
1998–2003	Ref.		Ref.
2004–2008	0.97 (0.84–1.12)	0.631	1.08 (0.89–1.31)	0.465
2009–2014	0.68 (0.59–0.79)	<0.001	0.87 (0.71–1.07)	0.198

ACS, acute coronary syndrome; CI, confidence interval; OR, odds ratio. In multivariable analyses, missing was handled as a dummy variable.

Table 3. Multivariable Logistic Regression of Factors Associated With the Prehospital Time ≥4 h and ≥6 h

	Multivariable analyses
	≥4 h	≥6 h
	Adjusted OR (95% CI)	Adjusted OR (95% CI)
Baseline characteristics
Age (years)
<65	Ref.	Ref.
65–79	1.19 (0.97–1.45)	1.25 (0.97–1.61)
≥80	1.45 (1.08–1.95)	1.22 (0.83–1.80)
Female	0.97 (0.78–1.21)	1.04 (0.79–1.37)
Living alone	1.34 (1.04–1.71)	1.04 (0.74–1.47)
Unemployed	1.21 (0.98–1.51)	1.16 (0.88–1.54)
Medical history
ACS	0.95 (0.79–1.15)	0.74 (0.58–0.94)
Diabetes mellitus	1.32 (1.11–1.57)	1.28 (1.02–1.60)
Hypertension	1.02 (0.86–1.22)	1.13 (0.90–1.42)
Dyslipidemia	0.99 (0.83–1.17)	0.96 (0.77–1.20)
Smoking	0.75 (0.62–0.90)	0.68 (0.53–0.85)
Drinking	1.08 (0.90–1.29)	1.08 (0.86–1.36)
Ambulance use	0.30 (0.25–0.35)	0.27 (0.21–0.33)
Onset time, h
06:00–11:59	Ref.	Ref.
12:00–17:59	0.97 (0.76–1.24)	0.95 (0.68–1.33)
18:00–23:59	1.18 (0.94–1.48)	1.35 (0.99–1.83)
00:00–05:59	2.17 (1.74–2.71)	3.08 (2.32–4.07)
Day of week
Weekend (Saturday–Sunday)	1.00 (0.83–1.19)	1.11 (0.89–1.39)
Season
Spring (March–May)	Ref.	Ref.
Summer (June–August)	1.08 (0.85–1.35)	1.18 (0.88–1.59)
Autumn (September–November)	0.90 (0.71–1.14)	0.95 (0.70–1.28)
Winter (December–February)	0.98 (0.78–1.23)	1.04 (0.78–1.40)
Year
1998–2003	Ref.	Ref.
2004–2008	1.21 (0.94–1.55)	1.13 (0.82–1.56)
2009–2014	0.89 (0.67–1.17)	0.77 (0.54–1.11)

ACS, acute coronary syndrome; CI, confidence interval; OR, odds ratio. In multivariable analyses, missing was handled as a dummy variable.

Discussion

Using a large-scale database from the AMI registry in Japan, we examined the time interval from AMI onset to the time of hospital arrival, as well as the factors associated with longer prehospital time among AMI patients in the PCI era. The main findings of the present study were that older age, DM, and the onset time of 0:00–5:59 h were associated with prehospital time ≥2 h, whereas ambulance use and smoking were associated with prehospital time <2 h.

Our results were similar to those from previous studies conducted in the United States (US), Sweden, and Japan.¹²^,¹⁴^,¹⁵^,²⁰ Age¹³^,²¹ and DM²² were factors that were less likely to cause the development of typical symptoms and prehospital time delay after AMI onset. At night, some AMI patients may hesitate to go to a hospital because they do not want to bother family members and/or others.²³^,²⁴ Therefore, efforts to promote early hospital arrival for AMI patients are needed to emphasize the use of consulting healthcare professionals, and ambulances, even at night immediately after AMI, especially for elderly and diabetic patients regardless of the presence or absence of typical symptoms.¹⁴

In contrast, the present study yielded results that were different from those of previous studies. We found that sex was not associated with prehospital time delay. An international registry of patients hospitalized with AMI and unstable angina reported that female sex was more likely associated with prehospital time delay,¹³ which was inconsistent with our findings. In contrast, another previous study of patients with AMI in the US reported that sex differences in prehospital time delay gradually reduced over the study period from 1986 to 2005.²⁰ Thus, sex differences in prehospital time delay were further reduced in the OACIS registry, which started in 1998, than in a registry based in the US. In addition, the absence of prehospital time delay for women in our study may be explained, in part, by the Japanese health insurance system providing all of its citizens with easy access to medical institutes and ambulances.

In our study, smoking tended to be associated with early hospital arrival, although previous studies using international and Japanese registries did not show this.¹³^,¹⁵ Smoking may trigger the development of AMI because it restricts coronary blood flow and lowers the ischemic threshold.²⁵ In addition, smoking leads to more intense pain,²⁶ which might result in smokers having earlier hospital arrival.

Our study showed that the time from AMI onset to hospital arrival was reduced by approximately 15 min during the study period. The MIYAGI-AMI registry study reported that approximately 60% of patients were hospitalized within 6 h after AMI onset, and this proportion remained stable from 1997 to 2008.¹⁸ Although a reason for the different time trend between the 2 studies is uncertain, the resuscitation time courses by emergency medical service (EMS) personnel for patients that suffer from out-of-hospital cardiac arrest were shortened in Osaka Prefecture during 1998–2006.²⁷ Thus, the reduced prehospital time would be possibly due to the improvements of the overall EMS system in Osaka Prefecture. In the US and Australia, efforts using a variety of media have been increased to encourage early hospital arrival for patients with AMI by targeting the general public and patients with high-risk AMI factors. These efforts included making patients aware of the signs of AMI, the importance of prompt first medical contact, and the promotion of using ambulances.²⁸^,²⁹ Furthermore, in Ireland, patient education was provided, including an action plan incorporating the behaviors leading to prehospital time delay at the onset of AMI and the family’s appropriate response, with follow up by phone and letter.³⁰ These efforts reportedly reduced prehospital time delay and increased the use of ambulances. The use of information and communication technology (ICT) would be useful in prehospital settings. For example, in Osaka, the mobile telemedicine system (MTS) that continuously transmits real-time 12-lead ECG from ambulances in a prehospital setting shortened the reperfusion delay in STEMI patients compared with those patients who did not have access to MTS.³¹ In addition, the introduction of a smartphone app, which allows paramedics to share real-time information on the transport situation of other ambulances and the treatment status of other patients after transport, improved patient acceptance by hospitals in Osaka Prefecture.³² Considering these results, we believe that the ICT contributes to shortening the prehospital time in patients with AMI. Our findings revealed the characteristics of patients who arrived late to hospital; this provides important evidence to help shorten the time between AMI onset and hospital arrival of patients; for example, the STOP-MI campaign in Japan.

Study Limitations

This study had several strengths, such as the large number of patients with AMI and the confirmation of factors influencing longer prehospital time in the recent PCI era, in comparison with previous studies.¹²^–¹⁵ In contrast, our study has several limitations. First, we could not exclude the influence of recall bias on AMI onset time from eligible patients; many previous studies about the time interval from AMI onset to hospital arrival for patients used self-reported methods such as the one used in this study.¹²^,¹⁴^,¹⁵ Second, the information on out-of-hospital sudden deaths due to AMI was not included in this study; no previous studies have also examined out-of-hospital sudden death. Third, the OACIS registry did not obtain information on the location of onset, income, educational status, distance to hospital, emotions, and perceptions at onset,³³^–³⁵ as these potential confounders may have influenced the prehospital time delay among AMI patients. Finally, our study did not evaluate the relationship between prehospital time and survival outcomes after hospital arrival. The survival outcome was significantly affected by other factors such as the time from hospital arrival to PCI or reperfusion successfully, clinical complications, chronic disease, and treatments by medical institutions;³⁶^–³⁹ hence, we considered the survival outcome as an inappropriate one in this study.

Conclusions

In this study population, older age, DM, and onset time of 0:00–5:59 h were associated with prehospital time delay in AMI patients, whereas smoking and ambulance use were associated with reduced in prehospital time. Healthcare providers and patients can help reduce the time to get to a medical facility by being aware of these findings.

Acknowledgments

We thank Nagisa Yoshioka, Satomi Kishimoto, Kyoko Tatsumi, Noriko Murakami, Mariko Kishida, Rie Nagai, Sugako Mitsuoka, and all other OACIS research coordinators and nurses for their excellent assistance with data collection. We also thank our colleagues from Osaka University Center of Medical Data Science and Advanced Clinical Epidemiology Investigator’s Research Project for providing insights and expertise for our research.

Sources of Funding

This work was supported by Grants-in-Aid for University and Society Collaboration (#19590816, #19390215, and #25461055) from the Japanese Ministry of Education, Culture, Sports, Science and Technology, Tokyo, Japan.

Disclosures

All authors have no conflicts of interest to report. Yasuhiko Sakata, Issei Komuro, Hiroyasu Iso and Yasushi Sakata are members of Circulation Journal’s Editorial Team.

IRB Information

The Osaka University Research Ethics Committee (reference No: 425) approved this study.

Data Availability

The deidentified participant data will be shared on a request basis. Please contact the corresponding author directly to request data sharing. The data contained baseline data of the patients and the study protocol in Japanese. The data will be available immediately. The data were analyzed using the Cochran-Armitage trend test for categorical variables, and the Jonckheere-Terpstra trend test for continuous variables were used to examine the trends. Continuous variables were compared using the Student’s t-test. Proportions were compared between groups using the chi-squared test. Logistic regression analyses were used to examine the impact of several variables associated with the prehospital time. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc.).

Appendix. OACIS Investigators

Chair

Yasushi Sakata, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita 565-0871, Japan.

Secretariat

Shungo Hikoso (Chief), Daisaku Nakatani, Hiroya Mizuno, Shinichiro Suna, Tomoharu Dohi, Takayuki Kojima, Akihiro Sunaga, Bolrathanak Oeun, Hirota Kida, Sugako Mitsuoka, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Suita, Japan.

Participating Hospitals of the OACIS (in alphabetical order)

Higashiosaka City Medical Center, Osaka; Ishinkai Yao General Hospital, Osaka; Japan Community Healthcare Organization Osaka Hospital, Osaka; Japan Community Healthcare Organization Osaka Minato Central Hospital, Osaka; Kaizuka City Hospital, Osaka; Kansai Rosai Hospital, Hyogo; Kashiwara Municipal Hospital, Osaka; Kawachi General Hospital, Osaka; Kitaosaka Hospital, Osaka; Kobe Ekisaikai Hospital, Hyogo; Meiwa Hospital, Hyogo; National Hospital Organization Osaka Minami Medical Center, Osaka; National Hospital Organization Osaka National Hospital, Osaka; Osaka Daiichi Hospital, Osaka; Osaka General Hospital of West Japan Railway Company, Osaka; Osaka General Medical Center, Osaka; Osaka Kaisei Hospital, Osaka; Osaka Police Hospital, Osaka; Osaka Rosai Hospital, Osaka; Osaka University Hospital, Osaka; Saiseikai Senri Hospital, Osaka; Sakurabashi Watanabe Hospital, Osaka; Settsu Iseikai Hospital, Osaka; Teramoto Kinen Hospital, Osaka; Yao Municipal Hospital, Osaka.

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References

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