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Impact of crowding in local ambulance demand on call-to-ambulance scene arrival in out-of-hospital cardiac arrest

a b s t r a c t

Background: Rapid emergency medical service (EMS) response is an important prognostic factor in out-of- hospital cardiac arrest (OHCA). This study aims to evaluate the association between local hourly EMS demand and ambulance response in OHCA. Methods: OHCA occurring in 24 districts of Seoul from 2013 to 2018 was analyzed. Hourly ambulance demand per ambulance in each local district of patient location at the hour of cardiac arrest was calculated as the crowding index. The crowding index was categorized according to quartiles (1Q: <=0.43, 2Q: 0.44-0.67, 3Q: 0.68-0.99, 4Q: >=1.0 calls/h\r/ambulance). The primary outcome was ambulance dispatched within 1 km of the OHCA scene. Multivariable logistic regression analysis was performed to test the association between the local hourly ambulance demand and outcomes.

Results: A total of 26,479 patients were analyzed. The rate of ambulance dispatched within 1 km decreased ac- cording to the crowding quartile (1Q: 31.3%, 2Q: 30.0%, 3Q: 28.8%, and 4Q: 26.6%). Compared to 1Q, adjusted odds ratios (95% CIs) of dispatch distance within 1 km in 2Q, 3Q, and 4Q were 0.92 (0.86-0.99), 0.86 (0.80-0.94), and 0.77 (0.71-0.84), respectively.

Conclusion: Crowding in local ambulance demand was associated with less ambulance dispatched within 1 km and delayed response to the scene in OHCA. Strategies to mitigate and adjust to ambulance demand crowding may be considered for better EMS response performance.

(C) 2021 Published by Elsevier Inc.

  1. Introduction

Out-of-hospital cardiac arrest (OHCA) leads to a severe social burden around the world with high mortality [1]. A rapid response to emer- gency medical service (EMS) ambulance is known to be related to better survival and neurologic outcomes in OHCA [2-5].

Early dispatch and arrival of ambulance to the scene is important for delivery of crucial EMS interventions such as high-quality chest com- pressions and defibrillation to the patients. Although the rate of by- stander cardiopulmonary resuscitation (CPR) has increased due to dispatcher-assisted CPR [6], a certain proportion of patients cannot re- ceive bystander CPR, and the quality of bystander CPR might be lower

* Corresponding author at: Department of Emergency Medicine, Seoul National University Boramae Medical Center, 20 Boramae-ro 5-gil, Sindaebang-dong, Dongjak-gu, Seoul, South Korea.

E-mail address: [email protected] (T.H. Kim).

than that of CPR performed by professional EMS providers [7-9]. Addi- tionally, defibrillation according to accurate Rhythm analysis and ad- vanced airway management could be delayed upon late response and arrival of EMS providers at the scene. Therefore, the response time inter- val (RTI) is counted as an index for evaluating the performance of EMS in OHCA [10].

Various factors are associated with the response and availability of ambulances, such as the number of ambulance demands, the size of local EMS resources, traffic conditions, and even climate [11-13]. A re- cent study showed an increase in ambulance demand exceeding popu- lation growth, especially an increased demand from patients who do not require emergent medical intervention [14].

Crowding is a matter of mismatch between demand and supply in the medical field. The Prehospital stage of resuscitation in OHCA can also be affected by crowding caused by limited EMS resources and in- creasing demand for EMS. Although crowding in the emergency depart- ment (ED) has already been shown to have a negative influence in terms of safety and clinical outcomes in multiple previous reports.

https://doi.org/10.1016/j.ajem.2021.12.003 0735-6757/(C) 2021 Published by Elsevier Inc.

[15-17], less is known about the effect of crowding of ambulance de- mand in the prehospital stage. This study aims to evaluate the associa- tion between ambulance demand crowding, which is defined as the hourly EMS ambulance call number, and ambulance response perfor- mance in OHCA.

  1. Methods
    1. Study design and setting

This is a retrospective, observational, and cross-sectional study using the national OHCA registry.

    1. Study setting

The EMS in Korea is based on a single-tiered, fire-based, and government-sponsored system. EMS in Seoul supports a population of approximately 9.7 million people and provides a basic to intermediate level of ambulance services in 25 administrative districts. Each fire sta- tion owns several satellite ambulance stations responsible for ambu- lance dispatch in the district. A fire station in a district runs three to eight ambulance stations, depending on the residential population of the district.

In South Korea, the telephone number of emergent medical calls is “119” throughout the country. When citizens call 119 for emergent sit- uations, the call is connected to the local 119 dispatch center. A single dispatch center receives all 119 emergency calls within Seoul and dis- patches an ambulance according to the availability of the closest ambu- lance in the district.

EMS providers can perform CPR with basic life support (BLS) levels at the scene and during transport with automatic external defibrillation (AED) and advanced airway management under direct medical control. Advanced cardiac life support medications are available at the emergency department (ED) and are limited in most prehospital areas of the city. The 2015 AHA guidelines have been accepted as the standard guideline by national academic organizations. [18,19]

    1. Data source and collection

This study used the nationwide OHCA database, which includes the dispatcher registry, basic ambulance run sheet, prehospital EMS cardiac arrest registry, and hospital record review. An EMS run sheet and pre- hospital EMS cardiac arrest registry were used in this study. [20-23] The EMS run sheet and prehospital EMS cardiac arrest registry, which includes Utstein factors, such as demographics and prehospital EMS management variables, was recorded by the on-duty EMS provider.

    1. Study population

All EMS-treated OHCA cases from 2013 to 2018 in 25 districts of Seoul were initially enrolled for analysis. Patients with arrest occurring in one district (Geumcheon-gu), unknown exposure, unknown age and sex, and unknown outcomes were excluded. Geumcheon-gu District was excluded because there were no official fire stations in the district at the time of the study period.

    1. Variables

The main exposure was local EMS ambulance call number per ambu- lance in each district of Seoul. All EMS ambulance call numbers in each administrative district were calculated for every hour during the study period and divided by the number of ambulances in each administrative district to evaluate EMS ambulance demand. The local EMS ambulance demand was categorized into four interquartile groups (1Q: <=0.43, 2Q: 0.44-0.67, 3Q: 0.68-0.99, 4Q: >=1.0 calls/h/ambulance). Utstein, demo- graphic, and prehospital variables, including age, gender, weekend, EMS call time (day or night), place of arrest (public or private), cause of arrest (medical or non-medical), witnessed status, bystander CPR, initial electrocardiogram (ECG), EMS response time interval, scene time interval, and transport time interval were collected. Initial ECG was defined as the first ECG checked at the scene and categorized as shockable rhythm (ventricular fibrillation and pulseless ventricular tachycardia) and Non-shockable rhythm (pulseless electrical activity

Image of Fig. 1

Fig. 1. Study flow chart.

EMS: Emergency Medical Service OHCA: Out-of-Hospital Cardiac Arrest.

Image of Fig. 2

Fig. 2. Trend of EMS ambulance response performance according to hourly ambulance demand.

and asystole). The response time interval (RTI) was defined as the time interval from the time when the emergency call was received by a call- taker in the 119 dispatch center to the time when the ambulance ar- rived at the OHCA scene. The scene time interval (STI) was defined as the time interval from the time when the ambulance arrived at the scene to the time when the ambulance departed the scene. The trans- port time interval (TTI) was defined as the time interval from the time when the ambulance departed from the scene to the time when the am- bulance arrived at the ED.

    1. Outcome measures

The primary outcome was ambulance dispatching distance to the scene within 1 km. The secondary and tertiary outcomes were RTI within 4 min and prehospital return of spontaneous circulation (ROSC), respectively. In previous studies, an RTI less than 5 min was suggested for better outcomes, and this time interval was accepted as the secondary outcome in this study. [24,25] The distance between the 119 ambulance and arrest place was calculated according to the 119 satellite navigation systems.

Table 1

Demographic findings according to exposure groups.

All Hourly ambulance demand per one ambulance p-value

N

%

Quartile 1 (<=0.43)

N

%

Quartile 2

(0.44-0.67)

N

%

Quartile 3

(0.68-0.99)

N

%

Quartile 4 (>=1.0)

N

%

All

N

26,479

100.0

7190

100.0

7589

100.0

5240

100.0

6460

100.0

Year

<0.01

2013

4208

15.9

1964

27.3

1274

16.8

583

11.1

387

6.0

2014

4499

17.0

1873

26.1

1340

17.7

687

13.1

599

9.3

2015

4618

17.4

1283

17.8

1400

18.4

889

17.0

1046

16.2

2016

4326

16.3

782

10.9

1209

15.9

952

18.2

1383

21.4

2017

4320

16.3

671

9.3

1214

16.0

1019

19.4

1416

21.9

2018

4508

17.0

617

8.6

1152

15.2

1110

21.2

1629

25.2

Age

<0.01

Mean, SD

65.2 (18.8)

64.6 (18.9)

64.8 (18.7)

65.6 (18.8)

65.8 (18.7)

Sex

0.66

Male

17,031

64.3

4663

64.9

4884

64.4

3345

63.8

4139

64.1

Day of week

0.28

Weekend

7522

28.4

2100

29.2

2108

27.8

1480

28.2

1834

28.4

Weekday

18,957

71.6

5090

70.9

5481

72.2

3760

71.8

4626

71.6

Time of day

<0.01

Day (8A ~ 8P)

15,798

59.7

3687

51.3

4459

58.8

3308

63.1

4344

67.2

Night (8P ~ 8A)

10,681

40.3

3503

48.7

3130

41.2

1932

36.9

2116

32.8

Place of arrest

<0.01

Public

6988

26.4

1904

26.5

1967

25.9

1416

27.0

1701

26.3

Private

17,237

65.1

4520

62.9

4954

65.3

3423

65.3

4340

67.2

Others

2254

8.5

766

10.7

668

8.8

401

7.7

419

6.5

Cause of arrest

0.19

Medical

21,899

82.7

5935

82.5

6298

83.0

4316

82.4

5350

82.8

witnessed arrest

12,825

48.4

3575

49.7

3684

48.5

2516

48.0

3050

47.2

<0.01

Bystander CPR

15,625

59.0

4042

56.2

4563

60.1

3135

59.8

3885

60.1

<0.01

Shockable initial rhythm

4015

15.2

1093

15.2

1177

15.5

809

15.4

936

14.5

0.87

EMS response time interval (min)

<0.01

Median, IQR

5 (3-6)

5 (4-6)

5 (4-6)

5 (3-6)

5 (4-6)

Scene time interval (min)

<0.01

Median, IQR

10 (7-13)

9 (6-12)

10 (7-13)

11 (8-14)

11 (8-14)

Transport time interval (min)

<0.01

Median, IQR

6 (4-9)

6 (4-9)

6 (4-8)

6 (4-9)

6 (4-8)

Ambulance dispatch distance<=1 km

7756

29.3

2249

31.3

2278

30.0

1508

28.8

1721

26.6

<0.01

EMS RTI <= 4 min

6591

24.9

2154

30.0

1907

25.1

1232

23.5

1298

20.1

<0.01

prehospital ROSC

2328

8.8

541

7.5

681

9.0

478

9.1

628

9.7

<0.01

Survival to discharge

2822

10.7

732

10.2

833

11.0

569

10.9

688

10.7

0.43

Survival with good CPC

1559

5.9

383

5.3

461

6.1

319

6.1

396

6.1

0.13

SD: Standard Deviation CPR: Cardio-Pulmonary Resuscitation EMS: Emergency medical service. RTI: Response time interval IQR: Interquartile range ROSC: Return of Spontaneous Circulation. CPC: Cerebral Performance Category.

    1. Statistical analysis

Patient demographics and several factors related to OHCA, such as place of arrest, witness status, initial ECG rhythm, time of arrest, and year, were compared according to local EMS ambulance demand. Cate- gorical variables were described using counts and proportions and com- pared with the chi-square test. Continuous variables are presented as the median and interquartile range (IQR) using the Kruskal-Wallis test. A multivariable logistic regression analysis was performed to test the association between the local EMS ambulance demand and out- comes. Potential confounders, such as sex, age, place of arrest, witness, bystander CPR, and initial ECG rhythm, were adjusted. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for outcomes. The interaction analysis was performed to compare the ef- fect of the arrest time and local EMS demand per ambulance on the out- comes. All analyses were performed using SAS version 9.4 (SAS(C) Cary, NC, USA) and STATA version 16.1 software (StataCorp, Texas, USA).

    1. Ethics statement

The study was approved by the Institutional Review Board of

Table 2

Association between hourly ambulance call number and outcomes in logistic regression analysis.

Unadjusted Adjusted?

OR

95% CI

OR

95% CI

Ambulance dispatch distance within 1 km Quartile 1 (<=0.43)

1.00

1.00

Quartile 2 (0.44-0.67) 0.92

0.88-1.01

0.92

0.86-0.99

Quartile 3 (0.68-0.99) 0.89

0.82-0.96

0.86

0.80-0.94

Quartile 4 (>=1.0) 0.80

0.74-0.86

0.77

0.71-0.84

Every increase of 0.1 ambulance 0.97

0.96-0.98

0.97

0.96-0.98

demand

Ambulance arrival within 4 mins

Quartile 1 (<=0.43)

1.00

1.00

Quartile 2 (0.44-0.67)

0.78

0.73-0.84

0.91

0.85-0.98

Quartile 3 (0.68-0.99)

0.72

0.66-0.78

0.91

0.94-0.99

Quartile 4 (>=1.0)

0.59

0.54-0.64

0.81

0.74-0.88

Every increase of 0.1 ambulance

0.94

0.93-0.95

0.97

0.96-0.98

demand

Prehospital ROSC

Quartile 1 (<=0.43) 1.00 1.00

Quartile 2 (0.44-0.67) 1.21 1.08-1.36 1.12 0.97-1.28

Quartile 3 (0.68-0.99) 1.23 1.08-1.40 1.07 0.91-1.24

Quartile 4 (>=1.0) 1.32 1.17-1.49 1.16 0.99-1.34

investigator’s hospital with a waiver of informed consent (IRB No. 1103-153-357).

Every increase of 0.1 ambulance demand

1.03 1.02-1.04 1.01 1.00-1.03

  1. Results

Of the 28,139 eligible OHCA patients, 26,479 patients were finally analyzed after excluding patients with arrest that occurred outside of Seoul or in one district without fire station (N = 774), unknown age and gender (N = 21), unknown exposure (N = 11), and unknown out- comes or outliers (N = 854). (Fig. 1).

As the crowding index increased, the proportion of ambulance dispatched within 1 km of distance and the proportion of ambulance ar- rived at the scene within 4 min both decreased (both p for trend<0.01, Fig. 2).

The number (percent) of patients in each exposure group was 7190 (27.2%) in 1Q, 7589 (28.7%) in 2Q, 5240 (19.8%) in 3Q, and 6460 (24.4%)

in 4Q. The proportions of ambulance dispatching distance within 1 km in 1Q, 2Q, 3Q, and 4Q were 31.3%, 30.0%, 28.8%, and 26.6%, respectively. (Table 1).

The AORs (95% CIs) of distance between the dispatched 119 ambu- lance and arrest place within 1 km in 2Q, 3Q, and 4Q were 0.92 (0.86-0.99), 0.86 (0.80-0.94), and 0.77 (0.71-0.84), respectively. For

every increase of 0.1 ambulance demand per hour, the rate of distance between the dispatched 119 ambulance and arrest place equal to or less than 1 km decreased by approximately 3%. (Table 2).

In the interaction analysis (Table 3), quartiles 2-4 were all negatively associated with ambulance dispatch distance within 1 km during week- days (2Q: 0.91 (0.84-0.99) 3Q: 0.84 (0.77-0.93) 4Q:0.75 (0.68-0.82));

however, during weekends, only the highest quartile was associated with a lower chance of ambulance dispatch distance within 1 km (2Q:

0.96 (0.84-1.10) 3Q: 0.94 (0.81-1.09) 4Q:0.87 (0.75-0.99)).

  1. Discussion

The results in this study revealed that crowding in local ambulance demand was associated with ambulance dispatch from longer distances as well as delayed ambulance response to the scene of OHCA. To the best of our knowledge, this is the first study to evaluate and quantify the im- pact of crowding on EMS ambulance demand on the performance of EMS response.

Prehospital EMS in Korea is a public-based system without any cost charged to the patients. All medical emergency calls requesting ambu- lance dispatch with various levels of acuity are received by a single “119” route in a single dispatch center covering the entire Seoul Metro- politan city. Although emergency calls suspected of cardiac arrest have

OR: Odds ratio CI: Confidence Interval EMS: Emergency medical service. RTI: Response time interval ROSC: Return of Spontaneous Circulation.

* Adjusted for age, sex, year, weekday, time of day, place of arrest, cause of arrest

(medical/non-medical), witnessed, bystander CPR, initial ECG rhythm(shockable/non- shockable).

most priority regarding prompt ambulance dispatch, if the nearest am- bulance in the district is preoccupied to calls with lower level of acuity, ambulances from further distances have to be dispatched instead, caus- ing a delay in ambulance arrival.

Delayed ambulance arrival at the scene in OHCA might increase no- flow time and miss the optimal timing for successful defibrillation. In- creasing nonurgent EMS ambulance demands have been reported in a

Table 3

Interaction effect of time (day time/night time and weekdays/weekends) of arrest with EMS crowding level on outcomes in OHCA.

Day time / Night time Day time (8A ~ 8P) Night time (8P ~ 8A)

AOR?

95% CI

AOR?

95% CI

Ambulance dispatch distance within 1 km

Quartile 1 (<=0.43)

1.00

1.00

Quartile 2 (0.44-0.67)

0.97

0.87-1.07

0.88

0.79-0.98

Quartile 3 (0.68-0.99)

0.89

0.79-0.99

0.86

0.75-0.97

Quartile 4 (>=1.0)

0.77

0.69-0.85

0.81

0.71-0.91

Ambulance arrival within 4 mins

Quartile 1 (<=0.43)

1.00

1.00

Quartile 2 (0.44-0.67)

0.94

0.85-1.04

0.85

0.76-0.95

Quartile 3 (0.68-0.99)

0.96

0.86-1.08

0.85

0.74-0.97

Quartile 4 (>=1.0)

0.80

0.72-0.89

0.82

0.72-0.94

Weekday / Weekend Weekday (Mon – Fri) Weekend (Sat, Sun)

AOR?? 95% CI AOR?? 95% CI

Ambulance dispatch distance within 1 km

Quartile 1 (<=0.43) 1.00 1.00

Quartile 2 (0.44-0.67)

0.91

0.84-0.99

0.96

0.84-1.10

Quartile 3 (0.68-0.99)

0.84

0.77-0.93

0.94

0.81-1.09

Quartile 4 (>=1.0)

0.75

0.68-0.82

0.87

0.75-0.99

Ambulance arrival within 4 mins

Quartile 1 (<=0.43) 1.00 1.00

Quartile 2 (0.44-0.67) 0.87 0.80-0.96 0.96 0.84-1.11

Quartile 3 (0.68-0.99) 0.86 0.78-0.95 1.06 0.91-1.24

Quartile 4 (>=1.0) 0.77 0.69-0.85 0.89 0.77-1.04

EMS: Emergency Medical Service OHCA: Out-of-Hospital Cardiac Arrest.

AOR: Adjusted Odds Ratio CI: Confidence Interval RTI: Response Time Interval.

* Adjusted for age, sex, year, weekday, place of arrest, cause of arrest(medical/non- medical), witnessed, bystander CPR, initial ECG rhythm(shockable/non-shockable).

?? Adjusted for age, sex, year, time of day, place of arrest, cause of arrest(medical/non-

medical), witnessed, bystander CPR, initial ECG rhythm(shockable/non-shockable).

previous study [14]. As we found in this study, the more ambulances are dispatched due to increased EMS calls, the longer time takes to arrive at the scene in truly emergent cases such as OHCA. In our system, the same ambulance dispatch protocol is applied regardless of the acuity of the call. Adopting multitiered responses where ambulances with high ser- vice levels respond only to critical cases might be considered. The strat- egy to accurately distinguish the high acuity and emergent cases during the early phase of call-taking in dispatch centers might also be impor- tant. Flexible allocation of ambulance and scheduling of the EMS work- force during the period expected to be most crowded might also be beneficial to minimize the impact of crowding.

In the interaction analysis (Table 3), ambulance demand crowding

had a greater association with EMS response performance during the weekdays. This might be related to the fact that the traffic condition is better on weekends than on weekdays. Although we could not obtain detailed geological information, spatial analysis using real-time ambu- lance dispatch routes and traffic information could be considered to evaluate the discrepancy between weekends and weekdays.

Although the association was not statistically significant in multivar- iable logistic regression analysis, the prehospital ROSC rate was highest in quartile 4. We believe the reason for that is because crowding has been increasing in Seoul; therefore, more OHCAs that occurred in later years might have been included in higher quartile groups (Table 1). Due to increased technical and infrastructural support, the introduction of dispatcher-assisted CPR programs and an increased prehospital EMS scene resuscitation time interval, the unadjusted prehospital ROSC rate has been increasing in Seoul in recent years [26-28].

This study has a few limitations. First, OHCAs that occurred in one district (Geumcheon-gu) without local fire stations were excluded from the study. Therefore, the ambulance demand in the excluded dis- trict might have affected the availability of ambulances in adjacent dis- tricts, which could have affected the results. Second, even though the call-to-scene time interval was affected by hourly increased EMS de- mand, the relationship with actual clinical outcome remains unclear. Further studies should include clinical information to determine the as- sociation between EMS calls and clinical outcomes. Third, this study was a retrospective observational study performed in a metropolitan city with a public-based single-tiered EMS setting. Interpretation and appli- cation of our results should be carefully done considering the unique- ness of each EMS system.

  1. Conclusion

Increased demand for prehospital EMS ambulance was associated with a longer dispatching distance and delay in scene arrival of ambu- lance in OHCA. Considering the increasing ambulance demand-supply mismatch over the years, strategies to mitigate and adjust to EMS am- bulance demand crowding may be considered for better EMS response performance in critical conditions such as OHCA.

Funding Acknowledgement

None.

Declaration of competing interest

There are no conflicts of interest for all authors in this study.

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