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Abstract

Introduction

The regionalized nature of trauma care necessitates interfacility transfer which is vulnerable to delays given its complexity. Little is known about the interval of time a patient spends at the sending hospital prior to when the transfer is initiated—the “decision to transfer” time. This primary objective of the study was to explore the impact of patient, environmental, and institutional characteristics on decision to transfer time.

Methods

This was a retrospective cohort study of injured adult patients who underwent emergent interfacility transfer by a provincial critical care transport organization over a 31-month period. Quantile regression was used to evaluate the impact of patient, environmental, and institutional characteristics on the time to decision to transfer.

Results

A total of 1128 patients were included. The median decision to transfer time was 2.42 h and the median total transport time was 3.12 h. The following variables were associated with an increase in time to decision to transfer at the 90th percentile of time: age >75 (+2.47 h), age 66–75 (+3.70 h), age 56–65 (+1.20 h), transfer between 00:00 and 07:59 (+2.08 h), and transfer in the summer (+2.25 h). The following variables were associated with a decrease in time to decision to transfer at the 90^th percentile of time: Glasgow Coma Scale 3–8 (−2.21 h), respiratory rate >30 (−2.01 h), sending site being a community hospital with <100 beds (−4.11 h), or the sending site being a nursing station (−5.66 h).

Conclusion

Time to decision to transfer was a sizable proportion of the patients interfacility transfer. Older patients were associated with a delay in decision to transfer as were patients transferred overnight and in the summer. These findings may be used to support the implementation of geriatric trauma triage guidelines and promote ongoing education and quality improvement initiatives to decrease delay.

Keywords

Interfacility transfer trauma delay

Introduction

It is well established that regionalized trauma systems lead to improved outcomes,^1,2 but this regionalization necessitates interfacility transfer. Even when accounting for severity of illness, patients transferred to a level I trauma center have a mortality benefit compared to those that stay at non-trauma centers.³ Despite the improved outcomes, patients requiring interfacility transfer still have a delay in reaching definitive care and have a 30% increase in mortality in the first 48 h compared to those initially presenting to a trauma center, necessitating ongoing research to streamline the interfacility transfer process to reduce time to definitive care.²

Interfacility transfer is a complex process with numerous steps creating ample opportunity for unintended and modifiable delay (Figure 1). Strides have been made to examine various intervals in the transfer process, especially those delays associated with the transport itself.^4–6 The interval of time prior to the initiation of interfacility transfer is less well studied. For this study, we defined the “decision to transfer” time to be the time from patient arrival at the hospital until the healthcare worker made the call to the transport organization to organize transfer. This time interval includes patient registration, assessment, and diagnostic testing. Decisions to transfer are often made in consultation with regional trauma center, burn center, and neurosurgical referral guidelines.

Figure 1.

Components of transfer.

This study aimed to examine the decision to transfer time and determine if specific patient, environmental, or institutional characteristics are associated with a delay in decision to transfer time.

Methods

This was a retrospective cohort study of injured patients undergoing interfacility transfer transported by a provincial critical care transport organization. This study was approved by the Research Ethics Board at Sunnybrook Health Sciences Centre (REB ID: 236-2019).

Setting

The study was undertaken in Ontario, Canada, with over 13 million people and a landmass of over one million square kilometers. A significant part of the population does not have immediate access to a tertiary care center, relying on a provincial critical care transport organization to provide transport for these patients.⁷ Furthermore, some Ontario residents live in remote communities whose only point of access to the healthcare system is a nursing station. Nursing stations have limited resources, often without access to diagnostic imaging or in-house blood testing. Most do not have a physician on site.⁸

Ornge is the sole provider of critical care transport (both land and air) in Ontario. Ornge operates the largest air ambulance fleet in Canada. They have nine bases that operate rotor or fixed-wing aircraft. A transport medicine physician triages transfer requests and provides online medical oversight to transporting paramedics.

Study population

This study included all emergent interfacility transfers for injured patients aged 18 years or older transported by Ornge between 1 June 2016 and 31 December 2018. Exclusion criteria were patients who had been admitted to hospital for greater than 24 h, were less than 18 years old, were transferred directly from scene, or had a non-emergent reason for transfer. Patients were excluded if they had been admitted to the hospital for greater than 24 h to decrease the heterogeneity of the population of patients and the times to decision to transfer, focusing on those who were transferred based on their presentation in the emergency department.

Data sources

All patient records were identified in the Ornge electronic patient care records (ePCR). This database includes all patients transferred by Ornge. The ePCR includes patient, environmental, and sending hospital characteristics on each transfer including time stamps at various time points in the transfer. All data gathered were garnered from the ePCR.

Outcome variable

The outcome variable was the time interval from patient presentation to initial call to facilitate transfer. This was measured from the time the patient arrived at the sending facility to the time of request to transfer the patient. This is referred to as the “decision to transfer” time.

Exposure variables

The primary objective of our study was to assess the impact of patient, environmental, and institutional characteristics on the decision to transfer time using quantile regression. The patient variables explored included age, sex, initial vital signs and Glasgow coma scale (GCS) by the transporting paramedics, ventilator dependence at time of transfer request, vasopressor dependence at time of transfer request, and mechanism of injury. The environmental variables explored included time of day and season when the transfer is requested. The institutional variable explored was the classification of the sending facility, academic, community with greater than 100 beds, community with less than 100 beds, or nursing station.

Data analysis

Descriptive statistics were used to characterize the population for all variables of interest. Continuous variables were all found to be non-normally distributed and summarized as medians and interquartile ranges.

Commonly used regression models for determining the association of variables with an outcome, such as ordinary least squares (OLS) or linear regression, assess how the mean of a conditional distribution varies with changes in system or patient characteristics. One major limitation to this type of analysis is that linear regression techniques assume the outcome’s variable is normally distributed. Quantile regression can overcome that assumption of normality.⁹ Our outcome, decision to transfer time, is not normally distributed but highly skewed, with the majority of time intervals being shorter. Furthermore, the times we were most interested in characterizing or reducing are longer time intervals, representing delays, which are at the tail end of this distribution. Quantile regression allowed us to break up the data into quantiles and to perform a regression at each quantile. This methodology has previously been used with success to assess interfacility transfer time intervals.^9,10

All exposure variables were considered for inclusion of each model. Variable selection was determined using stepwise selection with significance levels to enter and exit the model set at 0.1. Patients with any missing data for one or more of the variables of interest were excluded from the final model. Missing data resulted in exclusion of less than 1% of observations.

Quantile regression models at the 10^th, 30^th, 50^th, 70^th, and 90^th percentiles were used to describe the effect of patient, environmental, and institutional characteristics on the time to decision to transfer at each centile. p-values less than .05 were considered statistically significant for all analyses. All variables were assessed for multicollinearity using a variation inflation factor (VIF) of 4 as the cut-off for exclusion.

All statistical analyses were conducted using SAS Studio version 3.4 (SAS Institute, North Carolina, USA).

Results

There were a total of 16,797 patients transported during the study period. After excluding patients transferred that were not injured and aged less than 18 years or patients with missing data, our final study population was 1128 patients (Figure 2). The median age of patients was 49 years (IQR 30–63) and 71.2% were male (Table 1).

Figure 2.

Study flow chart.

Table 1.

General patient, environmental, and institutional demographics.

Patient characteristics	Total cohort (n = 1128)
Age, median (IQR)	49 (30–63)
Age in years grouped, n (%)
18–35	380 (33.69)
36–45	138 (12.23)
46–55	171 (15.16)
56–65	200 (17.73)
66–75	131 (11.61)
Greater than 75	108 (9.58)
Sex, n (%)
Male	803 (71.19)
Female	325 (28.81)
Mechanism of injury, n (%)
Other	394 (34.93)
Motor vehicle collision	433 (38.39)
Fall	249 (22.07)
Penetrating wound	52 (4.61)
Heart rate, n (%)
50–100	849 (75.27)
>100	269 (23.84)
<50	10 (0.88)
Systolic blood pressure, n (%)
90–180	1065 (94.41)
>180	28 (2.48)
<90	35 (3.11)
GCS, n (%)
13–15	905 (80.23)
9–12	31 (2.75)
3–8	192 (17.02)
Respiratory rate, n (%)
10–30	1079 (95.66)
>30	47 (4.17)
<10	2 (0.17)
Intubated, n (%)
No	1112 (98.58)
Yes	16 (1.42)
Vasopressor dependence, n (%)
No	1076 (95.39)
Yes	52 (4.61)
Blood products administered, n (%)
No	1026 (90.96)
Yes	102 (9.04)
Time of day, n (%)
0800–1659	546 (48.40)
1700–2359	372 (32.98)
2400–0759	210 (18.62)
Season, n (%)
Winter	156 (13.83)
Spring	250 (22.16)
Summer	447 (39.63)
Fall	275 (24.38)
Sending facility classification, n (%)
Academic center	10 (0.89)
Community hospital >100 beds	502 (44.50)
Community hospital <100 beds	532 (47.16)
Nursing station	84 (7.45)

The median decision to transfer time was 2.42 h (IQR 1.23–3.86). The median total transport time, measured from the time of decision to transfer until care was transferred to the receiving hospital, was 3.12 h (IQR 2.35–4.31).

The variables ultimately included in the quantile regression model by use of stepwise selection were age, mechanism of injury, GCS, respiratory rate, time of day, season, and type of sending site.

The results of the quantile regression models are found in Table 2. The following variables were associated with an increase in time to decision to transfer at the 90^th percentile of time: age >75 (+2.47 h), age 66–75 (+3.70 h), age 56–65 (+1.20 h), transfer between 00:00–07:59 (+2.08 h), and transfer in the summer (+2.25 h). The following variables were associated with a decrease in time to decision to transfer at the 90^th centile of time: GCS 3–8 (−2.21 h), respiratory rate >30 (−2.01 h), sending site being a community hospital with <100 beds (−4.11 h), or the sending site being a nursing station (−5.66 h).

Table 2.

Results of quantile regression model for variables of interest in time to decision to transfer.

Quantile
Variable	10th	30th	50th	70th	90th
Age
18–35	REF	REF	REF	REF	REF
36–45	0.15	0.10	0.22	0.10	0.41
46–55	0.20	−0.05	0.26	0.12	0.88
56–65	0.16	0.01	0.34	0.28	1.20
66–75	0.28	0.73	0.87	0.52	3.70
Greater than 75	0.23	0.41	0.88	1.28	2.47
Mechanism of injury
Motor vehicle collision	−0.05	0.00	0.01	−0.15	−0.46
Fall	0.19	0.28	0.54	0.65	0.61
Penetrating	−0.09	−0.21	−0.22	−0.40	−1.09
Other	REF	REF	REF	REF	REF
GCS
3–8	−0.33	−0.90	−1.29	−1.38	−2.21
9–12	−0.31	−0.52	−0.94	−0.87	−1.57
13–15	REF	REF	REF	REF	REF
Respiratory rate
>30	0.02	−0.22	−0.52	−0.95	−2.01
<10	1.58	0.57	2.05	4.98	−0.64
10–30	REF	REF	REF	REF	REF
Season
Winter	−0.07	−0.02	0.00	−0.25	−0.15
Spring	−0.15	0.01	0.05	−0.03	0.78
Summer	0.07	0.20	0.52	0.38	2.25
Fall	REF	REF	REF	REF	REF
Time of day
0000–0759	0.17	0.21	0.31	0.90	2.08
1700–2359	−0.10	−0.12	−0.14	−0.13	0.25
0800–1659	REF	REF	REF	REF	REF
Sending facility classification
Community hospital >100 beds	−2.24	−2.30	−3.27	−3.88	−3.96
Community hospital <100 beds	−2.22	−2.41	−3.40	−3.83	−4.11
Nursing station	−2.26	−2.16	−3.43	−4.58	−5.66
Academic center	REF	REF	REF	REF	REF

Coefficient estimates are reported as change to time interval in hours. REF: reference. Bolded estimates represent p-value < .05.

Discussion

The results illustrate three important findings in the decision to transfer injured patients; first, the decision to transfer time interval represents a significant proportion of the patient’s medical journey; second, elderly patients are at risk of a delay in decision to transfer; and third, that environmental variables may play a role in delays in decision to transfer.

The median time to decision to transfer was 2.42 h. Given that the median transport time was 3.12 h, the decision to transfer time represents a sizeable portion of the patients overall journey to definitive care. Unfortunately, 2.42 h falls short of the Ontario regional trauma center referral guidelines which recommend decision to transfer be made within the first hour. Previous studies on trauma patients in Ontario demonstrate a median emergency department length of stay of 3.4 h, comparable to this study considering this interval includes time for pickup of the patient.¹¹ The degree to which our results and previous results fall below the standard in provincial guidelines emphasizes the importance of this study and future research.

The patient variable associated with a significant delay in decision to transfer at the 90^th centile was older age. The under triage of geriatric trauma patients has been demonstrated numerous times.^12–14 Reasons for under triage are likely multifactorial, including comorbidities, medications blunting physiologic response, and frailty. Standard trauma scoring systems and triage guidelines may not be sensitive enough for this population. There has been advocacy for the creation of geriatric trauma triage guidelines which include a more inclusive list of criteria for transfer of geriatric trauma patients; however, this has not been implemented in our trauma system.^15,16 These geriatric specific trauma triage guidelines have been associated with a decreased emergency department (ED) length of stay.¹⁷

Being transferred overnight and in the summer was also associated with delay in decision to transfer in the 90^th centile. Merali at al.¹⁸ found that there was a significantly higher number of transfers for head injured patients outside of normal business hours and in the summer, consistent with our findings. In Ontario, there is seasonality to trauma volume with the summer having significantly higher volumes than other times of year which may lead to delays in workup. Many of these EDs are in “cottage country” areas which see an influx of visitors and ED visits in the summer months. With regard to delays in decision to transfer overnight, smallest EDs have single physician coverage overnight, meaning that there may be a delay in physician assessment or reassessment overnight. Additionally, previous qualitative research on decision to transfer found that delays may be related to hesitancy to call specialist colleagues overnight which would delay the decision to transfer.¹⁹

This study is limited in its retrospective nature and the use of a critical care transport database as its sole source of information. There were limited data on injuries, number of systems involved, and injury severity scale which could contribute to the decision to transfer time. There was also limited access to in-hospital data for patients after they had been transferred; important outcomes of these delays, such as effect on in-hospital mortality, could not be assessed. Future studies are needed to characterize if morbidity and mortality are impacted as a result of the delay in transfer. There may also be opportunity for educational initiatives and quality improvement research as similar initiatives have been successful previously in reducing decision to transfer time.²⁰

Conclusion

In conclusion, this study demonstrated that the decision to transfer time represents a significant time component in patients receiving definitive care. We found that older patients and patients transferred overnight or in the summer were associated with delays in decision to transfer. This study can inform continuing collaboration between transport organizations and hospital systems to enforce referral protocols.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

This study was approved by the Research Ethics Board at Sunnybrook Health Sciences Centre (REB ID: 236–2019).

Informed consent

Informed consent was not sought for the present study because of the retrospective nature of the study and the significant number of patients in the study making it not feasible to obtain informed consent from each. The research is minimal risk to those charts reviewed and all data are to be presented in aggregate with no identifying features.

Guarantor

BN.

Contributorship

VM contributed to the study by being responsible for the literature search, study design, data interpretation, and writing. BN contributed to the study by being responsible for the literature search, study design, data analysis, and critical revision.

ORCID iDs

Victoria Myers

Brodie Nolan

References

Demetriades

Martin

Salim

, et al. Relationship between American college of surgeons trauma center designation and mortality in patients with severe trauma (injury severity score > 15). J Am Coll Surgeons 2006; 202: 212–215.

Haas

Stukel

Gomez

, et al. The mortality benefit of direct trauma center transport in a regional trauma system. J Trauma Acute Care Surg 2012; 72: 1510–1517.

Garwe

Cowan

Neas

, et al. Survival benefit of transfer to tertiary trauma centers for major trauma patients initially presenting to nontertiary trauma centers. Acad Emerg Med 2010; 17: 1223–1232.

Quirion

Ahghari

Nolan

. Factors associated with non-optimal resource utilization of air ambulance for interfacility transfer of injured patients. CJEM 2020; 22: S45–S54.

Wong

McParland

Nolan

. Identifying causes of delay in interfacility transfer of patients by air ambulance. CJEM 2020; 22: S30–S37.

Nolan

Haas

Tien

, et al. Causes of delay during interfacility transports of injured patients transported by air ambulance. Prehosp Emerg Care 2020; 24: 625–633.

Gomez

Berube

Xiong

, et al. Identifying targets for potential interventions to reduce rural trauma deaths: a population-based analysis. J Trauma Inj Infect Crit Care 2010; 69: 633–639.

Health Quality Ontario . Health in the North. Toronto, ON: Health Quality Ontario, 2017, www.hqontario.ca.

Austin

Schull

. Quantile regression: a statistical tool for out-of-hospital research. Acad Emerg Med 2003; 10: 789–797.

10.

Nolan

Haas

Tien

, et al. Patient, paramedic and institutional factors associated with delays in interfacility transport of injured patients by air ambulance. Prehosp Emerg Care 2020; 24: 793–799.

11.

Gomez

Haas

de Mestral

, et al. Institutional and provider factors impeding access to trauma center care. J Trauma Acute Care Surg 2012; 73: 1288–1293.

12.

Alshibani

Alharbi

Conroy

. Under-triage of older trauma patients in prehospital care: a systematic review. Eur Geriatr Med 2021. DOI: 10.1007/s41999-021-00512-5. Epub ahead of print 2021.

13.

Lehmann

Beekley

Casey

, et al. The impact of advanced age on trauma triage decisions and outcomes: a statewide analysis. Am J Surg 2009; 197: 571–575.

14.

Amoako

Evans

Brown

, et al. Identifying predictors of undertriage in injured older adults after implementation of statewide geriatric trauma triage criteria. Acad Emerg Med 2019; 26: 648–656.

15.

Ichwan

Darbha

Shah

, et al. Geriatric-specific triage criteria are more sensitive than standard adult criteria in identifying need for trauma center care in injured older adults. Ann Emerg Med 2015; 65: 92–100.

16.

Caterino

Brown

Hamilton

, et al. Effect of geriatric-specific trauma triage criteria on outcomes in injured older adults: a statewide retrospective cohort study. J Am Geriatr Soc 2016; 64: 1944–1951.

17.

Hammer

Storey

Bell

, et al. Improving geriatric trauma outcomes. J Trauma Acute Care Surg 2016; 81: 162–167.

18.

Merali

Sharma

MacDonald

, et al. Emergent and urgent transfers to neurosurgical centers in Ontario. Prehosp Emerg Care 2016; 20: 245–253.

19.

Gagliardi

Nathens

. Exploring the characteristics of high-performing hospitals that influence trauma triage and transfer. J Trauma Acute Care Surg 2015; 78: 300–305.

20.

Dennis

Vella

Gunter

, et al. Rural trauma team development course decreases time to transfer for trauma patients. J Trauma Acute Care Surg 2016; 81: 632–637.

Characteristics associated with delays in decision to transfer injured patients

Abstract

Introduction

Methods

Results

Conclusion

Keywords

Introduction

Methods

Setting

Study population

Data sources

Outcome variable

Exposure variables

Data analysis

Results

Discussion

Conclusion

Footnotes

Declaration of conflicting interests

Funding

Ethical approval

Informed consent

Guarantor

Contributorship

ORCID iDs

References