Clinical trial NCT06666647: Response Times in Danish Emergency Medical Services
Official title: Ambulance and Helicopter Response Times in Danish Emergency Medical Services: Protocol for an Epidemiological Study. the AHRTEMIS Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Emergency Medical Services
Response
Triage
Survival Outcomes
Intensive Care Unit Days
Intensive Care Patients Invasively Ventilated
Keywords: emergency medical services
response time
intensive care unit treatment
ventilator days
Lead sponsor: Odense University Hospital
Sponsor class: OTHER
Collaborators: name: University of Southern Denmark
First posted: 2024-10-30
Enrollment: 2500000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Every patient in Denmark in need of ambulance and/or helicopter services treatment from 2016 to 2022 with one year follow-up

Brief summary: The overall aim of this retrospective observational study is to investigate the association of emergency medical services response time with patient survival and treatment outcomes.

The main question it aims to answer is:

What is the association between response time and patient survival?

The investigators will collect data for all patients who were treated by ambulance and/or helicopter services in Denmark and follow the patient's path from illness or injury to discharge from hospital with a focus on the significance of ambulance and helicopter response time.

Detailed description: INTRODUCTION Background and rationale Response time, defined as the time from dispatch to arrival of the vehicle at the patient´s address for emergency medical services (EMS) units, may be essential in critical situations such as e.g., cardiac arrest and serious injury. Apart from these conditions, there is currently only limited or even contradictory evidence to support that a short response time is associated with improved patient survival and other treatment parameters. There are other significant factors that affect mortality in the acute phase, such as competence on prehospital care provider level, emergency department patient load and staffing shortages, that have been shown to affect mortality. Confounders such as bidirectional causality and confounding by indication may hinder scientific interpretation of response time importance and significance for patient outcomes in observational studies.

Importance of the study The results of the research project may contribute to establishing a scientific background for when response time matters for the patient and when it does not matter. Thereby, the investigators expect that the results can enable an evidence-based differentiation of ambulance response and degree of urgency and purvey the use of alternate key performance indicators and benchmarking in EMS systems.

Aims of the study This project aims to increase the knowledge about the possible association between response time and survival and treatment outcomes.

The overall aim is to investigate the association of response time with treatment outcomes, including patient survival.

Research questions in the study

1. Is there an association between ambulance/helicopter response time and patient survival?
2. Is there an association between ambulance/helicopter response time and intensive care treatment characteristics?

METHODS Ethics committee and other approvals required Approvals for the study were obtained from the Regional Secretariat and Legal Matters, Region Southern Denmark, Journal no. 23/30574 and from the Executive Secretariat, Odense University Hospital, Journal number 23/31007. From the Danish Clinical Quality Program - National Clinical Registries, the investigators obtained permissions as well (DAH-2024-02-16 and DID-2024-02-16). The investigators will store data in accordance with current Danish Data Protection Agency regulations via a license agreement with OPEN, Odense Patient data Exploratory Network.

Patient consent According to the Act on Processing of Personal Data, register-based studies approved by the Danish Patient Safety Authorities do not require consent to use data already entered into the registry.

Setting Danish EMS is free of charge for everyone. It is a three-tiered system, comprising emergency medical technician ambulances, paramedic ambulances and anesthesiologist-staffed mobile emergency care units. Further, a nationwide anesthesiologist-staffed helicopter EMS can be dispatched by all five health care regions. In total, approximately 300 ambulances, twenty-six mobile emergency care units and four helicopters are available in Denmark. The Danish EMS has been described in detail previously.

The Danish national distress number 1-1-2 provides one point of entry for citizens requiring emergency assistance from police, fire brigade or EMS. The 1-1-2 calls are received by three national command centers: two operated by the police and one by the Copenhagen fire brigade, relaying medical emergencies to the relevant health care regional Emergency Medical Dispatch Center. The dispatch center is responsible for the EMS response from receiving the call until the patient is handed over to a hospital or patient contact is finalized on scene or during the call. Each health region has its own dispatch center that operates prehospital units, using criteria-based dispatch.

Criteria-based dispatch In Denmark, criteria-based dispatch is the method of triaging in EMS. Based on the interview with the caller making the 1-1-2 call, the dispatch call-taker, usually a nurse or a paramedic, selects one of the 37 main symptom chapters with specific sub-chapters in the Danish Index support tool (See Table 1). Once entered into the computer-aided dispatch software, the criterion selected automatically triggers a response, which can either be an ambulance, a rapid response vehicle, a physician-manned unit, or a combination of those. Non-conveyance may also be the response plan. Every mission must have an assigned Danish Index criterion to be dispatched.

The prehospital personnel document and record all interventions and measurements in a wireless digital tablet named the Electronic Prehospital Patient Record System. Data are encrypted and stored on mainframe computers. When an ambulance crew is dispatched on a mission, all details such as address, time stamps, patient identification, vital parameters, medications used, and treatment details are collected during the patient contact. Data consistency is predominant as all EMS personnel are obliged to document their activities.

Data sources and collection The project will collect data from the Electronic Prehospital Patient Record System in the period from 1 January 2016 to 31 December 2022 on all patients in Denmark, who have been treated by ambulance and/or helicopter services, with response modes A (with lights and sirens) and B (without lights and sirens). Other forms of missions are not covered by the project. Further, data from Danish Intensive Care Database and Danish Database for Acute Hospital Contacts will be extracted as per the predefined data variable.

Data linkage The investigators will link data from Electronic Prehospital Patient Record with Danish Intensive Care Database and the Danish Database for Acute Hospital Contacts from 2016 to 2022 with one year follow-up. The purpose is to chart the patient path in the Danish healthcare system from illness or injury to discharge from hospital, death or e.g., emigration. Data will be linked via the patient's unique civil personal register number.

Data variables To answer the research questions in the planned study, the investigators will extract variables from the databases Electronic Prehospital Patient Record, Danish Intensive Care database and Danish database for Acute Hospital Contacts.

Guidelines The investigators will adhere to The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines in the reporting of the study findings.

Missing data Missing data will be accounted for, and the investigators will use validated methods to mitigate the effects of missing data.

Measures Nominal results are expressed as medians with interquartile ranges or means with standard deviations. Odds and risk ratios are expressed with standard error and 95% confidence intervals.

Tables The investigators will produce tables to demonstrate the findings of the study in a tabular format, presenting all data of the included participants from the database searches.

Figures The investigators will construct a study flow-chart to illustrate the pathway of the study. Further, the investigators will develop a Directed Acyclic Graph to demonstrate the study pathway between exposure/treatment and outcome with confounders, expected study causalities and associations, and for a graphic overview of the study.

Analyses In the statistical analyses, the investigators will adjust for age, sex, acute illness, and chronic illness. Further, the investigators will use regression analysis, Kaplan-Meyer plots and linear regression models to assess and evaluate the association between response time and the primary and secondary outcomes. The investigators will use advanced statistical and epidemiological methods to assess confounders and biases and conduct a stratified analysis for each of the 37 dispatch categories.

In the analyses, the investigators will address causality, explained by way of the Bradford-Hill criteria, and the Sufficient-component cause model. The investigators will use Directed Acyclic Graphs to illustrate and identify measurable and unmeasurable confounders. The investigators will also apply causal mediation analysis as warranted, and address time-varying covariates and immortal time bias, if applicable.

Further, the investigators will use validated statistical methods to control for confounding including multivariable regression, stratification and inverse probability of treatment weighting and use this concept to mitigate and address time-dependent confounding.

Publications and dissemination The investigators plan to publish the results from the two studies in peer-reviewed scientific journals. Results will also be presented at relevant scientific congresses and meetings and on the project website, www.ahrtemis.dk.

In addition, the results will be presented in relevant publications, newspapers, digital media, television, social media, etc. Further, the AHRTEMIS project has planned a meeting in Q4 2024 with patients and relatives to formulate a layman's resume of the AHRTEMIS project synopsis and to define important aspects to EMS dispatch from the patient and relative perspective.

DISCUSSION In presenting the protocol for this substantive epidemiological assessment study, the investigators seek to pave the way for a comprehensive exploration of the association between EMS units' response time and patient survival and relevant outcomes in Denmark. The investigators will seek to scientifically quantify the importance and significance of response time association with patient outcomes, stratified as per selected Danish Index symptom-based criterion.

Through the adherence to validated guidelines such as the STROBE, the investigators aim to ensure consistent methodology and transparency in the reporting of the study findings. In the discussion of the findings, the investigators anticipate valuable insights to enhance clinical practice, guide future research in the field, and ultimately optimize the delivery of care for patient in need om emergency medical services treatment.

The use of advanced epidemiological and statistical methods will aide to mitigate major limitations that include confounding by indication and bidirectional causality, which are inherent in observational studies. The investigators will systematically address and quantify additional limitations, such as observation, classification, or measurement bias.

CONCLUSION In this epidemiological study, the investigators aim to describe the associations, if any, between EMS response time and patient survival and important outcome measures. The investigators will scientifically quantify the importance and significance of response time association with patient outcomes in a large material of Danish patients treated by ambulance and helicopter services from 2016-2022 with one year follow-up, using advanced epidemiological and statistical methodologies.

Table 1. Danish Index. Main symptom groups

Chapter Main symptoms

1. Unconscious adult
2. Unconscious child
3. Foreign body in airway
4. Disaster, major incident
5. Ordered task
6. Unclear problem
7. Allergic reaction
8. Bleeding, non-traumatic
9. Fire or electrical injury
10. Chest pain, heart disease
11. Diabetes
12. Drowning
13. Diving accident
14. Animal or insect bite
15. Fever
16. Poisoning in children
17. Childbirth
18. Gynecology, pregnancy
19. Headache
20. Skin and rashes
21. Hypothermia, hyperthermia
22. Chemicals, gasses
23. Seizures
24. Abdominal or back pain
25. Possible death or sudden infant death
26. Impaired consciousness, paralysis
27. Psychiatry, suicidal
28. Breathing difficulties
29. Intoxication, medications, alcohol, drug overdose
30. Sick child
31. Minor wound, fracture, injury
32. Traffic accident
33. Accident, not related to traffic
34. Urinary tract
35. Violence, abuse
36. Ear, nose, throat
37. Eye

Eligibility criteria: Inclusion Criteria:

* Every patient in Denmark in need of ambulance and/or helicopter services treatment in Denmark from 2016-2022 with outgoing response mode A (with lights and sirens) and B (without lights and sirens) is eligible for inclusion in the study.

Exclusion Criteria:

* No civil personal registration number available
* Patient not identified
* Patient not entered in the prehospital electronic patient journal
* invalid data entered
* No linkage between substitue personal register number and actual personal register number for patients identified later;

Primary outcomes:
measure: 30-day survival
timeFrame: Status at 30 days after hospital admission
Secondary outcomes:
measure: 24 hour survival
timeFrame: Status at 24 hours after hospital admission
measure: 48 hour survival
timeFrame: Status at 48 hours after hospital admission
measure: 7-day survival
timeFrame: Status at seven days after hospital admission
measure: 90-day survival
timeFrame: Status at 90 days after hospital admission
measure: Survival to hospital discharge
timeFrame: Status at hospital discharge, assessed up to 5 days
Other outcomes:
measure: Length of hospital stay
timeFrame: From hospital admission to hospital discharge, up to one year after enrollment
measure: Days in ICU
timeFrame: From admission to intensive care unit until discharge from intensive care unit, up to one year after enrollment
measure: Ventilator days
timeFrame: From admission to intensive care unit until discharge from intensive care unit, up to one year after enrollment
measure: Vaspressors and inotropics
timeFrame: From admission to intensive care unit until discharge from intensive care unit, up to one year after enrollment
measure: Continuous renal replacement therapy
timeFrame: From admission to intensive care unit discharge until dischare from intensive care unit, up to one year after enrollment
measure: Simplified Acute Physiology Score
timeFrame: At 24 hours after admission to intensive care unit
measure: Modifed Rankin Scale
timeFrame: At hospital discharge (assessed up to 5 days)
measure: Days in hospital at 180 days
timeFrame: At 180 days after index date
measure: Readmission within two days of discharge
timeFrame: At 48 hours after hospital discharge
measure: Charlson Comorbidity Index
timeFrame: Within 24 hours of admission
measure: Clinical Frailty Scale
timeFrame: Within 24 hours of admission
measure: Death or emigration up to one year after the first contact date
timeFrame: At one year after index date
Locations:
city: Odense
country: Denmark
facility: Odense University Hospital
Study officials:
References:
citation: Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015 Dec 10;34(28):3661-79. doi: 10.1002/sim.6607. Epub 2015 Aug 3.
citation: Chesnaye NC, Stel VS, Tripepi G, Dekker FW, Fu EL, Zoccali C, Jager KJ. An introduction to inverse probability of treatment weighting in observational research. Clin Kidney J. 2021 Aug 26;15(1):14-20. doi: 10.1093/ckj/sfab158. eCollection 2022 Jan.
citation: Levesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ. 2010 Mar 12;340:b5087. doi: 10.1136/bmj.b5087. No abstract available.
citation: Hu X, Zhao J, Lin Z, Wang Y, Peng H, Zhao H, Wan X, Yang C. Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proc Natl Acad Sci U S A. 2022 Jul 12;119(28):e2106858119. doi: 10.1073/pnas.2106858119. Epub 2022 Jul 5.
citation: Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021 May;36(5):465-478. doi: 10.1007/s10654-021-00757-1. Epub 2021 May 7.
citation: VanderWeele TJ, Hernan MA. Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs. Am J Epidemiol. 2012 Jun 15;175(12):1303-10. doi: 10.1093/aje/kwr458. Epub 2012 May 8.
citation: Rijnhart JJM, Valente MJ, Smyth HL, MacKinnon DP. Statistical Mediation Analysis for Models with a Binary Mediator and a Binary Outcome: the Differences Between Causal and Traditional Mediation Analysis. Prev Sci. 2023 Apr;24(3):408-418. doi: 10.1007/s11121-021-01308-6. Epub 2021 Nov 16.
citation: Suzuki E, Yamamoto E. Strength in causality: discerning causal mechanisms in the sufficient cause model. Eur J Epidemiol. 2021 Sep;36(9):899-908. doi: 10.1007/s10654-021-00798-6. Epub 2021 Sep 26.
citation: Schober P, Vetter TR. Linear Regression in Medical Research. Anesth Analg. 2021 Jan;132(1):108-109. doi: 10.1213/ANE.0000000000005206. No abstract available.
citation: Hess AS, Hess JR. Kaplan-Meier survival curves. Transfusion. 2020 Apr;60(4):670-672. doi: 10.1111/trf.15725. Epub 2020 Feb 20. No abstract available.
citation: Hsu CH, Yu M. Cox regression analysis with missing covariates via nonparametric multiple imputation. Stat Methods Med Res. 2019 Jun;28(6):1676-1688. doi: 10.1177/0962280218772592. Epub 2018 May 2.
citation: Zhang Z, Reinikainen J, Adeleke KA, Pieterse ME, Groothuis-Oudshoorn CGM. Time-varying covariates and coefficients in Cox regression models. Ann Transl Med. 2018 Apr;6(7):121. doi: 10.21037/atm.2018.02.12.
citation: Broderick JP, Adeoye O, Elm J. Evolution of the Modified Rankin Scale and Its Use in Future Stroke Trials. Stroke. 2017 Jul;48(7):2007-2012. doi: 10.1161/STROKEAHA.117.017866. Epub 2017 Jun 16. No abstract available.
citation: Jahn M, Rekowski J, Gerken G, Kribben A, Canbay A, Katsounas A. The predictive performance of SAPS 2 and SAPS 3 in an intermediate care unit for internal medicine at a German university transplant center; A retrospective analysis. PLoS One. 2019 Sep 25;14(9):e0222164. doi: 10.1371/journal.pone.0222164. eCollection 2019.
citation: Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8.
citation: Ayilara OF, Zhang L, Sajobi TT, Sawatzky R, Bohm E, Lix LM. Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry. Health Qual Life Outcomes. 2019 Jun 20;17(1):106. doi: 10.1186/s12955-019-1181-2.
citation: Li J, Guo S, Ma R, He J, Zhang X, Rui D, Ding Y, Li Y, Jian L, Cheng J, Guo H. Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets. BMC Med Res Methodol. 2024 Feb 16;24(1):41. doi: 10.1186/s12874-024-02173-x.
citation: Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019 Apr;13(Suppl 1):S31-S34. doi: 10.4103/sja.SJA_543_18.
citation: Schmidt M, Pedersen L, Sorensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol. 2014 Aug;29(8):541-9. doi: 10.1007/s10654-014-9930-3. Epub 2014 Jun 26.
citation: Pedersen CB. The Danish Civil Registration System. Scand J Public Health. 2011 Jul;39(7 Suppl):22-5. doi: 10.1177/1403494810387965.
citation: Alstrup K, Petersen JAK, Barfod C, Knudsen L, Rognas L, Moller TP. The Danish helicopter emergency medical service database: high quality data with great potential. Scand J Trauma Resusc Emerg Med. 2019 Apr 5;27(1):38. doi: 10.1186/s13049-019-0615-5.
citation: Kjaer J, Milling L, Wittrock D, Nielsen LB, Mikkelsen S. The data quality and applicability of a Danish prehospital electronic health record: A mixed-methods study. PLoS One. 2023 Oct 26;18(10):e0293577. doi: 10.1371/journal.pone.0293577. eCollection 2023.
citation: Andersen MS, Johnsen SP, Sorensen JN, Jepsen SB, Hansen JB, Christensen EF. Implementing a nationwide criteria-based emergency medical dispatch system: a register-based follow-up study. Scand J Trauma Resusc Emerg Med. 2013 Jul 9;21:53. doi: 10.1186/1757-7241-21-53.
citation: Lindskou TA, Mikkelsen S, Christensen EF, Hansen PA, Jorgensen G, Hendriksen OM, Kirkegaard H, Berlac PA, Sovso MB. The Danish prehospital emergency healthcare system and research possibilities. Scand J Trauma Resusc Emerg Med. 2019 Nov 4;27(1):100. doi: 10.1186/s13049-019-0676-5.
citation: Frydenlund J, Mackenhauer J, Christensen EF, Christensen HC, Vaeggemose U, Steinmetz J, Johnsen SP. Socioeconomic Disparities in Prehospital Emergency Care in a Danish Tax-Financed Healthcare System: Nationwide Cohort Study. Clin Epidemiol. 2022 Apr 27;14:555-565. doi: 10.2147/CLEP.S358801. eCollection 2022.
citation: Hansen PM, Rehn M, Lassen AM, Perner A, et al. Ambulance and Helicopter Response Times in Danish Emergency Medical Services. Protocol for an Epidemiological Assessment Analysis. The AHRTEMIS study. Open Science Framework. doi: 10.17605/OSF.IO/SZG4W. Accessed 21 August 2024. https://osf.io/
citation: Frischknecht Christensen E, Berlac PA, Nielsen H, Christiansen CF. The Danish quality database for prehospital emergency medical services. Clin Epidemiol. 2016 Oct 25;8:667-671. doi: 10.2147/CLEP.S100919. eCollection 2016.
citation: Hansen PM, Nielsen MS, Rehn M, Lassen AT, Mikkelsen S, Perner A, Brochner AC. Ambulance and helicopter response time. Association with patient outcome and illness severity: Protocol of a systematic literature review and meta-analysis. Acta Anaesthesiol Scand. 2024 Feb;68(2):287-296. doi: 10.1111/aas.14339. Epub 2023 Oct 23.
citation: Evans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annu Rev Genomics Hum Genet. 2015;16:327-50. doi: 10.1146/annurev-genom-090314-050016. Epub 2015 Apr 22.
citation: Sekula P, Del Greco M F, Pattaro C, Kottgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016 Nov;27(11):3253-3265. doi: 10.1681/ASN.2016010098. Epub 2016 Aug 2.
citation: Kyriacou DN, Lewis RJ. Confounding by Indication in Clinical Research. JAMA. 2016 Nov 1;316(17):1818-1819. doi: 10.1001/jama.2016.16435. No abstract available.
citation: Murray B, Kue R. The Use of Emergency Lights and Sirens by Ambulances and Their Effect on Patient Outcomes and Public Safety: A Comprehensive Review of the Literature. Prehosp Disaster Med. 2017 Apr;32(2):209-216. doi: 10.1017/S1049023X16001503. Epub 2017 Jan 30.
citation: Blackwell TH, Kline JA, Willis JJ, Hicks GM. Lack of association between prehospital response times and patient outcomes. Prehosp Emerg Care. 2009 Oct-Dec;13(4):444-50. doi: 10.1080/10903120902935363.
citation: Weiss S, Fullerton L, Oglesbee S, Duerden B, Froman P. Does ambulance response time influence patient condition among patients with specific medical and trauma emergencies? South Med J. 2013 Mar;106(3):230-5. doi: 10.1097/SMJ.0b013e3182882c70.
citation: Blanchard IE, Doig CJ, Hagel BE, Anton AR, Zygun DA, Kortbeek JB, Powell DG, Williamson TS, Fick GH, Innes GD. Emergency medical services response time and mortality in an urban setting. Prehosp Emerg Care. 2012 Jan-Mar;16(1):142-51. doi: 10.3109/10903127.2011.614046. Epub 2011 Oct 25.
citation: Pons PT, Haukoos JS, Bludworth W, Cribley T, Pons KA, Markovchick VJ. Paramedic response time: does it affect patient survival? Acad Emerg Med. 2005 Jul;12(7):594-600. doi: 10.1197/j.aem.2005.02.013.
citation: Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med. 2002 Apr;9(4):288-95. doi: 10.1111/j.1553-2712.2002.tb01321.x.
citation: Carr BG, Caplan JM, Pryor JP, Branas CC. A meta-analysis of prehospital care times for trauma. Prehosp Emerg Care. 2006 Apr-Jun;10(2):198-206. doi: 10.1080/10903120500541324.
citation: Eisenberg MS, Bergner L, Hallstrom A. Cardiac resuscitation in the community. Importance of rapid provision and implications for program planning. JAMA. 1979 May 4;241(18):1905-7. doi: 10.1001/jama.241.18.1905.