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| Name | Class |
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| University of Southern Denmark | OTHER |
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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.
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
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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients in need of treatment by emergency medical services | All patients in Denmark to whom an ambulance or helicopter has been disapatched |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Treatment by ambulance or helicopter services with response times | Procedure | Prehospital treatment by ambulance and/or helicopter services personnel of patients in acute conditions |
| Measure | Description | Time Frame |
|---|---|---|
| 30-day survival | Patient is alive 30 days after hospital admission | Status at 30 days after hospital admission |
| Measure | Description | Time Frame |
|---|---|---|
| 24 hour survival | The patient is alive 24 hours after hospital admission | Status at 24 hours after hospital admission |
| 48 hour survival | The patient is alive 48 hours after hospital admission |
| Measure | Description | Time Frame |
|---|---|---|
| Length of hospital stay | Days spent in the hospital (integer) | From hospital admission to hospital discharge, up to one year after enrollment |
| Days in ICU | Days spent in the intensive care unit (integer) |
Inclusion Criteria:
Exclusion Criteria:
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Every patient in Denmark in need of ambulance and/or helicopter services treatment from 2016 to 2022 with one year follow-up
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Odense University Hospital | Odense | 5000 | Denmark |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26238958 | Background | 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. | |
| 35035932 | Background | 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. |
| Label | URL |
|---|---|
| Website of the AHRTEMIS project | View source |
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There is no sharing of IPD as this is a retrospective cohort study without disclosure of individual patient details
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| ID | Term |
|---|---|
| D011930 | Reaction Time |
| ID | Term |
|---|---|
| D011580 | Psychological Techniques |
| D008919 | Investigative Techniques |
| D004191 | Behavioral Disciplines and Activities |
| D009424 | Nervous System Physiological Phenomena |
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| Status at 48 hours after hospital admission |
| 7-day survival | The patient is alive seven days after hospital admission | Status at seven days after hospital admission |
| 90-day survival | The patient is alive 90 days after hospital admission | Status at 90 days after hospital admission |
| Survival to hospital discharge | Patient is alive on hospital discharge | Status at hospital discharge, assessed up to 5 days |
| From admission to intensive care unit until discharge from intensive care unit, up to one year after enrollment |
| Ventilator days | Days on mechanical ventilation (integer) | From admission to intensive care unit until discharge from intensive care unit, up to one year after enrollment |
| Vaspressors and inotropics | The use of vasopressors and/or inotropics while in the intensive care unit (yes/no) | From admission to intensive care unit until discharge from intensive care unit, up to one year after enrollment |
| Continuous renal replacement therapy | The use of continuous renal replacement therapy while in the intensive care unit (yes/no) | From admission to intensive care unit discharge until dischare from intensive care unit, up to one year after enrollment |
| Simplified Acute Physiology Score | Simplified Acute Physiology Score (SAPS) is an adult physiology score based upon 17 variables derived from the APACHE score and collected within the first 24 hours of intensive care. The measurement comprises an integer point score between 0 and 163 and a predicted mortality between 0% and 100% | At 24 hours after admission to intensive care unit |
| Modifed Rankin Scale | Modified Rankin Scale at discharge from hospital. The Modified Rankin Score (mRS) is a 6 point disability scale with possible scores ranging from 0 to 5. 6 is usually added for patients who died during the hospital stay. 0 is the best possible score, 5 is for severe disability. | At hospital discharge (assessed up to 5 days) |
| Days in hospital at 180 days | Number of days spent in hospital 180 days post index date | At 180 days after index date |
| Readmission within two days of discharge | Readmission with two days of discarge from hopsital | At 48 hours after hospital discharge |
| Charlson Comorbidity Index | The Charlson Comorbidity Index predicts the ten-year mortality for a patient who may have a range of comorbid conditions. Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient. A score of zero indicates that no comorbidities were found. | Within 24 hours of admission |
| Clinical Frailty Scale | The clinical frailty scale is a 9-point scale that quantifies frailty based on function in individual patients. A scale score of 1 indicates a very fit person, whereas a score of 9 indicates a terminally ill patient (integer). | Within 24 hours of admission |
| Death or emigration up to one year after the first contact date | Death for unspecified reasons or emigration from the country up to one year after the first contact date (yes/no) | At one year after index date |
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| D055687 | Musculoskeletal and Neural Physiological Phenomena |