Short-term outcome and differences between rural and urban trauma patients treated by mobile intensive care units in Northern Finland: a retrospective analysis

The primary study area was the five northernmost hospital districts in Northern Finland, which had 741 135 inhabitants in 2012, covering approximately 50 % of the surface area of Finland. EMS consists of first responders and basic and advanced level ambulances. HEMS are provided by nation-wide FinnHEMS. In Northern Finland, a physician-staffed unit (FinnHEMS 50) operates out of Oulu University Hospital and a paramedic-staffed unit (FinnHEMS 51) operates out of Rovaniemi airport. A total of 464 000 inhabitants can be reached within 30 min and a helicopter or rapid response car is used, depending on operational factors. Annually, FinnHEMS 50 and 51 units, together, care for 800–1000 patients. Four central hospitals and one university hospital are located in the area.

The FinnHEMS is dispatched criteria-based for all life-threatening situations and high-energy accidents simultaneously with ground ambulances, from an emergency communication center [18]. Specially trained paramedics in FinnHEMS 51 may intubate an unconscious patient using ketamine and sedatives with strict systemic operative procedures, but online consultation with a pre-hospital anesthesiologist is always required. Inter-hospital transfers of critically ill patients are performed by ground ambulances and, if needed, supplemented by hospital staff.

All patients treated by FinnHEMS and recorded as trauma patients (blunt or penetrating) in the electronic HEMS database from 1 January 2012 to 31 December 2013 were identified. Pre-hospital deaths (cancelled missions because of pre-hospital death or those declared dead by FinnHEMS) were also retrieved but not included in the analysis because patients’ identification is not routinely recorded in the database if the mission is aborted. Pre-hospital data (time intervals, pre-hospital therapy, transport method, escorted by FinnHEMS, the use of a helicopter) were retrieved from the HEMS database and coupled to hospital data using personal identification numbers. The hospital data (key emergency therapy defined by Utstein style reporting for major trauma [19], location where the patient was discharged, and Pre-Injury American Society of Anesthesiologists Physical Status [ASA-PS] classification system) were retrieved from the medical records. Data on intensive care (Sepsis-Related Organ Failure Score at admission and the maximum score; the Acute Physiology and Chronic Health Evaluation-II score; Simplified Acute Physiology Score; Therapeutic Intervention Scoring System score; and length of respiratory therapy) were obtained from the databases of the intensive care clinical information systems of each hospital. The Injury Severity Score (ISS) of the patients who survived to hospital was calculated by the main researcher (LR), who is certified in the use of the Abbreviated Injury Score [20]. Thirty-day mortality data was retrieved from the Causes of Death Registry maintained by Statistics Finland.

The road and straight-line (Euclidean) distances from the site of injury to the helicopter base and referral hospital were calculated using ArcGIS 10.2 software (ESRI, Redlands, CA). The statistical grouping of municipalities by Statistics Finland was used to identify urban and rural patients [21]. This classification groups municipalities into three categories (urban, semi-urban, and rural) according to the proportion of inhabitants living in urban settlements and the population of the largest settlement. In this study, the municipalities were divided into two categories, urban and rural, with the latter including semi-urban municipalities.

Response time was defined as the time from dispatch to arrival on scene. Transport time was defined as the time between the start of transport and the patient’s arrival at the hospital. On-scene time was defined as the time between FinnHEMS arrival on scene and the start of transport. Injury was defined as severe if the ISS score was >15.

Data are expressed as medians with 25^th–75^th percentiles and P <0.05 was considered significant. The Mann–Whitney test was used to compare continuous data between the groups, and Pearson’s chi-square test was used to compare categorical variables. A multivariate logistic regression model was built using a maximum of two adjusting covariates at a time to assess the impact of the type of municipality on 30-day mortality. The number of adjusting covariates was based on a relatively low number of trauma deaths. The adjusting covariates used were age, gender, ISS score, the type of HEMS unit, airway distance to the site of injury and the following HEMS time intervals: response, on-scene and transport times. Age and ISS score were categorized, since the linearity assumption did not hold. Only the severely injured patients were included in the analysis because no deaths occurred when the ISS score was less than sixteen. The results of the model with the lowest log-likelihood function are presented.

Data were analyzed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, NY). We did not perform a power calculation because of the retrospective nature of the study and because the HEMS database had data only from the past two years (2012 and 2013).