Lead Times in the Early Management of Traumatic Brain Injury: Relation to Geographic Conditions and Clinical Outcomes in a Nationwide Swedish Registry Study

Lead Times in the Early Management of Traumatic Brain Injury: Relation to Geographic Conditions and Clinical Outcomes in a Nationwide Swedish Registry Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lead Times in the Early Management of Traumatic Brain Injury: Relation to Geographic Conditions and Clinical Outcomes in a Nationwide Swedish Registry Study Amanda Gu, Francisco Leal Méndez, Anders Lewén, Anders Hånell, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8065305/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Acta Neurochirurgica → Version 1 posted 10 You are reading this latest preprint version Abstract Background Traumatic brain injury (TBI) patients are at risk of sudden deterioration, requiring timely diagnostics and treatment to prevent secondary cerebral injuries. This study investigated lead times in prehospital and early intrahospital TBI management, assessing their association with geographical conditions, hospital caseloads, and patient outcomes. Methods This nationwide, observational cohort study included 5036 TBI patients (during 2018–2022) from the Swedish Trauma Registry (SweTrau). Lead times from trauma to alarm, from alarm to hospital arrival, and times to first computed tomography (CT) from alarm and hospital arrival, respectively, were calculated. These were analyzed against the geographical distribution of healthcare, hospital caseloads, and 30-day mortality. Results The majority of the cohort arrived in hospital within one hour and suffered a mild-to-moderate TBI. In univariate analyses, healthcare regions with larger geographical catchment areas exhibited longer time of prehospital management from alarm to arrival in hospital than smaller regions. Meanwhile, in multivariate linear regressions, larger region catchment area was independently associated with longer times from trauma to alarm and from alarm to hospital, but shorter time from alarm to first CT. In similar multivariate analyses, higher caseload was associated with longer time from alarm to first CT. Patients who were initially managed in a local hospital exhibited longer lead times overall, except from time to first CT from arrival in hospital. Furthermore, in the whole cohort, longer time from alarm to first CT and from arrival in hospital to first CT were associated with lower rate of mortality in univariate logistic regressions. However, this did not hold true in multivariate analysis after adjusting for demography and injury severity. Conclusions Lead times in TBI management varied by both geographical and hospital-bound factors. Faster lead times in TBI were associated with higher mortality in univariate analysis, but this association disappeared in multivariate analysis, suggesting that clinical severity rather than time alone is the stronger predictor of outcome. Nonetheless, it remains evident that efficient and qualitative management is a fundamental necessity for better outcomes in TBI management. Lead time outcome Swedish Trauma Registry traumatic brain injury trauma logistics Figures Figure 1 Introduction Traumatic brain injury (TBI) affects approximately 70 million people annually and is a leading cause of mortality and morbidity worldwide [ 1 – 3 ]. Mild-to-moderate TBI patients typically present with symptoms such as headache, nausea, and amnesia. In some cases, there is a rapid clinical deterioration with loss of consciousness, unreactive pupils, and airway compromise, due to intracranial bleedings and elevated intracranial pressure (ICP) [ 4 ]. In addition, a subset of severe TBI cases present immediately in an unconscious state [ 5 – 8 ]. Stable patients with mild TBI can typically be discharged from the emergency department or admitted for brief neurological monitoring without requiring further treatment [ 7 , 9 – 11 ]. Patients with severe injuries or early deterioration are at high risk of mortality and need prompt physiological and neurosurgical management, primarily due to airway obstruction and ventilation failure caused by brain herniation following expanding intracranial hemorrhages [ 12 – 15 ]. Additionally, patients with TBI are particularly vulnerable to secondary brain injuries resulting from hypoxia and hypotension, which may be aggravated by multi-trauma [ 5 , 16 ]. Effective management of TBI requires immediate resuscitation, early diagnostics and careful stratification to distinguish patients requiring emergent neurosurgery from those with mild injuries that can be managed conservatively. This necessitates efficient prehospital and in-hospital care pathways, which minimize delays from trauma to alarm activation, hospital arrival, imaging (trauma CT) and treatments. Prehospital care is particularly influenced by geographical variations in different hospital catchment areas, which may impact the time from injury to hospital arrival. Prehospitally, the need for immediate resuscitation must be weighed against the urgency of timely transport to a hospital capable of appropriate diagnostic imaging and advanced care [ 17 – 19 ]. The "Platinum Ten" principle recommends limiting on-site care to 10 minutes to avoid unnecessary delays while allowing sufficient time for initial stabilization [ 20 , 21 ]. However, as shown in the multicenter CENTER-TBI study, prehospital practices vary considerably across Europe [ 19 ]. Subsequently, upon contact with the receiving hospital, a trauma alarm may be triggered to initiate immediate care, following local or national guidelines [ 22 ]. Evaluation and resuscitation of trauma patients follow the Advanced Trauma Life Support (ATLS) protocol prioritizing airway (A), breathing (B), circulation (C), neurological disability (D), and exposure (E) [ 10 ]. Hemodynamically unstable patients who do not respond to initial interventions may require immediate surgical interventions, while a trauma CT may be performed to evaluate potential injuries in stable patients [ 5 – 8 , 23 ]. For patients with isolated head trauma, protocols typically mandate that trained personnel assess the patient within 15 minutes of arrival [ 9 ]. TBI patients with mass lesions are often transferred to neurosurgical centers for hematoma evacuation and neurointensive care [ 13 – 15 , 24 , 25 ]. However, variations in hospital experience and caseload, and the distance to the nearest neurosurgical center can differ and influence the time from injury to definitive treatment [ 19 , 26 ]. Sweden is a geographically large country with both densely populated urban areas and extensive sparsely populated regions, resulting in a substantial geographical variation in access to specialized neurosurgical care in terms of geographical distance and transportation time. The healthcare system is administratively divided into 20 individual health care regions, with a total of 49 local hospitals providing around the clock general trauma care, of which 7 university hospitals offer specialized neurosurgical care [ 1 , 17 , 27 , 28 ]. The common practice is that TBI patients are stabilized initially at local hospitals, followed by secondary transfer to a neurosurgical center if needed [ 27 , 29 ]. In regions maintaining a university hospital, patients may primarily be admitted to this specific neurosurgical department. Exceptionally, acute extracerebral hematomas may be evacuated in local hospitals as a life-saving procedure [ 27 ]. However, in geographically compact and densely populated regions, moderate to severe TBI patients are more often directly transported to hospitals with neurosurgical capabilities [ 27 , 29 ]. Ultimately, it is evident that the geographical distribution of healthcare resources is uneven and may affect the management of TBI patients. Many factors may influence the lapse of management of TBI patients, potentially delaying necessary diagnostics and treatments, and increasing risks of developing secondary brain injuries. There is limited evidence on how patient characteristics, geographical factors and hospital experience affect lead times in these early care pathways, and which impact delays have on clinical outcomes. Therefore, the aim of this study was to investigate variations in lead times, their explanatory variables and their effect on mortality in a Swedish nationwide cohort registry study. Materials and methods Study design and population This retrospective observational study utilized data from the Swedish Trauma Registry (SweTrau), a nation-wide registry with data from hospitals in Sweden that provide care for severe traumatic injuries. The study focused on patients with TBI diagnoses and New Injury Severity Score > 15 (S06.1–S06.6), aged 16 or older, treated between January 1, 2018, and December 31, 2022. The data extracted with these inclusion criteria covered 20 Swedish health care regions and 47 hospitals. From the 5914 patients with these diagnoses during this time period, 352 were excluded due to age below 16. Furthermore, duplicate registrations were identified by person identification number, temporary identification, age [years], date of birth, and gender. In total, 526 duplicate cases were excluded, resulting in a final cohort of 5036 individuals (Fig. 1 ). Data collection and variable definitions All data used in this study were acquired from the SweTrau registry [ 17 ]. Demographics (age, sex), injury mechanisms (falls, road traffic accidents etc.), injury severity (Glasgow Coma scale [GCS], abbreviated injury scale [AIS] head, injury severity score [ISS]), injury types (epidural hematoma [EDH], acute subdural hematoma [ASDH], traumatic subarachnoid hemorrhage [tSAH], contusion), interventions (craniotomy) and outcome (30-day mortality) were extracted from the registry. Registered date and time of trauma, alarm, primary hospital arrival and first CT, respectively, were also extracted. The accuracy of these time variables in SweTrau has previously been reported to be 74% or higher when allowing a margin of error up to 10 minutes, with a correctness of 89.7% of the registry data overall, and a case completeness of 100% for individuals with NISS > 15 [ 17 ]. The time intervals were calculated from trauma to alarm and from alarm to arrival in the primary hospital (local hospital or university hospital), which were considered indicators of access to care. Also, time to first CT from alarm and arrival in hospital was calculated, which were considered indicators of both pre- and in-hospital trauma management. Few individuals (n = 2) exhibited lead times of negative values which were treated as missing values. Outcome was dichotomized into mortality/survival 30 days post-injury. Statistical Analysis Statistical analyses were performed using SPSS (IBM SPSS Statistics, Version 29.0.2.0). Variables were described as medians (interquartile range (IQR)) or counts (proportions), depending on the data type. Differences in lead times for trauma management were analyzed in relation to geographical conditions (large vs. small counties), caseload (high vs. low), and clinical outcome (mortality vs. survival 30 days post-injury) using Mann-Whitney U-test. A multivariate linear regression was performed for each time interval (trauma to alarm, alarm to hospital, alarm to first CT, and hospital to first CT) as the dependent variable. The analyses were adjusted for hospital caseload (higher vs. lower), county size (larger vs. smaller), age, neurological injury severity (GCS) and type of initial hospital (university vs. local), to assess their independent associations. In addition, a multivariate logistic regression was performed with mortality as the dependent variable, to explore the independent association of the lead time variables after adjusting for demography (age and sex) and neurological injury severity (GCS). A p-value < 0.05 was considered statistically significant. Results Demography, management, lead times in management, injury severity, and outcome As presented in Table 1 , the median patient age was 65 years (IQR 46–78). The cohort was predominantly male (n = 3412, 68%), with a median pre-injury ASA score of 2 (IQR 1–3), and median GCS score at admission of 14 (IQR 12–15). The majority of patients were managed at university hospitals, either as primary or secondary cases (n = 3017, 59.9%). Median lead times were as follows: injury to alarm 29 minutes (IQR 2–38), alarm to hospital arrival 45 minutes (IQR 5–52), and arrival to CT 70 minutes (IQR 45–103). The median AIS score for head injuries was 2 (IQR 1–3). In total, 587 (11.7%) underwent craniotomy, and 481 (9.6%) received ICP monitoring. The overall 30-day mortality rate was 19% (n = 930). Table 1 Demography, injury mechanisms, admission status, injuries, time logistics, treatments, and outcome Variables Entire cohort Patients, n (%) 5036 (100.0) Age (years), median (IQR) 65 (46–78) Sex (male/female), n (%) Male 3412 (67.8) Female 1624 (32.2) Pre-injury ASA score, median (IQR) 2 (1–3) Injury mechanism, n (%). Traffic accident 1204 (23.9) Pedestrian accident 160 (3.2) Gunshot wound 21 (0.4) Penetrating trauma 20 (0.4) Blunt trauma 297 (5.9) Low energy fall 1850 (36.7) High energy fall 1268 (25.2) Injury from explosion 6 (0.1) Other (eg suffocation, burns) 101 (2.0) Missing 109 (2.2) GCS at admission, median (IQR) 14 (12–15) GCS motor at admission, median (IQR) 6 (5–6) AIS head, median (IQR) 2 (1–3) Epidural hematoma, n (%) (S06.4) 533 (10.6) Acute subdural hematoma, n (%) (S06.5) 3758 (74.6) Traumatic subarachnoid hemorrhage, n (%) (S06.6) 2541 (50.5) Contusion, n (%) (S06.1) 191 (3.8) Managed at university hospital alone, local hospital only, or both, * n (%) University hospital alone 1733 (37.5) Local hospital alone 1608 (34.8) Both 1284 (27.8) First admitting hospital, university hospital or local hospital*, n (%) University hospital 2337 (46.4) Local hospital 2699 (53.6) Trauma to alarm (minutes),* median (IQR) 29 (2–38) Alarm to hospital (minutes),* median (IQR) 45 (5–52) Alarm to first CT, * median (IQR) 153 (93–474) Hospital to first CT (minutes),* median (IQR) 70 (45–103) Craniotomy (yes), n (%) 587 (11.7) ICP-monitoring (yes), n (%) 481 (9.6) 30-day mortality, n (%) 930 (18.4) *Missing data: Managed at university hospital alone, local hospital alone, or both: n = 413 (8.2%); First admitting hospital: n = 2 (0.0%); Trauma to alarm: n = 848 (16.8); Alarm to hospital: n = 847 (16.8); Alarm to first CT: n = 171 (3.4); Hospital to first CT: n = 170 (3.4); 30 day mortality: n = 113 (2.2%) Lead times of trauma management in relation to geographical factors and hospital caseload The health care regions were dichotomized as smaller or larger based on the median geographical area per hospital (5713 km 2 ), resulting in 10 smaller and 10 larger regions. As also shown in Table 2 , geographically larger regions exhibited longer lead time between alarm to arrival in hospital (p < 0.05), but had no significant association to times from trauma to alarm, from alarm to first CT, or from hospital arrival to first CT. Hospitals were also dichotomized by caseload (Table 3 ), defined as low or high based on having a lower or higher annual volume of TBI patients than the median across all hospitals included in the registry data (46 cases in total, whereof 23 lower and 24 higher caseload hospitals, respectively). Hospital caseload was not significantly associated with any of the lead times. In a multivariate linear regression analysis of geographical area (smaller/larger) and caseload (low/high) in relation to the lead times of TBI management, adjustments were made for age, GCS score, and first admitting hospital (university/local hospital), as presented in Table 4 . Higher hospital caseload correlated independently with longer time from alarm to CT (B = 34.71, p < 0.05) and from hospital arrival to CT (B = 43.99, p < 0.05), but showed no correlation to the other lead times. Larger region size was associated with overall longer lead times (p < 0.05), except for time from alarm to first CT which was shorter (B = -10.76, p 0.05). Higher age was associated with longer time from trauma to alarm, from alarm to hospital and to CT, but not from hospital to CT (p > 0.050). Patients with lower GCS showed faster time from alarm to hospital, meanwhile, gender was not associated with any of the lead times (p > 0.05). Having been initially managed in a local hospital was associated with longer time from trauma to alarm, from alarm to hospital arrival, and from alarm to CT (all p < 0.05), but shorter time from hospital to CT (p < 0.05). Table 2 Time logistics of the trauma emergency care in relation to regional geography Variables Larger regions* Smaller regions** p-value N (%) valid median (IQR) N (%) valid median (IQR) Trauma to alarm (minutes) 8 (80.0) 5 (3–8) 10 (100.0) 5 (5–5) 0.778 Alarm to hospital (minutes) 10 (100.0) 54 (52–64) 10 (100.0) 51 (48–51) 0.041 Alarm to first CT (minutes) 10 (100.0) 98 (95–120) 10 (100.0) 107 (95–107) 0.224 Hospital to first CT (minutes) 10 (100.0) 37 (34–47) 10 (100.0) 48 (39–48) 0.926 * Average catchment area per hospital within the same region > 5713 km² (median of all regions) ** Average catchment area per hospital within the same region ≤ 5713 km² (median of all regions) Table 3 Time logistics of the trauma emergency care in relation to caseload Variables Higher caseload* Lower caseload** p-value N (%) valid median (IQR) N (%) valid median (IQR) Trauma to alarm (minutes) 21 (91.3) 5 (2–45) 22 (91.7) 5 (1–81) 0.095 Alarm to hospital (minutes) 22 (95.7) 51 (38–69) 24 (100.0) 56 (39–79) 0.881 Alarm to first CT (minutes) 21 (91.3) 44 (28–108) 20 (83.3) 42 (30–68) 0.481 Hospital to first CT (minutes) 21 (91.3) 102 (75–149) 21 (87.5) 107 (81–144) 0.529 * Average case load per year > 67 (median of all hospitals). ** Average case load per year < 67 (median of all hospitals ). Table 4 Multivariate linear regression analysis Trauma to alarm Regression coefficient (B) Standardized coefficients (b) p-value Caseload (lower/ higher caseload)* 39.95 0.013 0.445 Size (smaller/ larger regions)* 155.2 0.047 < 0.001 Age (years) 2.355 0.069 0.003 Gender (male/ female)** -29.88 -0.013 0.408 GCS in ED (scale) 0.099 0.020 0.216 Initial caregiver (university/ local)** 61.18 0.038 0.024 Alarm to hospital Regression coefficient (B) Standardized coefficients (b) p-value Caseload (lower/ higher caseload)* -0.271 -0.002 0.899 Size (smaller/ larger regions)* 8.535 0.093 < 0.001 Age (years) 0.166 0.081 < 0.001 Gender (male/ female)** -1.678 -0.018 0.253 GCS in ED (scale) 0.013 0.065 < 0.001 Initial caregiver (university/ local)** 2.935 0.045 0.008 Alarm to first CT Regression coefficient (B) Standardized coefficients (b) p-value Caseload (lower/ higher caseload)* 34.71 0.108 < 0.001 Size (smaller/ larger regions)* -10.76 -0.047 0.002 Age (years) 0.661 0.129 < 0.001 Gender (male/ female)** 1.936 0.008 0.569 GCS in ED (scale) -0.008 -0.016 0.297 Initial caregiver (university/ local)** 26.33 0.160 < 0.001 Hospital to first CT Regression coefficient (B) Standardized coefficients (b) p-value Caseload (lower/ higher caseload)* 43.99 0.043 0.006 Size (smaller/ larger regions)* -11.21 -0.015 0.294 Age (years) 0.213 0.013 0.382 Gender (male/ female)** -3.508 -0.005 0.751 GCS in ED (scale) 0.009 0.005 0.725 Initial caregiver (university/ local)** -36.85 -0.070 < 0.001 *Regression coefficients are presented for higher hospital caseload, larger geographical region size , ** Categorical variables, regression coefficients are presented for female gender and local hospital. Lead times of trauma management in relation to patient outcomes In a univariate analysis, patients who survived presented with significantly longer time from alarm to CT, and hospital to CT, but shorter time from trauma to alarm, as seen in Table 5 . Meanwhile, time from alarm to hospital was not associated with higher mortality (p > 0.05) (Table 5 ). In a multivariate logistic regression analysis, seen in Table 6 , longer time from trauma to alarm was not associated with higher mortality (OR 1.000, 95% CI (1.000–1.000), p < 0.05). Furthermore, there was no other independent association between any of the lead times and mortality, after adjustment for age, gender, and GCS score. Higher risk of mortality was associated with increasing age (OR 1.052, 95% CI (1.046–1.058), p < 0.05). Neither GCS nor gender was significantly associated with mortality. Table 5 Time logistics of the trauma emergency care in relation to survival and mortality Variables Survivors Deceased p-value N (%) valid median (IQR) N (%) valid median (IQR) Trauma to alarm (minutes) 3264 (81.7) 5 (2–27) 846 (91.2) 11 (3–380) < 0.001 Alarm to hospital (minutes) 3264 (81.7) 52 (38–70) 846 (91.2) 52 (37–67) 0.096 Alarm to first CT (minutes) 3472 (81.3) 107 (77–158) 660 (86.3) 87 (71–119) < 0.001 Hospital to first CT (minutes) 3265 (81.7) 48 (29–115) 868 (93.5) 37 (26–58) < 0.001 Table 6 Multivariate logistic regression analysis Variables Mortality OR (95% CI) p-value Age (years) 1.052 (1.046–1.058) < 0.001 Gender (male vs female) * 0.844 (0.706–1.009) 0.063 GCS (sum) 1.000 (1.000–1.001) 0.085 Trauma to alarm (minutes) 1.000 (1.000–1.000) < 0.001 Alarm to hospital (minutes) 1.042 (0.896–1.213) 1.042 Alarm to first CT (minutes) 0.957 (0.822–1.113) 0.568 Hospital to first CT (minutes) 1.037 (0.891–1.207) 0.638 * Categorical variable, OR and 95% CI is presented for female gender. Discussion In this large nationwide study of 5036 TBI patients, the main findings were that most patients arrived at a hospital within one hour of injury, while geographically larger regions exhibited longer prehospital management. More severe neurological and systemic injuries, indicated by lower GCS, were generally associated with shorter time to first CT. Univariate analyses showed overall longer in-hospital management among survivors, although, these relationships did not persist in multivariate regressions. However, the lead times also exhibited a complex interplay with other factors including injury severity, geographical conditions and resource availability. Our findings showed that Swedish prehospital management was generally effective, with most cases arriving at a hospital within an hour. However, there was substantial variation in pre- and in-hospital lead times. Firstly, there were several important factors related to the healthcare organization. Larger geographical county area was independently associated with longer prehospital lead times, consistent with greater transportation distances, but shorter time from arrival in hospital to first CT. The prehospital lead times were also longer at local hospitals, compared to university hospitals, while the opposite was true for in-hospital lead time from hospital arrival to first CT. One possible explanation is that university hospitals are often located in Sweden’s larger cities, where the proximity between patient and hospital and the density of prehospital resources, including helicopter emergency medical services, are generally higher. However, a potential drawback is the typically high patient load at these emergency departments, leading to increased competition for rapid assessment and imaging resources with other critically ill patients. In contrast, a major trauma case at a smaller hospital is more uncommon and may be prioritized more rapidly throughout the chain of care, leading to shorter in-hospital lead times. Consistent with this idea, higher caseload hospitals exhibited slower in-hospital lead times. While a high volume of cases could theoretically lead to greater efficiency through routine and experience [ 21 , 29 – 33 ], this potential advantage was likely outweighed by the strain on resources and bottlenecks associated with managing many critically ill patients simultaneously [ 27 , 30 ]. Secondly, there were also several important patient-specific factors related to the lead times in trauma management. Older patients consistently exhibited longer lead times. Moreover, the presence of comorbidities makes early management more complex, and older or more frail patients may require more thorough assessment and stabilization before proceeding to imaging or definitive care. Also, offering the full extent of advanced trauma care may not always be appropriate or beneficial in this patient group, and individualized decisions regarding the level of intervention are often required. As expected, patients with more severe neurological injuries exhibited shorter prehospital lead times, probably as they received high-priority to receive necessary diagnostics and possibly in-hospital emergency neurosurgery [ 20 ]. Regarding the clinical significance of lead times on outcome, univariate analyses in this study showed that patients who survived exhibited longer lead times to CT and definitive care. This was likely confounded by injury severity, as survivors tended to have milder injuries with less urgent need for intervention. Consistently, in multivariate analysis, adjusting for such clinical variables, no independent association between lead times and outcome could be demonstrated. This suggests that, at the group level, time intervals in trauma management may be of lower prognostic relevance compared to established predictors such as age and neurological injury severity as measured by GCS as major predictors of mortality in TBI [ 13 , 34 ]. Although the concept that "time is brain" remains highly relevant in case of impending brain herniation, this represents a relatively uncommon and dynamic subset in the entire spectrum of TBI eliciting a trauma alarm, as opposed to selected severe cases admitted to neurointensive care units [ 35 ]. In the current cohort, despite triggering trauma team activation due to suspected severe trauma, many patients presented with GCS scores within the mild-to-moderate range. Moreover, even in severe TBI, the number of patients requiring immediate neurosurgery with evacuation of intracranial bleedings may be limited, as a substantial proportion is unconscious due to factors not related to mass effect such as traumatic axonal lesions. Another aspect related to early diagnostics is the risk of intracranial bleeding progression, particularly among patients on anticoagulant therapy [ 36 ]. While such data were not available in this study, previous research has shown a clear benefit of early reversal of warfarin [ 37 ] and potential effects of prothrombin complex concentrates and tranexamic acid in patients on novel oral anticoagulants [ 38 ]. Nevertheless, these nuances may not shift overall outcome patterns at the group level, not least because trauma systems continuously strive to compensate for their weakest links. Clinical deterioration is often met with prompt countermeasures, and adverse events may be mitigated by e.g., emergency neurosurgery before causing lasting harm. Additionally, high-quality trauma care encompasses more than just rapid access to emergency neurosurgery. Timely resuscitation, with attention to airway, breathing, and circulation (ABCDE), is essential to avoid secondary brain injury from hypoxia and hypotension, both of which are well-established predictors of poor outcome in TBI [ 16 , 19 ]. Still, the optimal timing for intervention is not always in the emergency room: extended prehospital time may, in some cases, be justified by the need for airway protection or hemodynamic stabilization, potentially mitigating the harm of secondary insults before hospital arrival [ 19 ]. Ultimately, while specific patient subgroups such as those with herniation syndromes or anticoagulated patients with intracranial hemorrhage may benefit from faster intervention, the complex interplay between injury severity, physiological response, and care quality makes it difficult to isolate time as a primary driver of outcome. In this cohort, characterized by a predominance of mild-to-moderate TBI, the observed lack of association between time and outcome reinforces the notion that age and clinical severity, rather than lead times per se, remain the most robust predictor of prognosis in TBI. Methodological considerations The study has many strengths. It is based on a large national cohort of more than 5000 TBI patients with comprehensive data coverage. Missing data were relatively rare, although certain variables had lower data availability. Furthermore, the registry does not include information about the cause of death. Thus, especially in patients suffering multiple traumatic injuries, it cannot be certain that the patients deceased as a direct cause of the TBI. The extracted times of events from the registry data, used to calculate lead time intervals, may have been imprecise (+/- 1 hour), due to registration in SweTrau being performed retrospectively. This introduces uncertainty, particularly regarding the time intervals which were less than one hour. Although this uncertainty may have contributed to incomprehensive results, as mentioned, the used variables have been shown to have a correctness of 74% or higher for this patient group when allowing a margin of error up to 10 minutes [ 17 ]. Moreover, previous studies on TBI [ 13 , 34 ] have concluded that exact prehospital and in-hospital timings are not the primary determinants of patient outcomes, foreshadowing doubt to the significance of this uncertainty. In this study, having a regional hospital as the first admitting hospital was associated with longer time from trauma to alarm, alarm to hospital arrival, and alarm to CT, but shorter time from trauma and from arrival to CT. This may be caused by a higher proportion of missing data for time from trauma to alarm and alarm to hospital arrival, compared to the lead times which were independent of the time of alarm. However, this higher rate of missing data may also be caused by registration of patients who suffered TBI while already inpatient. Conclusions Many patients in this nationwide cohort arrived at a hospital within one hour of injury and underwent a first trauma CT within two hours. Geographically larger regions exhibited longer prehospital management, while university hospitals and higher caseload hospitals showed longer overall lead times but shorter in-hospital time to first CT. Severe neurological injuries were generally associated with more rapid trauma management, reflecting higher prioritization. Univariate analyses indicated that faster lead times were associated with higher mortality, while no such association was found in multivariate regressions. These findings suggest that lead times in TBI management interact intricately with both care-related and external factors, including injury severity, geographical challenges, system organization, and resource availability. While timely care, particularly in subgroups requiring urgent neurosurgical intervention or hemodynamic stabilization remains crucial, this study reinforces the view that clinical severity markers such as age and GCS are stronger predictors of outcome than the lead times in TBI management. Abbreviations AIS = Abbreviated Injury Scale ASA = American Society of Anesthesiologists ASDH = Acute Subdural Hematoma ATLS = Advanced Trauma Life Support BP = Blood pressure CI = Confidence interval CPR = Cardiopulmonary resuscitation CT = Computed tomography EDH = Epidural hematoma EMS = Emergency medical services GCS = Glasgow Coma Scale HEMS = Helicopter emergency medical services ICP = Intracranial pressure ISS = Injury Severity Score IQR = Interquartile range NISS = New Injury Severity Score OR = Odds-ratios PaCO 2 = Partial pressure of CO 2 sBP = Systolic blood pressure SpO 2 = Peripheral oxygen saturation SweTrau = Swedish Trauma Registry TBI = Traumatic brain injury tSAH= Traumatic subarachnoidal hemorrhage WSES = World Society of Emergency Surgery Declarations Conflict of interest: The authors declare no competing interests. Human ethics and consent to participate The study was approved by the Swedish Ethical Review Authority (Dnr 2023–07084-01) for the use of SweTrau registry data. Informed consent was not required; however, participants had the opportunity to opt out of data registration and request the removal of their registered data from SweTrau. Funding: The study was supported by Uppsala University Hospital. Author Contribution AG: Formal analysis, data curation, and writing – original draft. FLM: Conceptualization and writing – review and editing. AL: Conceptualization and writing – review and editing. AH: Writing – review and editing. LH: Conceptualization and writing – review and editing. PE: Writing – Conceptualization and writing – review and editing. TSW: Conceptualization and writing – original draft. FL: Conceptualization and writing – original draft. All authors have read and agreed to the submitted version of the manuscript. Data Availability The data is available upon reasonable request. 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J Neurotrauma 24:287–293. https://doi.org/10.1089/neu.2006.0031 Holmberg L, Frick Bergström M, Mani K, Wanhainen A, Andréasson H, Linder F (2023) Validation of the Swedish Trauma Registry (SweTrau). Eur J Trauma Emerg Surg 49:1627–1637. https://doi.org/10.1007/s00068-023-02244-6 Dash HH, Chavali S (2018) Management of traumatic brain injury patients. Korean J Anesthesiol 71:12–21. https://doi.org/10.4097/kjae.2018.71.1.12 Gravesteijn BY, Sewalt CA, Stocchetti N, Citerio G, Ercole A, Lingsma HF et al (2021) Prehospital Management of Traumatic Brain Injury across Europe: A CENTER-TBI Study. Prehosp Emerg Care 25:629–643. https://doi.org/10.1080/10903127.2020.1817210 Sy E, Amram O, Baer HJ, Hameed SM, Griesdale DEG (2021) Transport Time and Mortality in Critically Ill Patients with Severe Traumatic Brain Injury. Can J Neurol Sci 48:817–825. https://doi.org/10.1017/cjn.2021.5 Nabeta M, Murotani K, Kannae M, Tashiro K, Hirayu N, Morita T et al (2021) Comparison of physician-staffed helicopter with ground-based emergency medical services for trauma patients. Am J Emerg Med 45:75–79. https://doi.org/10.1016/j.ajem.2021.02.062 Holmberg L, Mani K, Thorbjørnsen K, Wanhainen A, Andréasson H, Juhlin C et al (2022) Trauma triage criteria as predictors of severe injury - a Swedish multicenter cohort study. BMC Emerg Med 22:40. https://doi.org/10.1186/s12873-022-00596-7 Hawryluk GWJ, Aguilera S, Buki A, Bulger E, Citerio G, Cooper DJ et al (2019) A management algorithm for patients with intracranial pressure monitoring: the Seattle International Severe Traumatic Brain Injury Consensus Conference (SIBICC). 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Scand J Trauma Resusc Emerg Med 31:101. https://doi.org/10.1186/s13049-023-01168-9 Leal-Méndez F, Holmberg L, Enblad P, Lewén A, Linder F, Wettervik TS (2024) Emergency neurosurgery for traumatic brain injury by general surgeons at local hospitals in Sweden: a viable option when time is brain. Scand J Trauma Resusc Emerg Med 32:115. https://doi.org/10.1186/s13049-024-01290-2 Befolkningstäthet i Sverige [Internet] (2025) Stat. SCB. [cited 2025 May 1]. https://www.scb.se/hitta-statistik/sverige-i-siffror/manniskorna-i-sverige/befolkningstathet-i-sverige/ . Accessed 1 May Härtl R, Gerber LM, Iacono L, Ni Q, Lyons K, Ghajar J (2006) Direct Transport Within An Organized State Trauma System Reduces Mortality in Patients With Severe Traumatic Brain Injury. J Trauma Acute Care Surg 60:1250. https://doi.org/10.1097/01.ta.0000203717.57821.8d Shibahashi K, Hoda H, Oishio M, Okura Y, Sugiyama K, Hamabe Y (2022) Hospital Volume-Outcome Relationship in Severe Traumatic Brain Injury: A Nationwide Observational Study in Japan. World Neurosurg 160:e118–e125. https://doi.org/10.1016/j.wneu.2021.12.106 Considering the Importance of Personal and Injury Factors Influencing Outcome After Traumatic Brain Injury - Archives of Physical Medicine and Rehabilitation [Internet]. [cited 2024 Nov 6]. https://www.archives-pmr.org/article/S0003-9993(24)00884-0/abstract . Accessed 6 Nov 2024 Trivedi DJ, Bass GA, Forssten MP, Scheufler K-M, Olivecrona M, Cao Y et al (2022) The significance of direct transportation to a trauma center on survival for severe traumatic brain injury. Eur J Trauma Emerg Surg 48:2803. https://doi.org/10.1007/s00068-022-01885-3 Brown JB, Kheng M, Carney NA, Rubiano AM, Puyana JC (2019) Geographical Disparity and Traumatic Brain Injury in America: Rural Areas Suffer Poorer Outcomes. J Neurosci Rural Pract 10:10–15. https://doi.org/10.4103/jnrp.jnrp_310_18 El-Menyar A, Consunji R, Abdelrahman H, Latifi R, Wahlen BM, Al-Thani H (2018) Predictors and Time-Based Hospital Mortality in Patients with Isolated and Polytrauma Brain Injuries. World J Surg 42:1. https://doi.org/10.1007/s00268-017-4310-2 Steyerberg EW, Wiegers E, Sewalt C, Buki A, Citerio G, De Keyser V et al (2019) Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicentre, longitudinal, cohort study. Lancet Neurol 18:923–934. https://doi.org/10.1016/S1474-4422(19)30232-7 Svedung Wettervik T, Lenell S, Enblad P, Lewén A (2021) Pre-injury antithrombotic agents predict intracranial hemorrhagic progression, but not worse clinical outcome in severe traumatic brain injury. Acta Neurochir (Wien) 163:1403–1413. https://doi.org/10.1007/s00701-021-04816-0 Ivascu FA, Howells GA, Junn FS, Bair HA, Bendick PJ, Janczyk RJ (2005) Rapid warfarin reversal in anticoagulated patients with traumatic intracranial hemorrhage reduces hemorrhage progression and mortality. J Trauma 59:1131–1137 discussion 1137–1139. https://doi.org/10.1097/01.ta.0000189067.16368.83 Effect of PCC on outcomes of severe traumatic brain injury patients on preinjury anticoagulation - PubMed [Internet]. [cited 2025 July 14]. https://pubmed.ncbi.nlm.nih.gov/38309997/ . Accessed 14 July 2025 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Acta Neurochirurgica → Version 1 posted Editorial decision: Revision requested 14 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers invited by journal 13 Nov, 2025 Editor assigned by journal 13 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 08 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:11:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8065305/v1/5a865bc3-a91d-4db8-a2b3-bd0de8363748.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lead Times in the Early Management of Traumatic Brain Injury: Relation to Geographic Conditions and Clinical Outcomes in a Nationwide Swedish Registry Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraumatic brain injury (TBI) affects approximately 70\u0026nbsp;million people annually and is a leading cause of mortality and morbidity worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Mild-to-moderate TBI patients typically present with symptoms such as headache, nausea, and amnesia. In some cases, there is a rapid clinical deterioration with loss of consciousness, unreactive pupils, and airway compromise, due to intracranial bleedings and elevated intracranial pressure (ICP) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, a subset of severe TBI cases present immediately in an unconscious state [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Stable patients with mild TBI can typically be discharged from the emergency department or admitted for brief neurological monitoring without requiring further treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Patients with severe injuries or early deterioration are at high risk of mortality and need prompt physiological and neurosurgical management, primarily due to airway obstruction and ventilation failure caused by brain herniation following expanding intracranial hemorrhages [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, patients with TBI are particularly vulnerable to secondary brain injuries resulting from hypoxia and hypotension, which may be aggravated by multi-trauma [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEffective management of TBI requires immediate resuscitation, early diagnostics and careful stratification to distinguish patients requiring emergent neurosurgery from those with mild injuries that can be managed conservatively. This necessitates efficient prehospital and in-hospital care pathways, which minimize delays from trauma to alarm activation, hospital arrival, imaging (trauma CT) and treatments. Prehospital care is particularly influenced by geographical variations in different hospital catchment areas, which may impact the time from injury to hospital arrival. Prehospitally, the need for immediate resuscitation must be weighed against the urgency of timely transport to a hospital capable of appropriate diagnostic imaging and advanced care [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The \"Platinum Ten\" principle recommends limiting on-site care to 10 minutes to avoid unnecessary delays while allowing sufficient time for initial stabilization [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, as shown in the multicenter CENTER-TBI study, prehospital practices vary considerably across Europe [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, upon contact with the receiving hospital, a trauma alarm may be triggered to initiate immediate care, following local or national guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEvaluation and resuscitation of trauma patients follow the Advanced Trauma Life Support (ATLS) protocol prioritizing airway (A), breathing (B), circulation (C), neurological disability (D), and exposure (E) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hemodynamically unstable patients who do not respond to initial interventions may require immediate surgical interventions, while a trauma CT may be performed to evaluate potential injuries in stable patients [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For patients with isolated head trauma, protocols typically mandate that trained personnel assess the patient within 15 minutes of arrival [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. TBI patients with mass lesions are often transferred to neurosurgical centers for hematoma evacuation and neurointensive care [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, variations in hospital experience and caseload, and the distance to the nearest neurosurgical center can differ and influence the time from injury to definitive treatment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSweden is a geographically large country with both densely populated urban areas and extensive sparsely populated regions, resulting in a substantial geographical variation in access to specialized neurosurgical care in terms of geographical distance and transportation time. The healthcare system is administratively divided into 20 individual health care regions, with a total of 49 local hospitals providing around the clock general trauma care, of which 7 university hospitals offer specialized neurosurgical care [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The common practice is that TBI patients are stabilized initially at local hospitals, followed by secondary transfer to a neurosurgical center if needed [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In regions maintaining a university hospital, patients may primarily be admitted to this specific neurosurgical department. Exceptionally, acute extracerebral hematomas may be evacuated in local hospitals as a life-saving procedure [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, in geographically compact and densely populated regions, moderate to severe TBI patients are more often directly transported to hospitals with neurosurgical capabilities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Ultimately, it is evident that the geographical distribution of healthcare resources is uneven and may affect the management of TBI patients.\u003c/p\u003e\u003cp\u003eMany factors may influence the lapse of management of TBI patients, potentially delaying necessary diagnostics and treatments, and increasing risks of developing secondary brain injuries. There is limited evidence on how patient characteristics, geographical factors and hospital experience affect lead times in these early care pathways, and which impact delays have on clinical outcomes. Therefore, the aim of this study was to investigate variations in lead times, their explanatory variables and their effect on mortality in a Swedish nationwide cohort registry study.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThis retrospective observational study utilized data from the Swedish Trauma Registry (SweTrau), a nation-wide registry with data from hospitals in Sweden that provide care for severe traumatic injuries. The study focused on patients with TBI diagnoses and New Injury Severity Score\u0026thinsp;\u0026gt;\u0026thinsp;15 (S06.1\u0026ndash;S06.6), aged 16 or older, treated between January 1, 2018, and December 31, 2022. The data extracted with these inclusion criteria covered 20 Swedish health care regions and 47 hospitals. From the 5914 patients with these diagnoses during this time period, 352 were excluded due to age below 16. Furthermore, duplicate registrations were identified by person identification number, temporary identification, age [years], date of birth, and gender. In total, 526 duplicate cases were excluded, resulting in a final cohort of 5036 individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection and variable definitions\u003c/h3\u003e\n\u003cp\u003eAll data used in this study were acquired from the SweTrau registry [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Demographics (age, sex), injury mechanisms (falls, road traffic accidents etc.), injury severity (Glasgow Coma scale [GCS], abbreviated injury scale [AIS] head, injury severity score [ISS]), injury types (epidural hematoma [EDH], acute subdural hematoma [ASDH], traumatic subarachnoid hemorrhage [tSAH], contusion), interventions (craniotomy) and outcome (30-day mortality) were extracted from the registry.\u003c/p\u003e\u003cp\u003eRegistered date and time of trauma, alarm, primary hospital arrival and first CT, respectively, were also extracted. The accuracy of these time variables in SweTrau has previously been reported to be 74% or higher when allowing a margin of error up to 10 minutes, with a correctness of 89.7% of the registry data overall, and a case completeness of 100% for individuals with NISS\u0026thinsp;\u0026gt;\u0026thinsp;15 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The time intervals were calculated from trauma to alarm and from alarm to arrival in the primary hospital (local hospital or university hospital), which were considered indicators of access to care. Also, time to first CT from alarm and arrival in hospital was calculated, which were considered indicators of both pre- and in-hospital trauma management. Few individuals (n\u0026thinsp;=\u0026thinsp;2) exhibited lead times of negative values which were treated as missing values. Outcome was dichotomized into mortality/survival 30 days post-injury.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS (IBM SPSS Statistics, Version 29.0.2.0). Variables were described as medians (interquartile range (IQR)) or counts (proportions), depending on the data type. Differences in lead times for trauma management were analyzed in relation to geographical conditions (large vs. small counties), caseload (high vs. low), and clinical outcome (mortality vs. survival 30 days post-injury) using Mann-Whitney U-test. A multivariate linear regression was performed for each time interval (trauma to alarm, alarm to hospital, alarm to first CT, and hospital to first CT) as the dependent variable. The analyses were adjusted for hospital caseload (higher vs. lower), county size (larger vs. smaller), age, neurological injury severity (GCS) and type of initial hospital (university vs. local), to assess their independent associations. In addition, a multivariate logistic regression was performed with mortality as the dependent variable, to explore the independent association of the lead time variables after adjusting for demography (age and sex) and neurological injury severity (GCS). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eDemography, management, lead times in management, injury severity, and outcome\u003c/h2\u003e\u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the median patient age was 65 years (IQR 46\u0026ndash;78). The cohort was predominantly male (n\u0026thinsp;=\u0026thinsp;3412, 68%), with a median pre-injury ASA score of 2 (IQR 1\u0026ndash;3), and median GCS score at admission of 14 (IQR 12\u0026ndash;15). The majority of patients were managed at university hospitals, either as primary or secondary cases (n\u0026thinsp;=\u0026thinsp;3017, 59.9%).\u003c/p\u003e\u003cp\u003eMedian lead times were as follows: injury to alarm 29 minutes (IQR 2\u0026ndash;38), alarm to hospital arrival 45 minutes (IQR 5\u0026ndash;52), and arrival to CT 70 minutes (IQR 45\u0026ndash;103). The median AIS score for head injuries was 2 (IQR 1\u0026ndash;3). In total, 587 (11.7%) underwent craniotomy, and 481 (9.6%) received ICP monitoring. The overall 30-day mortality rate was 19% (n\u0026thinsp;=\u0026thinsp;930).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemography, injury mechanisms, admission status, injuries, time logistics, treatments, and outcome\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eEntire cohort\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePatients, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e5036 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e65 (46\u0026ndash;78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eSex (male/female), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eMale 3412 (67.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eFemale 1624 (32.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePre-injury ASA score, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eInjury mechanism, n (%).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTraffic accident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1204 (23.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePedestrian accident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160 (3.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGunshot wound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (0.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePenetrating trauma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (0.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBlunt trauma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e297 (5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLow energy fall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1850 (36.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHigh energy fall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1268 (25.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eInjury from explosion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOther (eg suffocation, burns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS at admission, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e14 (12\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS motor at admission, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e6 (5\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIS head, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpidural hematoma, n (%) (S06.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e533 (10.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute subdural hematoma, n (%) (S06.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e3758 (74.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic subarachnoid hemorrhage, n (%) (S06.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e2541 (50.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContusion, n (%) (S06.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e191 (3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eManaged at university hospital alone, local hospital only, or both, * n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity hospital alone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1733 (37.5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal hospital alone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1608 (34.8)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1284 (27.8)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFirst admitting hospital, university hospital or local hospital*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2337 (46.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2699 (53.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrauma to alarm (minutes),* median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e29 (2\u0026ndash;38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to hospital (minutes),* median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e45 (5\u0026ndash;52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to first CT, * median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e153 (93\u0026ndash;474)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital to first CT (minutes),* median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e70 (45\u0026ndash;103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCraniotomy (yes), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e587 (11.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICP-monitoring (yes), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e481 (9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e930 (18.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003e*Missing data: Managed at university hospital alone, local hospital alone, or both: n\u0026thinsp;=\u0026thinsp;413 (8.2%); First admitting hospital: n\u0026thinsp;=\u0026thinsp;2 (0.0%); Trauma to alarm: n\u0026thinsp;=\u0026thinsp;848 (16.8); Alarm to hospital: n\u0026thinsp;=\u0026thinsp;847 (16.8); Alarm to first CT: n\u0026thinsp;=\u0026thinsp;171 (3.4); Hospital to first CT: n\u0026thinsp;=\u0026thinsp;170 (3.4); 30 day mortality: n\u0026thinsp;=\u0026thinsp;113 (2.2%)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLead times of trauma management in relation to geographical factors and hospital caseload\u003c/h2\u003e\u003cp\u003eThe health care regions were dichotomized as smaller or larger based on the median geographical area per hospital (5713 km\u003csup\u003e2\u003c/sup\u003e), resulting in 10 smaller and 10 larger regions. As also shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, geographically larger regions exhibited longer lead time between alarm to arrival in hospital (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but had no significant association to times from trauma to alarm, from alarm to first CT, or from hospital arrival to first CT. Hospitals were also dichotomized by caseload (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), defined as low or high based on having a lower or higher annual volume of TBI patients than the median across all hospitals included in the registry data (46 cases in total, whereof 23 lower and 24 higher caseload hospitals, respectively). Hospital caseload was not significantly associated with any of the lead times.\u003c/p\u003e\u003cp\u003eIn a multivariate linear regression analysis of geographical area (smaller/larger) and caseload (low/high) in relation to the lead times of TBI management, adjustments were made for age, GCS score, and first admitting hospital (university/local hospital), as presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Higher hospital caseload correlated independently with longer time from alarm to CT (B\u0026thinsp;=\u0026thinsp;34.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and from hospital arrival to CT (B\u0026thinsp;=\u0026thinsp;43.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but showed no correlation to the other lead times. Larger region size was associated with overall longer lead times (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for time from alarm to first CT which was shorter (B = -10.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and did not correlate to the time from hospital arrival to CT (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eHigher age was associated with longer time from trauma to alarm, from alarm to hospital and to CT, but not from hospital to CT (p\u0026thinsp;\u0026gt;\u0026thinsp;0.050). Patients with lower GCS showed faster time from alarm to hospital, meanwhile, gender was not associated with any of the lead times (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Having been initially managed in a local hospital was associated with longer time from trauma to alarm, from alarm to hospital arrival, and from alarm to CT (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but shorter time from hospital to CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTime logistics of the trauma emergency care in relation to regional geography\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLarger regions*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSmaller regions**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN (%) valid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN (%) valid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrauma to alarm (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3\u0026ndash;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (5\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to hospital (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (52\u0026ndash;64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51 (48\u0026ndash;51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (95\u0026ndash;120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (95\u0026ndash;107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (34\u0026ndash;47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (39\u0026ndash;48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e* Average catchment area per hospital within the same region\u0026thinsp;\u0026gt;\u0026thinsp;5713 km\u0026sup2; (median of all regions) ** Average catchment area per hospital within the same region\u0026thinsp;\u0026le;\u0026thinsp;5713 km\u0026sup2; (median of all regions)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTime logistics of the trauma emergency care in relation to caseload\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHigher caseload*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eLower caseload**\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN (%) valid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN (%) valid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrauma to alarm (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2\u0026ndash;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (91.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (1\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to hospital (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (38\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56 (39\u0026ndash;79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (28\u0026ndash;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42 (30\u0026ndash;68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (75\u0026ndash;149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (81\u0026ndash;144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* \u003cem\u003eAverage case load per year\u0026thinsp;\u0026gt;\u0026thinsp;67 (median of all hospitals). ** Average case load per year\u0026thinsp;\u0026lt;\u0026thinsp;67 (median of all hospitals\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultivariate linear regression analysis\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTrauma to alarm\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRegression coefficient (B)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eStandardized coefficients (b)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaseload (lower/ higher caseload)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize (smaller/ larger regions)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/ female)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-29.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS in ED (scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial caregiver (university/ local)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAlarm to hospital\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRegression coefficient (B)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eStandardized coefficients (b)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaseload (lower/ higher caseload)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize (smaller/ larger regions)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/ female)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS in ED (scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial caregiver (university/ local)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAlarm to first CT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRegression coefficient (B)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eStandardized coefficients (b)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaseload (lower/ higher caseload)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize (smaller/ larger regions)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/ female)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS in ED (scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial caregiver (university/ local)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHospital to first CT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRegression coefficient (B)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eStandardized coefficients (b)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaseload (lower/ higher caseload)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize (smaller/ larger regions)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-11.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/ female)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS in ED (scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial caregiver (university/ local)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-36.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*Regression coefficients are presented for higher hospital caseload, larger geographical region size\u003c/em\u003e,\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e** Categorical variables, regression coefficients are presented for female gender and local hospital.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLead times of trauma management in relation to patient outcomes\u003c/h3\u003e\n\u003cp\u003eIn a univariate analysis, patients who survived presented with significantly longer time from alarm to CT, and hospital to CT, but shorter time from trauma to alarm, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Meanwhile, time from alarm to hospital was not associated with higher mortality (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn a multivariate logistic regression analysis, seen in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, longer time from trauma to alarm was not associated with higher mortality (OR 1.000, 95% CI (1.000\u0026ndash;1.000), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, there was no other independent association between any of the lead times and mortality, after adjustment for age, gender, and GCS score. Higher risk of mortality was associated with increasing age (OR 1.052, 95% CI (1.046\u0026ndash;1.058), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Neither GCS nor gender was significantly associated with mortality.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTime logistics of the trauma emergency care in relation to survival and mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSurvivors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN (%) valid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN (%) valid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrauma to alarm (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3264 (81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e846 (91.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (3\u0026ndash;380)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to hospital (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3264 (81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (38\u0026ndash;70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e846 (91.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52 (37\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3472 (81.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (77\u0026ndash;158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e660 (86.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87 (71\u0026ndash;119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3265 (81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (29\u0026ndash;115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e868 (93.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (26\u0026ndash;58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.052 (1.046\u0026ndash;1.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male vs female) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.844 (0.706\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS (sum)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000 (1.000\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrauma to alarm (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to hospital (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.042 (0.896\u0026ndash;1.213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlarm to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.957 (0.822\u0026ndash;1.113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital to first CT (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.037 (0.891\u0026ndash;1.207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*\u003cem\u003eCategorical variable, OR and 95% CI is presented for female gender.\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large nationwide study of 5036 TBI patients, the main findings were that most patients arrived at a hospital within one hour of injury, while geographically larger regions exhibited longer prehospital management. More severe neurological and systemic injuries, indicated by lower GCS, were generally associated with shorter time to first CT. Univariate analyses showed overall longer in-hospital management among survivors, although, these relationships did not persist in multivariate regressions. However, the lead times also exhibited a complex interplay with other factors including injury severity, geographical conditions and resource availability.\u003c/p\u003e\u003cp\u003eOur findings showed that Swedish prehospital management was generally effective, with most cases arriving at a hospital within an hour. However, there was substantial variation in pre- and in-hospital lead times. Firstly, there were several important factors related to the healthcare organization. Larger geographical county area was independently associated with longer prehospital lead times, consistent with greater transportation distances, but shorter time from arrival in hospital to first CT. The prehospital lead times were also longer at local hospitals, compared to university hospitals, while the opposite was true for in-hospital lead time from hospital arrival to first CT. One possible explanation is that university hospitals are often located in Sweden\u0026rsquo;s larger cities, where the proximity between patient and hospital and the density of prehospital resources, including helicopter emergency medical services, are generally higher. However, a potential drawback is the typically high patient load at these emergency departments, leading to increased competition for rapid assessment and imaging resources with other critically ill patients. In contrast, a major trauma case at a smaller hospital is more uncommon and may be prioritized more rapidly throughout the chain of care, leading to shorter in-hospital lead times. Consistent with this idea, higher caseload hospitals exhibited slower in-hospital lead times. While a high volume of cases could theoretically lead to greater efficiency through routine and experience [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], this potential advantage was likely outweighed by the strain on resources and bottlenecks associated with managing many critically ill patients simultaneously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Secondly, there were also several important patient-specific factors related to the lead times in trauma management. Older patients consistently exhibited longer lead times. Moreover, the presence of comorbidities makes early management more complex, and older or more frail patients may require more thorough assessment and stabilization before proceeding to imaging or definitive care. Also, offering the full extent of advanced trauma care may not always be appropriate or beneficial in this patient group, and individualized decisions regarding the level of intervention are often required. As expected, patients with more severe neurological injuries exhibited shorter prehospital lead times, probably as they received high-priority to receive necessary diagnostics and possibly in-hospital emergency neurosurgery [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding the clinical significance of lead times on outcome, univariate analyses in this study showed that patients who survived exhibited longer lead times to CT and definitive care. This was likely confounded by injury severity, as survivors tended to have milder injuries with less urgent need for intervention. Consistently, in multivariate analysis, adjusting for such clinical variables, no independent association between lead times and outcome could be demonstrated. This suggests that, at the group level, time intervals in trauma management may be of lower prognostic relevance compared to established predictors such as age and neurological injury severity as measured by GCS as major predictors of mortality in TBI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the concept that \"time is brain\" remains highly relevant in case of impending brain herniation, this represents a relatively uncommon and dynamic subset in the entire spectrum of TBI eliciting a trauma alarm, as opposed to selected severe cases admitted to neurointensive care units [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the current cohort, despite triggering trauma team activation due to suspected severe trauma, many patients presented with GCS scores within the mild-to-moderate range. Moreover, even in severe TBI, the number of patients requiring immediate neurosurgery with evacuation of intracranial bleedings may be limited, as a substantial proportion is unconscious due to factors not related to mass effect such as traumatic axonal lesions. Another aspect related to early diagnostics is the risk of intracranial bleeding progression, particularly among patients on anticoagulant therapy [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. While such data were not available in this study, previous research has shown a clear benefit of early reversal of warfarin [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and potential effects of prothrombin complex concentrates and tranexamic acid in patients on novel oral anticoagulants [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Nevertheless, these nuances may not shift overall outcome patterns at the group level, not least because trauma systems continuously strive to compensate for their weakest links. Clinical deterioration is often met with prompt countermeasures, and adverse events may be mitigated by e.g., emergency neurosurgery before causing lasting harm. Additionally, high-quality trauma care encompasses more than just rapid access to emergency neurosurgery. Timely resuscitation, with attention to airway, breathing, and circulation (ABCDE), is essential to avoid secondary brain injury from hypoxia and hypotension, both of which are well-established predictors of poor outcome in TBI [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Still, the optimal timing for intervention is not always in the emergency room: extended prehospital time may, in some cases, be justified by the need for airway protection or hemodynamic stabilization, potentially mitigating the harm of secondary insults before hospital arrival [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUltimately, while specific patient subgroups such as those with herniation syndromes or anticoagulated patients with intracranial hemorrhage may benefit from faster intervention, the complex interplay between injury severity, physiological response, and care quality makes it difficult to isolate time as a primary driver of outcome. In this cohort, characterized by a predominance of mild-to-moderate TBI, the observed lack of association between time and outcome reinforces the notion that age and clinical severity, rather than lead times per se, remain the most robust predictor of prognosis in TBI.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMethodological considerations\u003c/h2\u003e\u003cp\u003eThe study has many strengths. It is based on a large national cohort of more than 5000 TBI patients with comprehensive data coverage. Missing data were relatively rare, although certain variables had lower data availability. Furthermore, the registry does not include information about the cause of death. Thus, especially in patients suffering multiple traumatic injuries, it cannot be certain that the patients deceased as a direct cause of the TBI.\u003c/p\u003e\u003cp\u003eThe extracted times of events from the registry data, used to calculate lead time intervals, may have been imprecise (+/- 1 hour), due to registration in SweTrau being performed retrospectively. This introduces uncertainty, particularly regarding the time intervals which were less than one hour. Although this uncertainty may have contributed to incomprehensive results, as mentioned, the used variables have been shown to have a correctness of 74% or higher for this patient group when allowing a margin of error up to 10 minutes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, previous studies on TBI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] have concluded that exact prehospital and in-hospital timings are not the primary determinants of patient outcomes, foreshadowing doubt to the significance of this uncertainty.\u003c/p\u003e\u003cp\u003eIn this study, having a regional hospital as the first admitting hospital was associated with longer time from trauma to alarm, alarm to hospital arrival, and alarm to CT, but shorter time from trauma and from arrival to CT. This may be caused by a higher proportion of missing data for time from trauma to alarm and alarm to hospital arrival, compared to the lead times which were independent of the time of alarm. However, this higher rate of missing data may also be caused by registration of patients who suffered TBI while already inpatient.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMany patients in this nationwide cohort arrived at a hospital within one hour of injury and underwent a first trauma CT within two hours. Geographically larger regions exhibited longer prehospital management, while university hospitals and higher caseload hospitals showed longer overall lead times but shorter in-hospital time to first CT. Severe neurological injuries were generally associated with more rapid trauma management, reflecting higher prioritization. Univariate analyses indicated that faster lead times were associated with higher mortality, while no such association was found in multivariate regressions. These findings suggest that lead times in TBI management interact intricately with both care-related and external factors, including injury severity, geographical challenges, system organization, and resource availability. While timely care, particularly in subgroups requiring urgent neurosurgical intervention or hemodynamic stabilization remains crucial, this study reinforces the view that clinical severity markers such as age and GCS are stronger predictors of outcome than the lead times in TBI management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIS = Abbreviated Injury Scale\u003c/p\u003e\n\u003cp\u003eASA = American Society of Anesthesiologists\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASDH = Acute Subdural Hematoma\u003c/p\u003e\n\u003cp\u003eATLS = Advanced Trauma Life Support\u003c/p\u003e\n\u003cp\u003eBP = Blood pressure\u003c/p\u003e\n\u003cp\u003eCI = Confidence interval\u003c/p\u003e\n\u003cp\u003eCPR = Cardiopulmonary resuscitation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT = Computed tomography\u003c/p\u003e\n\u003cp\u003eEDH = Epidural hematoma\u003c/p\u003e\n\u003cp\u003eEMS = Emergency medical services\u003c/p\u003e\n\u003cp\u003eGCS = Glasgow Coma Scale\u003c/p\u003e\n\u003cp\u003eHEMS = Helicopter emergency medical services\u003c/p\u003e\n\u003cp\u003eICP = Intracranial pressure\u003c/p\u003e\n\u003cp\u003eISS = Injury Severity Score\u003c/p\u003e\n\u003cp\u003eIQR = Interquartile range\u003c/p\u003e\n\u003cp\u003eNISS = New Injury Severity Score\u003c/p\u003e\n\u003cp\u003eOR = Odds-ratios\u003c/p\u003e\n\u003cp\u003ePaCO\u003csub\u003e2\u003c/sub\u003e = Partial pressure of CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003esBP = Systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e= Peripheral oxygen saturation\u003c/p\u003e\n\u003cp\u003eSweTrau = Swedish Trauma Registry\u003c/p\u003e\n\u003cp\u003eTBI = Traumatic brain injury\u003c/p\u003e\n\u003cp\u003etSAH= Traumatic subarachnoidal hemorrhage\u003c/p\u003e\n\u003cp\u003eWSES = World Society of Emergency Surgery\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate\u003c/strong\u003e\u003cp\u003eThe study was approved by the Swedish Ethical Review Authority (Dnr 2023\u0026ndash;07084-01) for the use of SweTrau registry data. Informed consent was not required; however, participants had the opportunity to opt out of data registration and request the removal of their registered data from SweTrau.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe study was supported by Uppsala University Hospital.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAG: Formal analysis, data curation, and writing \u0026ndash; original draft. FLM: Conceptualization and writing \u0026ndash; review and editing. AL: Conceptualization and writing \u0026ndash; review and editing. AH: Writing \u0026ndash; review and editing. LH: Conceptualization and writing \u0026ndash; review and editing. PE: Writing \u0026ndash; Conceptualization and writing \u0026ndash; review and editing. TSW: Conceptualization and writing \u0026ndash; original draft. FL: Conceptualization and writing \u0026ndash; original draft. All authors have read and agreed to the submitted version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data is available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u0026Aring;rsrapport-SweTrau -2021.pdf [Internet]. [cited 2024 Oct 4]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rcsyd.se/swetrau/wp-content/uploads/sites/10/2022/06/A%CC%8Arsrapport-SweTrau-2021.pdf\u003c/span\u003e\u003cspan address=\"https://rcsyd.se/swetrau/wp-content/uploads/sites/10/2022/06/A%CC%8Arsrapport-SweTrau-2021.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 4 Oct 2024\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDewan MC, Rattani A, Gupta S, Baticulon RE, Hung Y-C, Punchak M et al (2019) Estimating the global incidence of traumatic brain injury. 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J Trauma 59:1131\u0026ndash;1137 discussion 1137\u0026ndash;1139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.ta.0000189067.16368.83\u003c/span\u003e\u003cspan address=\"10.1097/01.ta.0000189067.16368.83\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEffect of PCC on outcomes of severe traumatic brain injury patients on preinjury anticoagulation - PubMed [Internet]. [cited 2025 July 14]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38309997/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38309997/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 14 July 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-neurochirurgica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anch","sideBox":"Learn more about [Acta Neurochirurgica](http://link.springer.com/journal/701)","snPcode":"701","submissionUrl":"https://submission.springernature.com/new-submission/701/3","title":"Acta Neurochirurgica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lead time, outcome, Swedish Trauma Registry, traumatic brain injury, trauma logistics","lastPublishedDoi":"10.21203/rs.3.rs-8065305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8065305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTraumatic brain injury (TBI) patients are at risk of sudden deterioration, requiring timely diagnostics and treatment to prevent secondary cerebral injuries. This study investigated lead times in prehospital and early intrahospital TBI management, assessing their association with geographical conditions, hospital caseloads, and patient outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis nationwide, observational cohort study included 5036 TBI patients (during 2018\u0026ndash;2022) from the Swedish Trauma Registry (SweTrau). Lead times from trauma to alarm, from alarm to hospital arrival, and times to first computed tomography (CT) from alarm and hospital arrival, respectively, were calculated. These were analyzed against the geographical distribution of healthcare, hospital caseloads, and 30-day mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe majority of the cohort arrived in hospital within one hour and suffered a mild-to-moderate TBI. In univariate analyses, healthcare regions with larger geographical catchment areas exhibited longer time of prehospital management from alarm to arrival in hospital than smaller regions. Meanwhile, in multivariate linear regressions, larger region catchment area was independently associated with longer times from trauma to alarm and from alarm to hospital, but shorter time from alarm to first CT. In similar multivariate analyses, higher caseload was associated with longer time from alarm to first CT. Patients who were initially managed in a local hospital exhibited longer lead times overall, except from time to first CT from arrival in hospital. Furthermore, in the whole cohort, longer time from alarm to first CT and from arrival in hospital to first CT were associated with lower rate of mortality in univariate logistic regressions. However, this did not hold true in multivariate analysis after adjusting for demography and injury severity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLead times in TBI management varied by both geographical and hospital-bound factors. Faster lead times in TBI were associated with higher mortality in univariate analysis, but this association disappeared in multivariate analysis, suggesting that clinical severity rather than time alone is the stronger predictor of outcome. Nonetheless, it remains evident that efficient and qualitative management is a fundamental necessity for better outcomes in TBI management.\u003c/p\u003e","manuscriptTitle":"Lead Times in the Early Management of Traumatic Brain Injury: Relation to Geographic Conditions and Clinical Outcomes in a Nationwide Swedish Registry Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 14:41:13","doi":"10.21203/rs.3.rs-8065305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T12:52:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T20:15:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44438010031764313115563815682968846746","date":"2025-12-15T19:15:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169869018139575030725415352161059810906","date":"2025-12-11T08:41:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T22:22:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327800257612973023984035695790054451224","date":"2025-11-20T09:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-13T08:14:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-13T05:21:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T05:19:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Neurochirurgica","date":"2025-11-08T16:41:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-neurochirurgica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anch","sideBox":"Learn more about [Acta Neurochirurgica](http://link.springer.com/journal/701)","snPcode":"701","submissionUrl":"https://submission.springernature.com/new-submission/701/3","title":"Acta Neurochirurgica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f269237-6f26-4bb2-8b48-a6e9b226ed1b","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:07:37+00:00","versionOfRecord":{"articleIdentity":"rs-8065305","link":"https://doi.org/10.1007/s00701-026-06817-3","journal":{"identity":"acta-neurochirurgica","isVorOnly":false,"title":"Acta Neurochirurgica"},"publishedOn":"2026-03-10 15:59:33","publishedOnDateReadable":"March 10th, 2026"},"versionCreatedAt":"2025-11-25 14:41:13","video":"","vorDoi":"10.1007/s00701-026-06817-3","vorDoiUrl":"https://doi.org/10.1007/s00701-026-06817-3","workflowStages":[]},"version":"v1","identity":"rs-8065305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8065305","identity":"rs-8065305","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}