Article, Traumatology

Major trauma registry of Navarre (Spain): the accuracy of different survival prediction models

a b s t r a c t

Objective: To determine which factors predict death among trauma patients who are alive on arrival at hospital.

Methods: Design prospective cohort study method. Data were collected on 378 trauma patients who were initially delivered by the emergency medical services of Navarre (Spain) with multiple injuries with a New Injury Severity Score of 15 or more in 2011-2012. These data related to age, gender, presence of premorbid conditions, Abbreviated Injury Score, injury severity score, new injury severity score (NISS), Revised Trauma Score , and prehospital and hospital Response times. Bivariate analysis was used to show the association between each variable and time until death. Mortality prediction was modeled using logistic regression analysis.

Results: The variables related to the end result were the age of the patient, associated comorbidity, NISS, and hospital RTS. Two models were formulated: in one, the variables used were quantitative, while in the other model these variables were converted into dichotomous qualitative variables. The predictive capability of the two models was compared with the trauma and injury severity score using the area under the curve. The predictive capacities of the three models had areas under the curve of 0.93, 0.88, and 0.87. The response times of the Navarre emergency services system, measured as the sum of the time taken to reach the hospital (median time of 65 min), formulate computed tomography (46 min), and perform crucial surgery (115 min), when required, were not taken into account.

Conclusion: Age, premorbid conditions, hospital RTS, and NISS are significant predictors of death after trauma. The time intervals between the accident and arrival at the hospital, arrival at the hospital and the first computed tomography scan or the first crucial emergency intervention, do not appear to affect the risk of death.

(C) 2013

Introduction

Trauma is a major public health issue worldwide and one of the leading causes of death and disability. It also has high medical and social costs [1-3]. For people under 35, injury is the leading cause of death. According to the World Health Organization, traffic accidents will go from being the ninth largest single cause of global deaths (irrespective of age) in 2004 to the fifth largest single cause in 2030. Over the same period, it will go from being the ninth most significant cause of disease to third [4].

Polytrauma is important in our society for a number of reasons. It is highly prevalent (in Spain it is the fifth largest cause of mortality across

? Permission note (ethics): The study has been approved by the Ethics Committee of the Department of Health of the government of Navarre. The subjects gave their informed consent to the work.

* Corresponding author. Tel.: +34 638007066; fax: +34 848422350.

E-mail address: [email protected] (T. Belzunegui).

the population as a whole and the most common cause of death among people under 40) and has significant economic costs, with direct and indirect costs (health care, social care, and loss of productivity) and major social repercussions, with a large number of premature deaths (i.e. many potential years of life lost) and disabilities [5,6].

In view of this situation, it is important to be familiar with the epidemiological profile of trauma patients and to determine the factors that play a role in their mortality. These objectives are met by collecting data and analyzing patterns that can be used to plan health policy. It should also help us in our formulation of critical analyses not only of our actions but also of our system for providing care to serious trauma patients, and help pinpoint areas for improvement [7].

Comparisons of mortality rates with predicted survival rates among trauma patients are useful in assessing the quality of care provided to injured patients. Several European countries use the trauma and injury severity score (TRISS), which was developed in North America [8]. The TRISS is a logistic regression model of survival probability based on variables such as age, revised trauma score (RTS)

0735-6757/$ – see front matter (C) 2013 http://dx.doi.org/10.1016/j.ajem.2013.06.026

[9], and injury severity score (ISS). [10,11] The TRISS coefficients have been updated from the initial major trauma outcome study [12] and, most recently, in 2009 with data obtained from the national trauma databank). [13,14]

Our objectives are to analyze data from the first Spanish major trauma registry (MTR) (the register of trauma patients attended to by the accident and emergency health care system in Navarre); [15] to learn about the outpatient care received by the victim, the seriousness of their injuries, and the treatment and progress of these patients in hospital, and to determine the variables that predict mortality; and to compare our performance with internationally accepted standards.

Methods

Navarra is a region in the north of Spain that shares a border with France, with an area of 10,421 km2 and a population of 637,000. The emergency health care system is managed by a coordination centre, which mobilizes resources for outpatient care according to the seriousness of the victim’s condition (medicalized and non-medical- ized ambulances) that carry patients to the appropriate hospital emergency services. Navarre has a trauma centre and two general Regional hospitals.

The Major Trauma Registry of Navarre (MTRN) is an online IT application that uses programming language JAVA+JSP, and is hosted on a JBoss 5.0 server and PostgreSQL database. Variables, with their relevant categories entered in this database, were adapted strictly to

those defined by the Utstein model (Table 1) [16,17]. The injuries sustained by each patient were entered using an IT application based on the Abbreviated Injury Scale [18].

Data protection was guaranteed using SSL 3.0/TLS 1.0 encryption mechanisms and an access register. The project was approved by the ethics committee of the Navarre health service.

In order to be included in the register, to ensure consistent data collection and comparison across Europe, a patient who sustains a severe injury must meet the following criteria: a new injury severity score (NISS) of >=15. Exclusion criteria were admission to reporting hospital more than 24 hours after injury was sustained, patient declared dead before arrival at hospital or showing no signs of life upon arrival and no response to hospital resuscitation, or victim of asphyxia or drowning. Burn patients are excluded if burns are the predominant injury [16].

Design: Prospective cohort study method.

Data management and analysis

Analysis was performed using SPSS version 21.0 [19].

Categorical data were presented using the absolute number and the percentage, while quantitative data were expressed using the mean and SD and the median and inter-quartile range (IQR), when considered appropriate. Categorical data were compared using the ?2 test. When the conditions of application were not met, and in 2 x 2 tables, we used Fisher’s exact test. Quantitative variables were compared using the

Table 1

predictive model variables

Variables related to the fragility of the patient

Data variable categories or values

Age The patient’s age at the time of injury

Gender 1 = Female; 2 = Male

Pre-injury ASA-PS classification system 1 = A normal healthy patient; 2 = A patient with mild systemic disease; 3 = A patient with severe

systemic disease

Variables related to the accident

Dominating type of injury 1 = Blunt; 2 = Penetrating

Mechanism of injury 1 = Motor vehicle injury; 2 = Motorcycle injury; 3 = Bicycle injury; 4 = Pedestrian; 5 = Traffic: other;

6 = Shot by handgun, shotgun, rifle, other firearm of any dimension; 7 = Stabbed by knife, sword, dagger, other pointed or sharp

object; 8 = Struck or hit by blunt object; 9 = Low energy fall; 10 = High energy fall

Intention of injury 1 = Accident (unintentional); 2 = Self-inflicted (suspected suicide, incomplete suicide attempt, or injury attempt);

3 = Assault; 4 = Other

RTS and T-RTS upon arrival of EMS personnel at scene First recorded pre-intervention upon arrival at scene by medical personnel trained to assess. RTS and T-RTS upon arrival in A&E/hospital First recorded upon arrival in the ED/hospital.

Arterial base excess First measured arterial base excess after arrival in the hospital.

Coagulation: INR Use the first measured INR within the first hour after hospital arrival.

AIS The AIS severity codes that reflect the patient’s injuries.

ISS and NISS The ISS and NISS codes that reflect the patient’s injuries. System of emergency characteristic descriptors

Time from alarm to arrival at scene The time from when the emergency call is answered (at the emergency call centre) until the first

medical provider arrives at the patient.

Highest level of prehospital care provider 1 = Level I. No field care; 2 = Level II. Basic life support; 3 = Level III. Advanced life support,

no physician present; 4 = Level IV. Advanced life support on-scene, physician field care

Pre-hospital intubation 1 = No; 2 = Yes

Time from alarm to hospital arrival The time between when the alarm call is answered (at the emergency call centre) and when the

patient arrives at the reporting hospital.

Time to first CT scan The time from hospital admission until the time marked on the first CT scan image. Time until first key emergency interventions The time from hospital admission until the FIRST emergency intervention.

Type of first key emergency intervention 1 = Damage control thoracotomy; 2 = damage control laparotomy; 3 = Extraperitoneal pelvic

packing; 4 = Limb revascularisation; 5 = interventional radiology; 6 = Craniotomy; 7 = intracranial pressure device

Outcomes

Discharge destination 1 = Home; 2 = Rehabilitation; 3 = Morgue; 4 = Another CCU (higher treatment level); 5 = Another intermediate or low care somatic hospital ward

Glasgow Outcome Scale – at discharge from main hospital 1 = good recovery; 2 = Moderate disability (disabled but independent); 3 = Severe disability

(Conscious but disabled; depends upon others); 4 = Persistent vegetative state (unresponsive); 5 = Death

survival status 1 = Dead; 2 = Alive (30 days after injury) Major Trauma Registry of Navarre 2011-2012.

Student t test, while non-parametric tests were compared using Mann- Whitney U test. When more than two quantitative variables were being compared, we used variance analysis.

To determine the logistic regression model that is the best predictor of mortality, a univariate analysis was conducted to determine which variables were associated with the dependant variable (survival). receiver operating characteristic curves were used to determine the best index when there are different indices that measure the same thing, and logistic regression to determine the model that best predicts the survival of our patients.

When adapting the model, variables with P b .10 and those considered relevant from a clinical perspective were chosen. We assessed the validity of the model with the area under the ROC curve. We concluded that there was statistical significance when P b .05.

Results

Between January 1, 2011, and December 31, 2012, 378 patients who met the criteria for inclusion (69.3% men and 30.7% women) were recorded in the MTRN. Of this number, 71 (18.8%) died.

The proportions of the most relevant variables from a clinical perspective, broken down according to survival and non-survival rates, are provided in Table 2.

The average age was 52.32 +- 22.7, with a range of between 0 and 99 years. The average age of men was 49.72 +- 21.15, while that of women was 58.21 +- 24.92 (P b .05). With regards to the morbidity and age of patients, healthy patients were on average 40.35 +- 17.89 years of age, while those who suffered from a mild systemic disease were on average 71.82 +- 13.78 years of age. Those with a severe systemic disease were on average 76.92 +- 9.71 (P b .001) years of age. The average seriousness of the 148 patients (39.3%) who did not receive advanced care in their prehospital care (as measured with the NISS) was 24.16 +- 7.62 points, while those who received vital support through medical intensive care ambulance was 29.20 +- 10.67 (P b

.001). These groups had a hospital RTS of 7.53 +- 0.83 and 6.62 +- 1.65, respectively (P b .001).

The average seriousness of the 38 patients (10%) who were intubated by outpatient medical teams (as measured with the NISS) was 39.66 +- 12.29 points, while patients who were not intubated were 25.82 +- 8.53 (P b .001). These groups had a hospital RTS of

4.06 +- 0.68 and 7.30 +- 1.12, respectively (P b .001).

Table 2

Profile of injury- related patients with NISS >=15, Major Trauma Registry of Navarre, 2011-2012

Variables

Measurement

Dead

Alive

P

Total patients

378

71 (18.8 %)

307 (81.2 %)

Age, mean (SD) Gender

Male

52.3 +- 22.7

262 (69.3 %)

67.1 +- 19.7

43 (16.4 %)

48.9 +- 22

219 (83.6 %)

b .001

.076

Female

Premorbid Conditions

116 (30.7 %)

28 (24.1 %)

88 (75.9 %)

b .001

Normal healthy patient

240 (63.5 %)

25 (10.4 %)

215 (89.6 %)

Mild systemic disease

102 (27 %)

30 (29.4 %)

72 (70.6 %)

Severe systemic disease Type of Injury

Blunt

36 (9.5 %)

361 (95.5 %)

16 (44.4 %)

70 (19.4 %)

20 (55.6 %)

291 (80.6 %)

.215

Penetrating Mechanism of Injury

Traffic

17 (4.5 %)

163 (43.1 %)

1 (5.9 %)

27 (16.6 %)

16 (94.1 %)

136 (83.4 %)

.035

Shot by handgun or stabbed by knife

12 (3.2 %)

1 (8.3 %)

11 (91.7 %)

Low energy fall

116 (30.7)

30 (25.9 %)

86 (74.1 %)

High energy fall

56 (14.8 %)

12 (21.4 %)

44 (78.6 %)

Other

Intention of Injury Accident (unintentional)

31 (8.2 %)

346 (91.5 %)

1 (3.2 %)

64 (18. 5 %)

30 (96.8 %)

282 (81.5 %)

.868

Self-inflicted

17 (4.5 %)

4 (23. 5 %)

13 (76.5 %)

Assault Physiological scores

RTS upon arrival of EMS personnel at scene, mean (SD)

15 (4 %)

7.31 +- 1.08

3 (20 %)

6.38 +- 1.61

12 (80 %)

7.52 +- 0.77

b .001

T-RTS upon arrival of EMS personnel at scene, mean (SD)

11.36 +- 1.26

10.31 +- 1.93

11.61 +- 0.88

b .001

RTS upon arrival in A&E/hospital, mean (SD)

6.98 +- 1.46

5.52 +- 1.78

7.32 +- 1.13

b .001

T-RTS upon arrival in A&E/hospital, mean (SD)

11.03 +- 1.62

9.39 +- 2

11.40+- 1.24

b .001

Anatomically based severity scores

ISS, mean (SD)

20.68 +- 8.86

27.97 +- 9.50

18.99 +- 7.80

b .001

NISS, mean (SD)

27.21 +- 9.88

36.61 +- 11.15

25.04 +- 8.16

b .001

Analytical parameters

Arterial Base Excess, mean (SD)

-4.16 +- 4.7 (157)

-6.13 +- 6.01

-3.53 +- 4.02

.016

Coagulation: INR, mean (SD)

1.22 +- 1 (360)

1.51 +- 1.25

1.15 +- 0.5

.021

Field Care Life Support

No Field Care or Basic Life Support

148 (39.3 %)

19 (12.8 %)

129 (87.2 %)

.017

Advanced Life Support, Physician Field Care

229 (60.7 %)

52 (22.7 %)

177 (77.3 %)

prehospital intubation

No

340 (89.9 %)

53 (15.6 %)

287 (84.4 %)

b .001

Yes

Times of response

Time from alarm to arrival at scene, mean (SD)

38 (10.1 %)

00:26 +- 00:28 (101)

18 (47.4 %)

00:23 +- 00:15

20 (52.6 %)

00:27 +- 00:30

.56

(101 patients; 277 missing values), median (IQR)

00:18 (00:10-00:31)

00:17 (00:13-00:31)

00:18 (00:10-00:34)

.962

Time from alarm to hospital arrival, mean (SD)

01:14 + 00:38 (179)

01:06+ 00:25

01:16+ 00:40

.056

(179 patients; 199 missing values), median (IQR)

01:05 (00:46-01:36)

01:03 (00:50-01:19)

01:10 (00:44-01:39)

.473

Time to first computed tomography (CT) scan, mean (SD)

01:04 + 00:59 (357)

00:45 + 00:40

01:08 + 01:01

b .001

(357 patients), median (IQR)

00:46 (00:29-01:14)

00:35 (00:20-00:58)

00:49(00:30-01:19)

.003

Time until first crucial emergency interventions, mean (SD)

02:43 + 02:07 (86)

02:45 +02:32

02:42 + 01:58

.92

(86 patients), median (IQR)

01:56 (01:10-03:35)

01:40 (01:08-03:03)

02:05(01:10-04:11)

.229

The average seriousness of the 63 patients who arrived at hospital more than 80 minutes after their accident (as measured with the NISS) was 26.97 +- 8.97 points, while that of patients who arrived at hospital less than 80 minutes after their accident (116 patients) was

30.25 +- 11.17(P = .05). These groups had a hospital RTS of 6.86 +-

1.53 and 6.52 +- 1.74, respectively (P = .20).

The average seriousness of the 210 patients who had their body computed tomographic (CT) scan taken more than 40 minutes after their arrival at hospital (as measured with the NISS) was 25.47 +- 9.1 points, while that of patients who had their body CT scan less than 40 minutes after their arrival (147 patients) was 29.80 +- 10.43 (P b

.001). These groups had a hospital RTS of 7.23 +- 1.27 and 6.64 +- 1.57, respectively (P b .001).

Ninety-two crucial emergency interventions (24.3% of all victims) were performed. These were as follows: 28 (30.4%) craniotomy; 19 (20.7%) damage control laparotomy; 14 (15.2%) Intracranial pressure

(ICP) device; 12 (13%) interventional radiology; 10 (10.9%) damage

control thoracotomy; 7 (7.6%) limb revascularisation and 2 (2.2%) extraperitoneal pelvic packing.

The average seriousness measured of patients (41 patients) who had surgery more than 120 minutes after their arrival at hospital (as measured with the NISS) was 33 +- 10.97 points, while that of patients were had surgery less than 120 minutes after their arrival (51 patients) was 33.42 +- 12.71 (P = .87). These groups had a hospital RTS of 6.49 +- and 5.90 +-, respectively (P = .13).

Of these patients, 250 (66.4%) made a satisfactory recovery; 25 (6.6%) survived with a moderate level of disability; 31 (8.2%) were left with severe disability; and 1 (0.7%) were left in a persistent vegetative state. 71 patients (18.8%) died.

Trauma prediction survival models

To determine which of the values used that measure the same thing were most suitable, the ROC curves were made and the area under the curve (AUC) of anatomic indices (ISS and NISS) and physiological indices (prehospital and hospital RTS and triage RTS) (Fig. 1) were calculated. The anatomic parameter with the largest AUC

was the NISS, while the physiological parameter with the largest AUC was the hospital RTS.

The model included variables with a P <= 10 in respect of mortality in the univariant study (Table 2). In the first phase, the following parameters were included: age (quantitative), sex, morbidity (healthy/with a chronic illness), Type of accident, NISS, Hospital RTS, arterial base excess, INR, prehospital care, intubation, time of arrival at hospital, and the realization of the body CT. The variable insertion “Enter” method was used.

The factors included in the equation were age, morbidity, NISS, and hospital RTS.

The construction of mortality prediction models was expressed as logit (P), where logit is the link function of the logistic regression model and represents the natural logarithm of the probability (P) of a positive outcome (survival/death). The logit (P) of the model was:

Model 1: Logit (P) = -5.72 – 0.07 (age) – 1.15 (morbidity)

– 1.33 (NISS) + 0.73 (hospital RTS).

A second model was built, in which age was a qualitative variable (up to and more than 59 years of age), morbidity, NISS (up to and more than 19), and hospital RTS (up to or more than 6.9). The logit (P) of the model was:

Model 2: Logit (P) = -6.24 – 1.53 (if more than 59 years of age)

– 1.10 (if illness) – 2.52 (if NISS is more than 19) + 2.77 (if hospital RTS is more than 7).

TRISS was calculated for each patient using the following formula: TRISS model Logit = -0.4499 – 1.7430 (if age is more than 54) + 0.8085 (RTS) -0.0835 (ISS) for Blunt injuries or Logit = -2.5355 – 1.1360 (if age is more than 54)* + 0.9934 (RTS) -0.0651 (ISS) for

Penetrating injuries.

Predicted Death rate = 1/(1 + eLogit): 13.30%.

Fig. 2 shows the ROC curves of the updated models.

In terms of odds ratio (OR) with its 95% confidence interval (CI), the results were as follows (i.e. Probability of death):

Fig. 1. Receiver operating characteristic curves of severity indices. AUC and 95% CI: prehospital T-RTS: 0.70 (0.63-0.78); prehospital RTS: 071 (0.63-0.78); hospital T-RTS: 0.79 (0.72- 0.85) and hospital RTS: 0.79 (0.73-0.86). ISS: 0.78 (0.72-0.85); NISS: 0.81 (0.76-0.86).

Fig. 2. Receiver operating characteristic curves of the models. AUC and CI 95%. M1: 0.93 (0.91-0.96); M2: 0.88 (0.85-0.92); TRISS: 0.87 (0.83-0.92).

Model 1: Age: 1.08 (1.05-1.11); comorbidity (yes): 2.02 (1.09-

6.83); NISS 1.14 (1.08-1.20) and hospital RTS: 0.48 (0.36-0.65)

Model 2: Age (if more than 59): 4.35 (1.59-11.91); comorbidity

(yes): 2.46 (1.03-6.68); NISS (more than 19): 11.88 (2.53-

55.78) and hospital RTS (less than 7): 16.26 (7.51-35.22).

Discussion

In the models generated in our study of the death or survival of polytrauma patient, we were interested in studying variables on three levels:

The fragility of patient, as a function of age and prior illnesses.
  • The seriousness of injuries, determined by the type of injury sustained measuring using NISS, and their physiological effects as measured using hospital RTS.
  • Medical action taken by the emergency health care system, and which we determined according to different response times: arrival at hospital, realization of first CT scan, and crucial surgery.

    Fragility

  • Age is a classic variable in the prediction of mortality in all of the models designed, whether as a continuous variable or in age groups [8,12,13]. In our case, it is also very clear that age is a determinant of mortality and the factor that leads to other variables in a univariant analysis, such as gender, being falsely linked to mortality.

    Comorbidity is studied less in serious trauma, with The Quebec Trauma Registry (QTR), which is based on a regionalized Trauma system with mandatory participation of all trauma centres and standardized inclusion criteria and coding practices, able to provide the benefit of the most experience. In its studies, the QTR designs models that improve the predictive capability of the TRISS and includes comorbidity as a dichotomous variable in the multivariable equation, and improves the predictive capability of their sample. [20- 22] These studies vary in their cut-off point in respect of the TRISS (55 years), setting it at 65 [20]. In our sample, the point that best stipulates the influence of age in mortality is 59 years of age, leaving

    us half-way between what is proposed in the TRISS and what is proposed by the QTR. An accident victim over the age of 59 is 4.35 times more likely to die than someone under 59. Morbidity is also an independent predictive variable, with a OR of 2.46.

    Seriousness of injuries

    Studies have been conducted in relation to the capacity for discrimination between RTS and Triage-RTS (T-RTS). Some authors have opted for T-RTS, given that its Predictive capacity is similar and it is easier to calculate, in particular at the scene of an accident [23]. In our case, both parameters show a predictive capacity that is very similar, provided that they are those taken in hospital, given that they are much higher than those measures in the prehospital environment. We believe that this is due to the fact that hospital parameters are taken at least 30 minutes after the prehospital parameters, and once the first Therapeutic measures have been taken. In addition, the very uncomfortable prehospital environment and the measurement of said parameters by untrained staff when care is provided by non-medical personnel (39.3% of cases) mean that the physiological repercussions of injuries to the patient in hospital are more reliable than before admission to hospital.

    Nevertheless, we believe that the measurement of prehospital T- RTS is important [23], as it provides the centre for coordination with information that is very important information for a correct allocation of resources, referrals to hospitals, and forewarning to accident and emergency departments. In our study, this variable is of great importance to mortality (OR of 0.5 for each point of RTS), slightly more so than other studies [24].

    Several authors have argued in favor of replacing ISS with NISS. Osler et al considered NISS to be easier to calculate and more predictive of survival than the ISS method; [25] the study by Lavoie et al confirmed their findings [26]. NISS will be equal to or greater than ISS for any given patient and appears to be a more accurate method for rating severely injured patients; [27] this is true specifically for patients with multiple head injuries [28]. The increased number of patients included by choosing NISS N 15 instead of ISS N 15 should be seen as an increase in ‘sensitivity’ without a loss of ‘specificity’ in an ideal definition of major trauma. In our study, we also demonstrated that predictive capacity is slightly better using NISS. The OR shows that each point in ISS increases the likelihood of death by 1.14 times (if the victim has in ISS score of more than 19, their chances of death increases by 11.9), slightly more than the score obtained by Lichtveld and Colls in 2007 [24].

    Public health response

    Outpatient response times

    It is important to note that according to many trauma experts, the first 60 minutes after an injury has occurred (referred to as the “golden hour”) is the most crucial period in saving lives [29]. After this period, the risk of death or injury severity rises significantly [29]. However, definitive references are generally not provided when this concept is discussed [30,31]. Even in the country where this term was coined, it has been shown that outpatient response times per se do not influence the chances of survival [32]. In our case, there is no difference in the time taken to reach the scene of an accident between patients that survive and those who do not (17 and 18 minutes, respectively) or in the time taken to reach a hospital (01:03 and 01:10, respectively).

    We believe that our emergency health care system attends to emergencies quickly enough to ensure that there are no significant differences between patients who survive and patients who die. Given that the accidents in questions are usually high-energy accidents and that it is not possible to determine the injuries sustained in outpatient care, all patients are transferred with the same speed. The conclusion

    is that hospital response times are not sufficiently fast to rule out any connection between these times and the mortality of these patients.

    Body CT scans

    The addition of whole-body CT scans into early trauma care has significantly increased the Probability of survival in patients with polytrauma. Whole-body CT scans are recommended as a standard diagnostic method during the early resuscitation phase for patients with polytrauma [33,34]. In 62% of all patients who required a CT scan, a whole-body CT scan was obtained in the study conducted by Fung Kon Jin et al [35]. We conducted CT scans on 95% of our patients; it was not possible to conduct scans on 10 patients who went into the operating theatre as an emergency without said scan. It is our view that in this regard, we followed the recommendations in a perfectly satisfactory manner.

    With regards to “CT acquisition times”, other studies record a median CT acquisition time of 76 min. (IQR: 52-115) [36]. In the study of Fung Kon Jin et al, the first CT session was completed in a median of 105 min after arrival. The complete radiological workup was finished in 114 min (median). Patients with ISS N 15 had a significantly shorter time until CT imaging and time until completion of CT imaging. In our study, we recorded a median time of 46 minutes, much less than the times found in the bibliography consulted. As with other studies, patients who were more severely injured (as measured using ISS) were transported more promptly to CT, resulting in faster diagnostic imaging [35]. For this reason, we found significant differences in the time taken to perform CT scans between patients who die and those who do not (35 and 49 minutes, respectively). However, this difference of 14 minutes is of great significance in terms the ability to make therapeutical decisions and the survival of the patient.

    We also wish to confirm the reliability of the times used, since they are obtained automatically by the hospital IT system and are not subject to interpretation by the doctor responsible for the case.

    Time required to perform crucial surgery

    Various experts recommend that the time between an injury being sustained and an operation should be minimized for patients in need of Urgent surgery to control bleeding and patients presenting with hemorrhagic shock and an identified source of bleeding [37]. Nevertheless, there are no data to indicate a clear correlation between response times in crucial surgical procedures and survival [37]. In our case, although the average time required for such intervention was shorter among patients who died (1 hour 40 minutes) than among those who survived (2 hours 5 minutes), no doubt due to the seriousness of the patients’ condition, these differences were not significant. Another aspect to be taken into account is that according to Utstein, crucial surgery to halt abdominal bleeding (laparotomy for damage control) and other surgery for monitoring purposes (the insertion of an intracranial pressure catheter) less able to cure the patient have equal status [16]. This could slant the assessment of response times in surgery and its correlation with survival rates. In our sample, in addition, the number of patients subject to surgery was small, limiting our ability to draw conclusions.

    Predictive capacity of models and comparison with TRISS

    The most reliable model is that generated by the MTN base (M1), which uses continuous quantitative variables such as age, RTS, and NISS, and a categorical variable (healthy or with a chronic illness). We obtained a very good predictive capacity (AUC: 0.93), which was reduced when we converted age, RTS, and NISS into dichotomous categories to make the model more intuitive from a clinical perspective (M2, AUC: 0.88).

    The results of the TRISS model are not as reliable as those of the model generated by the same sample, since it comes from major trauma outcome study in the United States. Demographic differences

    and differences in care, as well as differences in registers, could explain this difference.

    Different studies recommend the creation and use of local databases to determine the parameters that play a role in the mortality of, and improvement in the condition of, accident victims [20-22,24].

    Overall, we consider response times of the emergency healthcare system and outpatient care (time of arrival at hospital) and inpatient care (body CT scan and, where necessary, crucial surgery) to be most acceptable when compared to those referred to in other publications [30,33,34]. In any event, they do not play a role in patient mortality, as demonstrated in the logistic regression model, which excludes them from its predictions.

    We believe that trauma patients were managed well: those in a more Serious condition arrived at hospital first, although the differences between patients were not significant; and patients in a more serious condition received body CT scans and were operated on before those in a less serious condition. Death was a consequence of the fragility of the patient and the seriousness of their injuries. The response times of the health system did not play a role.

    Nevertheless, we must obviously strive to improve response times in the years to come, and compare them with current times in order to determine the relative importance of response times to rates of survival among our patients with greater accuracy.

    Limitations

    Our region is small both in terms of size and population and, as a result, the size of the simple is small when compared to large MTRs. As a result, the ability to stratify the sample according to age, RTS, ISS, or NISS is very limited, as this would reduce the statistical power of the sample. The dependent variable is survival or death; however, we have no information on the quality of life of said survivors. Increasing the amount of information on these patients is a concern that has recently been raised in a number of publications [20,24,38,39].

    Strengths

    All MTRs have limitations. To ensure the quality and integrity of these MTRs, there must be a central organization responsible for adding, validating, and analyzing information. The MTRN is a comprehensive population register–the first of its kind in Spain– that guarantees the quality of this information, as Navarre is a small region with close control of patients receiving care in hospitals. This quality is guaranteed by the computerization of all records and the prospective collection of variables with a data manager that avoids the presence of missing values.

    Conclusion

    There is no perfect model for predicting patient survival in the MT on a global level. Therefore, current predictive systems must be required to make appropriate adjustments to the populations they describe, and be applicable to similar populations. For this reason, it is important that all autonomous regions develop their own MTRs and analyze their data. This would enable them to predict mortality and analyze unusual cases, compare different therapies, and provide additional support for Clinical decisions.

    The analysis of MTR data provides a methodology that has been adapted to improve quality, assistance to establish prevention programs and, in short, is a valuable benchmark tool for reference and contrast in trauma research.

    With regards to the MTRN, we have designed a model that can predict the final outcome in terms of survival and determined by the seriousness of injuries measures according to NISS and hospital RTS and the fragility of patients measures according to age and comorbidity with great accuracy. The response times of the health system did not influence these results.

    Further research is required in order to learn about the quality of life of surviving patients.

    Acknowledgments

    The authors would like to thank the emergency department doctors and data managers from centres that participated in the research.

    We would also like to thank Silvia Ayerra and Imanol Pinto for their extremely valuable contribution to the implementation of the IT application; and Berta Ibanez and Koldo Cambra, for their statistical and methodological assistance.

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