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Prehospital Trauma Life Support (PHTLS) training of ambulance caregivers and impact on survival of trauma victims
Resuscitation 83 (2012) 1259–1264
Contents lists available at SciVerse ScienceDirect
Resuscitation journal homepage: www.elsevier.com/locate/resuscitation
Clinical paper
Prehospital Trauma Life Support (PHTLS) training of ambulance caregivers and impact on survival of trauma victims夽 Jakob Johansson a,b,∗ , Hans Blomberg a,b , Bodil Svennblad c , Lisa Wernroth c , Håkan Melhus e , Liisa Byberg a,c , Karl Michaëlsson c,d , Rolf Karlsten a , Rolf Gedeborg a,c a
Department of Surgical Sciences, Anaesthesiology and Intensive Care, Uppsala University, Uppsala, Sweden Centre of Emergency Medicine, Uppsala University Hospital, Uppsala, Sweden c Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden d Department of Surgical Sciences, Orthopedics, Uppsala University, Uppsala, Sweden e Department of Medical Sciences, Uppsala University, Uppsala, Sweden b
a r t i c l e
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Article history: Received 21 November 2011 Received in revised form 23 January 2012 Accepted 10 February 2012 Keywords: Trauma Advanced life support Prehospital Trauma Life Support Survival
a b s t r a c t Background: The Prehospital Trauma Life Support (PHTLS) course has been widely implemented and approximately half a million prehospital caregivers in over 50 countries have taken this course. Still, the effect on injury outcome remains to be established. The objective of this study was to investigate the association between PHTLS training of ambulance crew members and the mortality in trauma patients. Methods: A population-based observational study of 2830 injured patients, who either died or were hospitalized for more than 24 h, was performed during gradual implementation of PHTLS in Uppsala County in Sweden between 1998 and 2004. Prehospital patient records were linked to hospital-discharge records, cause-of-death records, and information on PHTLS training and the educational level of ambulance crews. The main outcome measure was death, on scene or in hospital. Results: Adjusting for multiple potential confounders, PHTLS training appeared to be associated with a reduction in mortality, but the precision of this estimate was poor (odds ratio, 0.71; 95% confidence interval, 0.42–1.19). The mortality risk was 4.7% (36/763) without PHTLS training and 4.5% (94/2067) with PHTLS training. The predicted absolute risk reduction is estimated to correspond to 0.5 lives saved annually per 100,000 population with PHTLS fully implemented. Conclusions: PHTLS training of ambulance crew members may be associated with reduced mortality in trauma patients, but the precision in this estimate was low due to the overall low mortality. While there may be a relative risk reduction, the predicted absolute risk reduction in this population was low. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Trauma is the leading cause of death among persons below 60 years of age.1 It is a well-established belief that optimal treatment in the early phase after trauma has a major impact on mortality.2 Therefore, over the years, raising the educational level among prehospital caregivers and the implementation of specific educational programs that target trauma care have been two widely adopted strategies aimed at improving the outcome for trauma victims.
夽 A Spanish translated version of the summary of this article appears as Appendix in the final online version at doi:10.1016/j.resuscitation.2012.02.018. ∗ Corresponding author at: Department of Surgical Sciences – Anaesthesiology & Intensive Care, Uppsala University Hospital, S-751 85 Uppsala, Sweden. Tel.: +46 18 6110000; fax: +46 18 559357. E-mail address: [email protected] (J. Johansson). 0300-9572/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.resuscitation.2012.02.018
This strategy has high face validity, but the underlying evidence is poor.3 In the late 1970s, the American College of Surgeons Committee on Trauma developed the Advanced Trauma Life Support (ATLS) course for physicians.4,5 Although implemented worldwide, there is still no strong evidence that ATLS lowers mortality in trauma victims.6,7 According to a recent study, ATLS-training might even impair outcome.8 The Prehospital Trauma Life Support (PHTLS) program was introduced in 1983 to integrate prehospital trauma care with the ATLS program.9 PHTLS has been recognized as one of the leading educational programs for prehospital emergency trauma care and approximately half a million prehospital caregivers in over 50 countries have taken this course.9 However, the scientific support for improved patient outcome is limited, and there is no evidence to recommend advanced life-support (ALS) training for ambulance crews.10–12 Substantial effort and money are put into the PHTLS training program and
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there is an obvious need to evaluate the possible benefits for patients. The aim of this study was to investigate the association between PHTLS training of ambulance crew members and the outcome in trauma patients. 2. Methods
There were no major changes in the standard of pre-hospital trauma care or equipment used during the study period. All ambulance crew members in the organisation were trained and authorized to perform trauma care according to international standard treatment, except from endotracheal intubation. A PHTLS certification did not change the authorization to use any equipment or perform any specific intervention.
2.1. Source population
2.5. Outcome
Uppsala County is an administrative region for health care in Sweden, with a population of 302,564 and a population density of 43 inhabitants/km2 in 2004. There are two hospitals in the county; Uppsala University Hospital, a 1100-bed tertiary care facility, and Enköping Hospital, a small local hospital handling only minor trauma.
Prehospital injury deaths were defined as autopsied injury deaths not associated with a hospital admission.19 Only prehospital deaths with documented involvement of ambulance services were included. Hospital death was defined as death during a hospital stay with a principal discharge diagnosis indicating injury. The composite outcome of either prehospital or hospital death was studied.
2.2. Emergency Medical Service (EMS)-system The ambulance staff consisted of registered nurses and emergency medical technician (EMT) equivalents (nursing assistants with special ambulance training). During the study period the number of registered nurses employed as ambulance crew members increased rapidly. Prior to and during the study period all ambulance crew members were, as part of their regular skill practice, annually trained and authorized to provide ALS in medical emergencies and trauma, with the exception of endotracheal intubation (only performed in cardiac arrest) and chest drainage. 2.3. Study population All primary incident hospital admissions for injury in the county of Uppsala from 1998 through 2004 were extracted from the Swedish National Patient Registry (NPR).13 Injury deaths, where the underlying cause of death was V01-Y36 according to ICD-10,14 were extracted from the Cause of Death Registry (CDR). These codes include all external causes of injury except those referring to “Complications of medical and surgical care”, “Sequelae of external causes of morbidity and mortality”, and “Supplementary factors related to causes of morbidity and mortality classified elsewhere”. These datasets were combined using the unique personal identification number for person-based linkage.15 Admissions resulting in a hospital stay of one day or less and discharge alive were excluded to reduce the number of minor injuries included.16 Dispatch information, prehospital time intervals, medical information, and the identity of the ambulance crew members were added from prehospital electronic patient records.17 For each individual ambulance crew member the following information was collected: years of employment in health care services and in ambulance services; the date for exam as a registered nurse and the date for PHTLS training. The final dataset consisted of 2830 injury events with complete data (Fig. 1). The study was approved by the regional Human Ethics Committee. 2.4. Exposure The PHTLS program is a standardized curriculum for prehospital caregivers. The core component is a 16-h course with a mixture of lectures and interactive skill stations.18 If at least one ambulance crew member was PHTLS certified (had passed the final examination in PHTLS), the injured patient was considered exposed. Exposure status was not dependent on whether the certified crew member was in charge of the crew or not. The time elapsed since PHTLS certification was considered in a sensitivity analysis. No refresher courses were performed during the study period.
2.6. Possible confounders The International Classification of disease Injury Severity Score (ICISS) was calculated based on diagnosis-specific survival probabilities for individual injury ICD-10 codes.20–22 For descriptive purposes, ICISS was categorized as critical (0–0.219), severe (0.220–0.354), serious (0.355–0.664), moderate (0.665–0.940), or minor (0.941–1.0). Injury severity estimates based on the Revised Trauma Score (RTS) were also available.23 Each victim’s injuries were categorized according to the injury mortality diagnosis matrix for ICD-10.24 Five categories of major injury regions (head, spinal cord, thorax, abdomen, and pelvis) were defined from the matrix. Causes of injury were classified according to the matrix developed by the National Center for Health Statistics, Centers for Disease Control, USA.25 Data was collapsed to three categories: traffic injuries, falls, and other injuries. The basic educational level (nurse or EMT equivalent) and employment time of the ambulance crew members were also considered as potential confounders. Exposure status was determined from the highest educational level and the longest work experience in the ambulance crew. To account for a possible period effect (such as changes in trauma care over time during the study period), the calendar year of the injury was included in the multivariable models. The patients’ age and sex were also considered as potential confounders. 2.7. Statistics Multivariable logistic regression analysis was used to model the composite outcome of prehospital or hospital death using all the possible confounders described above. Collinearity was assessed from variance inflation factors. Both age and calendar year of injury were entered as linear effects in the models based on inspection of a logit plot of each variable. ICISS was used after logit transformation.26 Effect-measure modification was evaluated by including product terms between the exposure variable and injury severity (ICISS) and year, respectively. The multivariable logistic regression model was also used to calculate the predicted mortality for each injury event. The difference in mean predicted mortality between the PHTLS group and the non-PHTLS group estimated the absolute risk reduction. Population-averaged models using generalized estimation equations (GEE) were used to handle correlations from ambulance crews appearing multiple times in the dataset and patients appearing multiple times in the study population.27,28 The number of lives potentially saved annually was estimated by the proportion of eligible patients corresponding to the estimated absolute risk
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5235 severe injuries (hospitalized > 1 day or dead) with documented participation of EMS
483 without a valid personal identification number in EMS record
Information on educational level among ambulance personnel added
1129 with incomplete information to determine crew PHTLS certification status
3623 severe injuries (hospitalized > 1 day or dead) with documented participation of EMS and information on ambulance crew’s PHTLS certification status
793 without complete information on both crew members’ identity and PHTLS certification status and covariates
2830 incident injuries (hospitalized > 1 day or dead) with documented participation of EMS and information on identity and PHTLS certification status for both members of the ambulance crew and all covariates in the multivariable model
Fig. 1. Flow diagram for selection of the study population. EMS: Emergency Medical Service.
reduction. All analyses were done as complete subjects analyses. As a sensitivity analysis, multiple imputation using predictive mean matching (aregImpute) that fits separate flexible additive imputation models, was also applied.29,30 Missing data for PHTLS status and employment time were imputed based on mortality, PHTLS status, educational level (nurse/EMT), calendar year, and employment time. Ten bootstrap samples were generated. The statistical packages SAS version 9 (SAS Institute Inc., Cary, NC, USA) and R version 2.9.2 (R Foundation for Statistical Computing, Vienna, Austria) were used for data management and statistical analyses.
0.41–1.25), with a corresponding predicted absolute risk reduction of 0.2%. There was no indication of serious collinearity in the regression model, with the highest variance inflation factor being 1.78. This analysis, however, violates the assumption of independent observations since the same ambulance crews were involved in many cases of injury and also the same patients occurred multiple times in the study population. We analytically considered such possible correlations (supplementary data: Table 4). Results from these analyses were very similar to those from using standard logistic regression. The choice of variance structure did not affect the result and, in general, the correlations were minimal (<0.1).
3. Results 3.3. Subgroup analyses 3.1. Cohort characteristics During the 7-year study period, EMSs responded to 2830 injury events with complete data for the analyses (Fig. 1). The proportion of injury events handled by ambulance crew members with PHTLS training increased over time (Fig. 2). There were no major differences in patient characteristics; however, ambulance crews in the PHTLS group had more years of employment in ambulance services, compared to the non-PHTLS group (Table 1). There was no apparent overall difference in response time, on-scene time, or transport time between the two groups.
A product term for PHTLS and injury severity in the logistic regression model indicated the possible presence of effect-measure modification (likelihood ratio test P = 0.08), but analysis stratified for injury severity did not indicate any substantial influence on the estimated OR for PHTLS (Table 2). Exclusion of injuries caused by falls strengthened the estimated protective effect of PHTLS to OR = 0.54 (Table 2) but with poor precision (95% CI, 0.13–2.41). A short interval from PHTLS training resulted in a stronger protective association with mortality (Table 2). The precision in these estimates was poor.
3.2. Relative and absolute mortality risk
3.4. Characteristics of excluded patients
The mortality was 4.7% (36/763) without PHTLS training and 4.5% (94/2067) with PHTLS training. The crude (unadjusted) odds ratio (OR) for mortality was 0.96 [95% confidence interval (CI), 0.66–1.44]. The adjusted OR for mortality was 0.71 (95% CI,
There were no major differences in age, sex, cause of injury, injury severity, major organs injured, or mortality when characteristics among the excluded cases were compared to the study population (supplementary data: Table 3). In electronic EMS patient
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B. PHTLS trained present
100 80 60 40
Proportion of injury events (%)
80 60 40
Registered nurse EMT equivalent
0
0
20
At least one with PHTLS No one with PHTLS
20
Proportion of injury events (%)
100
A. Highest educational level present
1998
2000
2002
2004
1998
2000
2002
2004
Fig. 2. Change during the study period in the distribution of highest educational level in the ambulance crews (left panel) and the proportion of injury events where at least one PHTLS-trained individual was present (right panel). EMT equivalent, Emergency Medical Technician equivalent (nursing assistants with special ambulance training). Table 1 Baseline characteristics of the Study Cohort. N
Age, % (N) 0–14 15–24 25–44 45–64 65–74 ≥75 Sex, % (N) Male ICISS stratified, % (N) Critical Severe Serious Moderate Minor Head injury, % (N) Spinal cord injury, % (N) Thoracic injury, % (N) Abdominal injury, % (N) Pelvic injury, % (N) No. of major injury regions, % (N) None One Two Three or more Cause of injury, % (N) Fall Traffic Other Highest crew member education, % (N) EMT equivalent Registered nurse Shortest interval from PHTLS training in the ambulance crew (years), median (IQR) Maximum years of individual experience in EMS, median (IQR) Response time minutes, median (IQR) On-scene time minutes, median (IQR) Transport time minutes, median (IQR) Prehospital mortality, % (N) Hospital mortality, % (N)
At least one member of the ambulance crew with PHTLS certification (N = 2067)
No member of the ambulance crew with PHTLS certification (N = 763)
2830 2 (39) 4 (93) 11 (219) 18 (362) 11 (220) 55 (1134)
1 (9) 5 (37) 10 (78) 11 (86) 11 (85) 61 (468)
37 (759)
38 (292)
1 (23) 1 (20) 5 (96) 48 (1000) 45 (928) 9 (194) 0 (6) 6 (122) 1 (27) 6 (118)
0 (2) 1 (7) 4 (30) 50 (379) 45 (345) 9 (71) 0 (0) 6 (42) 1 (7) 6 (46)
80 (1653) 18 (372) 2 (33) 0 (9)
80 (612) 18 (140) 1 (7) 1 (4)
76 (1570) 11 (234) 13 (263)
80 (607) 9 (71) 11 (85)
32 (660) 68 (1407)
75 (574) 24 (189)
2830 2830
2830 2830 2830 2830 2830 2830
2830
2830
2067 2830 2563 2497 2059 2830 2830
1.4 (0.7–2.3) 18 (15–22) 9 (5–15) 11 (8–16) 11 (7–25) 1% (20) 4% (74)
NA 16 (12–19) 9 (5–15) 10 (7–14) 12 (7–24) 0% (2) 4% (34)
ICISS, International Classification of disease Injury Severity Score; IQR, interquartile range; EMS, Emergency Medical Service; EMT, Emergency Medical Technician.
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Table 2 Complementary analysesa , using subgroups and graded exposures, of the association between PHTLS certification and mortality. Subgroup
Number of patients (no PHTLS/PHTLS)
Number of deaths (no PHTLS/PHTLS)
Crude (unadjusted) OR (95% CI)
Adjusted OR (95% CI)
Traffic and other injury, excluding falls Effect-measure modification by injury severity More severe injury (ICISS < 0.665) Less severe injury (ICISS ≥ 0.665) Length of the interval since PHTLS certification (shortest in the ambulance crew) ≤1 year >1 year
156/497
7/29
1.32 (0.60–3.33)
0.54 (0.13–2.41)
39/139 724/1928 763/2067
9/35 27/59 36/94
1.12 (0.50–2.71) 0.81 (0.52–1.31)
0.80 (0.20–3.44) 0.77 (0.28–2.04)
0.75 (0.44–1.24) 1.09 (0.72–1.66)
0.66 (0.36–1.20) 0.76 (0.42–1.41)
OR, odds ratio; CI, confidence interval; ICISS, International Classification of disease Injury Severity Score. a Multivariable logistic regression model also included age, sex, injury severity (ICISS), cause of injury (all transport/fall/other), study year, head injury, spinal cord injury, thoracic injury, abdominal injury, pelvic injury, and years of employment in ambulance service.
care reports without a valid personal identification number for the patient, the median RTS was 8, identical to the median RTS among those with a complete personal identification number. Multiple imputation of the major offending variables – PHTLS status and employment time – did not substantially change the estimated association or the precision in the estimate (supplementary data: Table 4).
4. Discussion This population-based observational study indicates that PHTLS training of ambulance crew members may be associated with approximately 30% relative reduction of mortality in trauma victims. However, with the low overall mortality in this population, the precision of this estimate was low making the interpretation of this possible association difficult. The predicted absolute risk reduction is estimated to correspond to 0.5 lives saved annually per 100,000 population with PHTLS fully implemented. Despite decades of widespread implementation of educational programs such as ATLS and PHTLS, there is scant scientific evidence for beneficial effects in trauma outcome.6,7,12 Today, a substantial proportion of ambulance crews include an ALS-trained member.31 While there are studies on conditions other than trauma indicating that ALS training and higher educational levels among ambulance crews improves outcomes,32,33 there is little support for such an association in trauma care.34–36 Three conditions provided a rare opportunity to perform a comprehensive observational study of the association between PHTLS training and outcome: (a) the introduction of prehospital electronic medical records, (b) the subsequent gradual implementation of PHTLS, and (c) exact record linkage of patient injury data in national healthcare databases. We could associate each injury case with the educational level of the ambulance crew and adjust for each crew’s years of experience and basic educational level. We further controlled for injury cause, type and severity, and also the year of the study in which the exposure/incident occurred in order to account for a possible period effect. The population-based design and good control of confounding – and especially a potential period effect – support the findings in this study. Exclusion of observations with incomplete data was unfortunate; however, the comparable characteristics of these patients to the study population, the small impact on the estimated association from imputation of missing values, and the absence of apparent effect-measure modification from injury severity did not raise any grave concern of a selection bias. A particular challenge for this type of study is that the same ambulance crews appear several times in the data, and similarly a patient may also present repeated times. Ignoring such correlations within the data may lead to incorrect statistical inference and
biased estimates of variance. However, methods that do not utilize all available data may be inefficient. Analytically accounting for these showed that their impact on the estimated associations was minimal. Despite good control of several important confounders, limited causal inference can be made from a single observational study. Largely due to the relatively low overall mortality in our study, the precision of our estimates was low. We can only speculate why PHTLS training appeared to be associated with reduced mortality after trauma. According to the PHTLS concept, endotracheal intubation is the preferred method of airway control, although several studies dispute the benefits of this procedure.36–38 In our study, no member of the ambulance crew was authorized to perform endotracheal intubation on trauma patients, irrespective of PHTLS training. Thus, acquisition of this specific airway management skill could not explain the lowered mortality seen in our study. In fact, in this EMS system, ALS interventions related to PHTLS had already been implemented before PHTLS education, with the exception of endotracheal intubation. Therefore, the observed association between PHTLS education and reduced mortality was most likely not due to the application of individual ALS interventions. It has been demonstrated that PHTLS training improves adherence to established priorities and management of the trauma victim in a structured approach, which could possibly shorten time to definitive care.12,39 In our study though, response time, on-scene time, and transport time did not appear influenced by PHTLS training. The impact of the structured approach, including assessment and setting priorities that the PHTLS concept is focused on, is difficult to measure and could possibly explain the association with reduced mortality. The percentage of ambulance crews trained in PHTLS went from approximately 15% to almost 90% in a relatively short time period. This focused educational effort could possibly create a Hawthornelike effect, making the PHTLS-trained personnel particularly prone to perform better in general, regardless of the content of the course. However, the implementation of PHTLS in the organization could possibly also have influenced ambulance crew members not trained in PHTLS in a positive direction, thus also improving outcome in the control group. Concerning the ability to generalize our results to other settings, it is notable that the PHTLS course is highly standardized. However, the basic educational level of prehospital crews varies between organizations and therefore other results could possibly be achieved in a similar study in another setting such as a paramedic or EMT-based system. The study population represents the population admitted to a general hospital, but is not representative of a selected trauma-center population. This is reflected by the female dominance and large number of falls causing minor or moderate injuries.
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Excluding falls from the study population, i.e. mainly elderly patients with hip fractures, as expected strengthened the association between PHTLS and reduced mortality. In the subgroup of traffic crash victims there was a signal of stronger association between PHTLS training and reduced mortality. It is therefore possible that both the relative and especially the absolute risk reduction might be higher in a population with higher prevalence of multiply injured patients. The relation with injury severity is also of high interest, but likely complex. One may expect to find the strongest effect of PHTLS on injuries with intermediate severity. With low injury severity mortality is very low and with high injury severity mortality might be inevitable. However, due to the limited mortality in the study population we are unable to analytically pursue this in detail. The presented estimates from all the subgroup analyses are highly uncertain and must be interpreted with great caution. 5. Conclusion This population-based study of lethal or hospitalized injuries indicates that PHTLS training of ambulance crew members may be associated with reduced mortality in trauma patients, but the precision in this estimate was low due to the overall low mortality. Although our study indicates a relative risk reduction, the predicted absolute risk reduction in this population was low. Conflicts of interest Dr. Blomberg reported that he has been an instructor in PHTLS since 2002. No other conflicts of interest were reported. Acknowledgments This project was funded by Uppsala University and the Laerdal Foundation for Acute Medicine. No funding organization or sponsor was involved in the design and conduct of the study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.resuscitation.2012.02.018. References 1. Krug EG, Sharma GK, Lozano R. The global burden of injuries. Am J Public Health 2000;90:523–6. 2. Trunkey DD, Lim Jr RC, Blaisdell FW. Traumatic injury. A health care crisis. West J Med 1974;120:92–4. 3. Lerner EB, Moscati RM. The golden hour: scientific fact or medical “urban legend”? Acad Emerg Med 2001;8:758–60. 4. American College of Surgeons. Atls advanced trauma life support. Lippincott Williams & Wilkins; 2006. 5. Collicott PE, Hughes I. Training in advanced trauma life support. JAMA 1980;243:1156–9. 6. Soreide K. Three decades (1978–2008) of advanced trauma life support (atls) practice revised and evidence revisited. Scand J Trauma Resusc Emerg Med 2008;16:19. 7. Jayaraman S, Sethi D. Advanced trauma life support training for hospital staff. Cochrane Database Syst Rev 2009:CD004173.
8. Drimousis PG, Theodorou D, Toutouzas K, et al. Advanced trauma life support certified physicians in a non trauma system setting: is it enough? Resuscitation 2011;82:180–4. 9. Salomone JP, Pons PT. Phtls prehospital trauma life support; 2007. 10. Ali J, Adam RU, Gana TJ, Williams JI. Trauma patient outcome after the prehospital trauma life support program. J Trauma 1997;42:1018–21 [discussion 1021–1012]. 11. Arreola-Risa C, Mock C, Herrera-Escamilla AJ, Contreras I, Vargas J. Costeffectiveness and benefit of alternatives to improve training for prehospital trauma care in mexico. Prehosp Disaster Med 2004;19:318–25. 12. Jayaraman S, Sethi D. Advanced trauma life support training for ambulance crews. Cochrane Database Syst Rev 2010:CD003109. 13. Gedeborg R, Engquist H, Berglund L, Michaelsson K. Identification of incident injuries in hospital discharge registers. Epidemiology 2008;19:860–7. 14. World Health Organization. International statistical classification of diseases and health related problems icd-10. Geneva: World Health Organization; 2005. 15. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A. The swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol 2009;24:659–67. 16. Cryer C, Langley JD. Studies need to make explicit the theoretical and case definitions of injury. Inj Prev 2008;14:74–7. 17. Karlsten R, Sjoqvist BA. Telemedicine and decision support in emergency ambulances in uppsala. J Telemed Telecare 2000;6:1–7. 18. National association of emergency medical technicians (naemt).. 19. Gedeborg R, Chen L, Thiblin I, et al. Prehospital injury deaths—strengthening the case for prevention: Nationwide cohort study. J Trauma; doi:10.1097/TA.0b013e3182288272, in press. 20. Clark DE, Winchell RJ. Risk adjustment for injured patients using administrative data. J Trauma 2004;57:130–40 [discussion 140]. 21. Osler T, Rutledge R, Deis J, Bedrick E. Iciss: an international classification of disease-9 based injury severity score. J Trauma 1996;41:380–6 [discussion 386–388]. 22. Stephenson S, Henley G, Harrison JE, Langley JD. Diagnosis based injury severity scaling: investigation of a method using australian and new zealand hospitalisations. Inj Prev 2004;10:379–83. 23. Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the trauma score. J Trauma 1989;29:623–9. 24. Fingerhut LA, Warner M. The icd-10 injury mortality diagnosis matrix. Inj Prev 2006;12:24–9. 25. Recommended framework for presenting injury mortality data. MMWR Recomm Rep 1997;46:1–30. 26. Moore L, Lavoie A, Bergeron E, Emond M. Modeling probability-based injury severity scores in logistic regression models: the logit transformation should be used. J Trauma 2007;62:601–5. 27. Carey V, Zeger SL, Diggle P. Modelling multivariate binary data with alternating logistic regressions. Biometrica 1993;80:517–26. 28. Miglioretti DL, Heagerty PJ. Marginal modeling of nonnested multilevel data using standard software. Am J Epidemiol 2007;165:453–63. 29. Harrell Jr FE. Regression modeling strategies. New York: Springer; 2001. 30. Marshall A, Altman DG, Holder RL. Comparison of imputation methods for handling missing covariate data when fitting a cox proportional hazards model: a resampling study. BMC Med Res Methodol 2010;10:112. 31. Bulger EM, Nathens AB, Rivara FP, MacKenzie E, Sabath DR, Jurkovich GJ. National variability in out-of-hospital treatment after traumatic injury. Ann Emerg Med 2007;49:293–301. 32. Woodall J, McCarthy M, Johnston T, Tippett V, Bonham R. Impact of advanced cardiac life support-skilled paramedics on survival from out-of-hospital cardiac arrest in a statewide emergency medical service. Emerg Med J 2007;24:134–8. 33. Stiell IG, Spaite DW, Field B, et al. Advanced life support for out-of-hospital respiratory distress. N Engl J Med 2007;356:2156–64. 34. Haas B, Nathens AB. Pro/con debate: is the scoop and run approach the best approach to trauma services organization? Crit Care 2008;12:224. 35. Isenberg DL, Bissell R. Does advanced life support provide benefits to patients?: a literature review. Prehosp Disaster Med 2005;20:265–70. 36. Stiell IG, Nesbitt LP, Pickett W, et al. The opals major trauma study: impact of advanced life-support on survival and morbidity. CMAJ 2008;178:1141–52. 37. Eckstein M, Chan L, Schneir A, Palmer R. Effect of prehospital advanced life support on outcomes of major trauma patients. J Trauma 2000;48:643–8. 38. Bulger EM, Maier RV. Prehospital care of the injured: what’s new. Surg Clin North Am 2007;87:37–53, vi. 39. Ali J, Adam R, Josa D, et al. Effect of basic prehospital trauma life support program on cognitive and trauma management skills. World J Surg 1998;22:1192–6.
Contents lists available at SciVerse ScienceDirect
Resuscitation journal homepage: www.elsevier.com/locate/resuscitation
Clinical paper
Prehospital Trauma Life Support (PHTLS) training of ambulance caregivers and impact on survival of trauma victims夽 Jakob Johansson a,b,∗ , Hans Blomberg a,b , Bodil Svennblad c , Lisa Wernroth c , Håkan Melhus e , Liisa Byberg a,c , Karl Michaëlsson c,d , Rolf Karlsten a , Rolf Gedeborg a,c a
Department of Surgical Sciences, Anaesthesiology and Intensive Care, Uppsala University, Uppsala, Sweden Centre of Emergency Medicine, Uppsala University Hospital, Uppsala, Sweden c Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden d Department of Surgical Sciences, Orthopedics, Uppsala University, Uppsala, Sweden e Department of Medical Sciences, Uppsala University, Uppsala, Sweden b
a r t i c l e
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Article history: Received 21 November 2011 Received in revised form 23 January 2012 Accepted 10 February 2012 Keywords: Trauma Advanced life support Prehospital Trauma Life Support Survival
a b s t r a c t Background: The Prehospital Trauma Life Support (PHTLS) course has been widely implemented and approximately half a million prehospital caregivers in over 50 countries have taken this course. Still, the effect on injury outcome remains to be established. The objective of this study was to investigate the association between PHTLS training of ambulance crew members and the mortality in trauma patients. Methods: A population-based observational study of 2830 injured patients, who either died or were hospitalized for more than 24 h, was performed during gradual implementation of PHTLS in Uppsala County in Sweden between 1998 and 2004. Prehospital patient records were linked to hospital-discharge records, cause-of-death records, and information on PHTLS training and the educational level of ambulance crews. The main outcome measure was death, on scene or in hospital. Results: Adjusting for multiple potential confounders, PHTLS training appeared to be associated with a reduction in mortality, but the precision of this estimate was poor (odds ratio, 0.71; 95% confidence interval, 0.42–1.19). The mortality risk was 4.7% (36/763) without PHTLS training and 4.5% (94/2067) with PHTLS training. The predicted absolute risk reduction is estimated to correspond to 0.5 lives saved annually per 100,000 population with PHTLS fully implemented. Conclusions: PHTLS training of ambulance crew members may be associated with reduced mortality in trauma patients, but the precision in this estimate was low due to the overall low mortality. While there may be a relative risk reduction, the predicted absolute risk reduction in this population was low. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Trauma is the leading cause of death among persons below 60 years of age.1 It is a well-established belief that optimal treatment in the early phase after trauma has a major impact on mortality.2 Therefore, over the years, raising the educational level among prehospital caregivers and the implementation of specific educational programs that target trauma care have been two widely adopted strategies aimed at improving the outcome for trauma victims.
夽 A Spanish translated version of the summary of this article appears as Appendix in the final online version at doi:10.1016/j.resuscitation.2012.02.018. ∗ Corresponding author at: Department of Surgical Sciences – Anaesthesiology & Intensive Care, Uppsala University Hospital, S-751 85 Uppsala, Sweden. Tel.: +46 18 6110000; fax: +46 18 559357. E-mail address: [email protected] (J. Johansson). 0300-9572/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.resuscitation.2012.02.018
This strategy has high face validity, but the underlying evidence is poor.3 In the late 1970s, the American College of Surgeons Committee on Trauma developed the Advanced Trauma Life Support (ATLS) course for physicians.4,5 Although implemented worldwide, there is still no strong evidence that ATLS lowers mortality in trauma victims.6,7 According to a recent study, ATLS-training might even impair outcome.8 The Prehospital Trauma Life Support (PHTLS) program was introduced in 1983 to integrate prehospital trauma care with the ATLS program.9 PHTLS has been recognized as one of the leading educational programs for prehospital emergency trauma care and approximately half a million prehospital caregivers in over 50 countries have taken this course.9 However, the scientific support for improved patient outcome is limited, and there is no evidence to recommend advanced life-support (ALS) training for ambulance crews.10–12 Substantial effort and money are put into the PHTLS training program and
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there is an obvious need to evaluate the possible benefits for patients. The aim of this study was to investigate the association between PHTLS training of ambulance crew members and the outcome in trauma patients. 2. Methods
There were no major changes in the standard of pre-hospital trauma care or equipment used during the study period. All ambulance crew members in the organisation were trained and authorized to perform trauma care according to international standard treatment, except from endotracheal intubation. A PHTLS certification did not change the authorization to use any equipment or perform any specific intervention.
2.1. Source population
2.5. Outcome
Uppsala County is an administrative region for health care in Sweden, with a population of 302,564 and a population density of 43 inhabitants/km2 in 2004. There are two hospitals in the county; Uppsala University Hospital, a 1100-bed tertiary care facility, and Enköping Hospital, a small local hospital handling only minor trauma.
Prehospital injury deaths were defined as autopsied injury deaths not associated with a hospital admission.19 Only prehospital deaths with documented involvement of ambulance services were included. Hospital death was defined as death during a hospital stay with a principal discharge diagnosis indicating injury. The composite outcome of either prehospital or hospital death was studied.
2.2. Emergency Medical Service (EMS)-system The ambulance staff consisted of registered nurses and emergency medical technician (EMT) equivalents (nursing assistants with special ambulance training). During the study period the number of registered nurses employed as ambulance crew members increased rapidly. Prior to and during the study period all ambulance crew members were, as part of their regular skill practice, annually trained and authorized to provide ALS in medical emergencies and trauma, with the exception of endotracheal intubation (only performed in cardiac arrest) and chest drainage. 2.3. Study population All primary incident hospital admissions for injury in the county of Uppsala from 1998 through 2004 were extracted from the Swedish National Patient Registry (NPR).13 Injury deaths, where the underlying cause of death was V01-Y36 according to ICD-10,14 were extracted from the Cause of Death Registry (CDR). These codes include all external causes of injury except those referring to “Complications of medical and surgical care”, “Sequelae of external causes of morbidity and mortality”, and “Supplementary factors related to causes of morbidity and mortality classified elsewhere”. These datasets were combined using the unique personal identification number for person-based linkage.15 Admissions resulting in a hospital stay of one day or less and discharge alive were excluded to reduce the number of minor injuries included.16 Dispatch information, prehospital time intervals, medical information, and the identity of the ambulance crew members were added from prehospital electronic patient records.17 For each individual ambulance crew member the following information was collected: years of employment in health care services and in ambulance services; the date for exam as a registered nurse and the date for PHTLS training. The final dataset consisted of 2830 injury events with complete data (Fig. 1). The study was approved by the regional Human Ethics Committee. 2.4. Exposure The PHTLS program is a standardized curriculum for prehospital caregivers. The core component is a 16-h course with a mixture of lectures and interactive skill stations.18 If at least one ambulance crew member was PHTLS certified (had passed the final examination in PHTLS), the injured patient was considered exposed. Exposure status was not dependent on whether the certified crew member was in charge of the crew or not. The time elapsed since PHTLS certification was considered in a sensitivity analysis. No refresher courses were performed during the study period.
2.6. Possible confounders The International Classification of disease Injury Severity Score (ICISS) was calculated based on diagnosis-specific survival probabilities for individual injury ICD-10 codes.20–22 For descriptive purposes, ICISS was categorized as critical (0–0.219), severe (0.220–0.354), serious (0.355–0.664), moderate (0.665–0.940), or minor (0.941–1.0). Injury severity estimates based on the Revised Trauma Score (RTS) were also available.23 Each victim’s injuries were categorized according to the injury mortality diagnosis matrix for ICD-10.24 Five categories of major injury regions (head, spinal cord, thorax, abdomen, and pelvis) were defined from the matrix. Causes of injury were classified according to the matrix developed by the National Center for Health Statistics, Centers for Disease Control, USA.25 Data was collapsed to three categories: traffic injuries, falls, and other injuries. The basic educational level (nurse or EMT equivalent) and employment time of the ambulance crew members were also considered as potential confounders. Exposure status was determined from the highest educational level and the longest work experience in the ambulance crew. To account for a possible period effect (such as changes in trauma care over time during the study period), the calendar year of the injury was included in the multivariable models. The patients’ age and sex were also considered as potential confounders. 2.7. Statistics Multivariable logistic regression analysis was used to model the composite outcome of prehospital or hospital death using all the possible confounders described above. Collinearity was assessed from variance inflation factors. Both age and calendar year of injury were entered as linear effects in the models based on inspection of a logit plot of each variable. ICISS was used after logit transformation.26 Effect-measure modification was evaluated by including product terms between the exposure variable and injury severity (ICISS) and year, respectively. The multivariable logistic regression model was also used to calculate the predicted mortality for each injury event. The difference in mean predicted mortality between the PHTLS group and the non-PHTLS group estimated the absolute risk reduction. Population-averaged models using generalized estimation equations (GEE) were used to handle correlations from ambulance crews appearing multiple times in the dataset and patients appearing multiple times in the study population.27,28 The number of lives potentially saved annually was estimated by the proportion of eligible patients corresponding to the estimated absolute risk
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5235 severe injuries (hospitalized > 1 day or dead) with documented participation of EMS
483 without a valid personal identification number in EMS record
Information on educational level among ambulance personnel added
1129 with incomplete information to determine crew PHTLS certification status
3623 severe injuries (hospitalized > 1 day or dead) with documented participation of EMS and information on ambulance crew’s PHTLS certification status
793 without complete information on both crew members’ identity and PHTLS certification status and covariates
2830 incident injuries (hospitalized > 1 day or dead) with documented participation of EMS and information on identity and PHTLS certification status for both members of the ambulance crew and all covariates in the multivariable model
Fig. 1. Flow diagram for selection of the study population. EMS: Emergency Medical Service.
reduction. All analyses were done as complete subjects analyses. As a sensitivity analysis, multiple imputation using predictive mean matching (aregImpute) that fits separate flexible additive imputation models, was also applied.29,30 Missing data for PHTLS status and employment time were imputed based on mortality, PHTLS status, educational level (nurse/EMT), calendar year, and employment time. Ten bootstrap samples were generated. The statistical packages SAS version 9 (SAS Institute Inc., Cary, NC, USA) and R version 2.9.2 (R Foundation for Statistical Computing, Vienna, Austria) were used for data management and statistical analyses.
0.41–1.25), with a corresponding predicted absolute risk reduction of 0.2%. There was no indication of serious collinearity in the regression model, with the highest variance inflation factor being 1.78. This analysis, however, violates the assumption of independent observations since the same ambulance crews were involved in many cases of injury and also the same patients occurred multiple times in the study population. We analytically considered such possible correlations (supplementary data: Table 4). Results from these analyses were very similar to those from using standard logistic regression. The choice of variance structure did not affect the result and, in general, the correlations were minimal (<0.1).
3. Results 3.3. Subgroup analyses 3.1. Cohort characteristics During the 7-year study period, EMSs responded to 2830 injury events with complete data for the analyses (Fig. 1). The proportion of injury events handled by ambulance crew members with PHTLS training increased over time (Fig. 2). There were no major differences in patient characteristics; however, ambulance crews in the PHTLS group had more years of employment in ambulance services, compared to the non-PHTLS group (Table 1). There was no apparent overall difference in response time, on-scene time, or transport time between the two groups.
A product term for PHTLS and injury severity in the logistic regression model indicated the possible presence of effect-measure modification (likelihood ratio test P = 0.08), but analysis stratified for injury severity did not indicate any substantial influence on the estimated OR for PHTLS (Table 2). Exclusion of injuries caused by falls strengthened the estimated protective effect of PHTLS to OR = 0.54 (Table 2) but with poor precision (95% CI, 0.13–2.41). A short interval from PHTLS training resulted in a stronger protective association with mortality (Table 2). The precision in these estimates was poor.
3.2. Relative and absolute mortality risk
3.4. Characteristics of excluded patients
The mortality was 4.7% (36/763) without PHTLS training and 4.5% (94/2067) with PHTLS training. The crude (unadjusted) odds ratio (OR) for mortality was 0.96 [95% confidence interval (CI), 0.66–1.44]. The adjusted OR for mortality was 0.71 (95% CI,
There were no major differences in age, sex, cause of injury, injury severity, major organs injured, or mortality when characteristics among the excluded cases were compared to the study population (supplementary data: Table 3). In electronic EMS patient
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B. PHTLS trained present
100 80 60 40
Proportion of injury events (%)
80 60 40
Registered nurse EMT equivalent
0
0
20
At least one with PHTLS No one with PHTLS
20
Proportion of injury events (%)
100
A. Highest educational level present
1998
2000
2002
2004
1998
2000
2002
2004
Fig. 2. Change during the study period in the distribution of highest educational level in the ambulance crews (left panel) and the proportion of injury events where at least one PHTLS-trained individual was present (right panel). EMT equivalent, Emergency Medical Technician equivalent (nursing assistants with special ambulance training). Table 1 Baseline characteristics of the Study Cohort. N
Age, % (N) 0–14 15–24 25–44 45–64 65–74 ≥75 Sex, % (N) Male ICISS stratified, % (N) Critical Severe Serious Moderate Minor Head injury, % (N) Spinal cord injury, % (N) Thoracic injury, % (N) Abdominal injury, % (N) Pelvic injury, % (N) No. of major injury regions, % (N) None One Two Three or more Cause of injury, % (N) Fall Traffic Other Highest crew member education, % (N) EMT equivalent Registered nurse Shortest interval from PHTLS training in the ambulance crew (years), median (IQR) Maximum years of individual experience in EMS, median (IQR) Response time minutes, median (IQR) On-scene time minutes, median (IQR) Transport time minutes, median (IQR) Prehospital mortality, % (N) Hospital mortality, % (N)
At least one member of the ambulance crew with PHTLS certification (N = 2067)
No member of the ambulance crew with PHTLS certification (N = 763)
2830 2 (39) 4 (93) 11 (219) 18 (362) 11 (220) 55 (1134)
1 (9) 5 (37) 10 (78) 11 (86) 11 (85) 61 (468)
37 (759)
38 (292)
1 (23) 1 (20) 5 (96) 48 (1000) 45 (928) 9 (194) 0 (6) 6 (122) 1 (27) 6 (118)
0 (2) 1 (7) 4 (30) 50 (379) 45 (345) 9 (71) 0 (0) 6 (42) 1 (7) 6 (46)
80 (1653) 18 (372) 2 (33) 0 (9)
80 (612) 18 (140) 1 (7) 1 (4)
76 (1570) 11 (234) 13 (263)
80 (607) 9 (71) 11 (85)
32 (660) 68 (1407)
75 (574) 24 (189)
2830 2830
2830 2830 2830 2830 2830 2830
2830
2830
2067 2830 2563 2497 2059 2830 2830
1.4 (0.7–2.3) 18 (15–22) 9 (5–15) 11 (8–16) 11 (7–25) 1% (20) 4% (74)
NA 16 (12–19) 9 (5–15) 10 (7–14) 12 (7–24) 0% (2) 4% (34)
ICISS, International Classification of disease Injury Severity Score; IQR, interquartile range; EMS, Emergency Medical Service; EMT, Emergency Medical Technician.
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Table 2 Complementary analysesa , using subgroups and graded exposures, of the association between PHTLS certification and mortality. Subgroup
Number of patients (no PHTLS/PHTLS)
Number of deaths (no PHTLS/PHTLS)
Crude (unadjusted) OR (95% CI)
Adjusted OR (95% CI)
Traffic and other injury, excluding falls Effect-measure modification by injury severity More severe injury (ICISS < 0.665) Less severe injury (ICISS ≥ 0.665) Length of the interval since PHTLS certification (shortest in the ambulance crew) ≤1 year >1 year
156/497
7/29
1.32 (0.60–3.33)
0.54 (0.13–2.41)
39/139 724/1928 763/2067
9/35 27/59 36/94
1.12 (0.50–2.71) 0.81 (0.52–1.31)
0.80 (0.20–3.44) 0.77 (0.28–2.04)
0.75 (0.44–1.24) 1.09 (0.72–1.66)
0.66 (0.36–1.20) 0.76 (0.42–1.41)
OR, odds ratio; CI, confidence interval; ICISS, International Classification of disease Injury Severity Score. a Multivariable logistic regression model also included age, sex, injury severity (ICISS), cause of injury (all transport/fall/other), study year, head injury, spinal cord injury, thoracic injury, abdominal injury, pelvic injury, and years of employment in ambulance service.
care reports without a valid personal identification number for the patient, the median RTS was 8, identical to the median RTS among those with a complete personal identification number. Multiple imputation of the major offending variables – PHTLS status and employment time – did not substantially change the estimated association or the precision in the estimate (supplementary data: Table 4).
4. Discussion This population-based observational study indicates that PHTLS training of ambulance crew members may be associated with approximately 30% relative reduction of mortality in trauma victims. However, with the low overall mortality in this population, the precision of this estimate was low making the interpretation of this possible association difficult. The predicted absolute risk reduction is estimated to correspond to 0.5 lives saved annually per 100,000 population with PHTLS fully implemented. Despite decades of widespread implementation of educational programs such as ATLS and PHTLS, there is scant scientific evidence for beneficial effects in trauma outcome.6,7,12 Today, a substantial proportion of ambulance crews include an ALS-trained member.31 While there are studies on conditions other than trauma indicating that ALS training and higher educational levels among ambulance crews improves outcomes,32,33 there is little support for such an association in trauma care.34–36 Three conditions provided a rare opportunity to perform a comprehensive observational study of the association between PHTLS training and outcome: (a) the introduction of prehospital electronic medical records, (b) the subsequent gradual implementation of PHTLS, and (c) exact record linkage of patient injury data in national healthcare databases. We could associate each injury case with the educational level of the ambulance crew and adjust for each crew’s years of experience and basic educational level. We further controlled for injury cause, type and severity, and also the year of the study in which the exposure/incident occurred in order to account for a possible period effect. The population-based design and good control of confounding – and especially a potential period effect – support the findings in this study. Exclusion of observations with incomplete data was unfortunate; however, the comparable characteristics of these patients to the study population, the small impact on the estimated association from imputation of missing values, and the absence of apparent effect-measure modification from injury severity did not raise any grave concern of a selection bias. A particular challenge for this type of study is that the same ambulance crews appear several times in the data, and similarly a patient may also present repeated times. Ignoring such correlations within the data may lead to incorrect statistical inference and
biased estimates of variance. However, methods that do not utilize all available data may be inefficient. Analytically accounting for these showed that their impact on the estimated associations was minimal. Despite good control of several important confounders, limited causal inference can be made from a single observational study. Largely due to the relatively low overall mortality in our study, the precision of our estimates was low. We can only speculate why PHTLS training appeared to be associated with reduced mortality after trauma. According to the PHTLS concept, endotracheal intubation is the preferred method of airway control, although several studies dispute the benefits of this procedure.36–38 In our study, no member of the ambulance crew was authorized to perform endotracheal intubation on trauma patients, irrespective of PHTLS training. Thus, acquisition of this specific airway management skill could not explain the lowered mortality seen in our study. In fact, in this EMS system, ALS interventions related to PHTLS had already been implemented before PHTLS education, with the exception of endotracheal intubation. Therefore, the observed association between PHTLS education and reduced mortality was most likely not due to the application of individual ALS interventions. It has been demonstrated that PHTLS training improves adherence to established priorities and management of the trauma victim in a structured approach, which could possibly shorten time to definitive care.12,39 In our study though, response time, on-scene time, and transport time did not appear influenced by PHTLS training. The impact of the structured approach, including assessment and setting priorities that the PHTLS concept is focused on, is difficult to measure and could possibly explain the association with reduced mortality. The percentage of ambulance crews trained in PHTLS went from approximately 15% to almost 90% in a relatively short time period. This focused educational effort could possibly create a Hawthornelike effect, making the PHTLS-trained personnel particularly prone to perform better in general, regardless of the content of the course. However, the implementation of PHTLS in the organization could possibly also have influenced ambulance crew members not trained in PHTLS in a positive direction, thus also improving outcome in the control group. Concerning the ability to generalize our results to other settings, it is notable that the PHTLS course is highly standardized. However, the basic educational level of prehospital crews varies between organizations and therefore other results could possibly be achieved in a similar study in another setting such as a paramedic or EMT-based system. The study population represents the population admitted to a general hospital, but is not representative of a selected trauma-center population. This is reflected by the female dominance and large number of falls causing minor or moderate injuries.
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Excluding falls from the study population, i.e. mainly elderly patients with hip fractures, as expected strengthened the association between PHTLS and reduced mortality. In the subgroup of traffic crash victims there was a signal of stronger association between PHTLS training and reduced mortality. It is therefore possible that both the relative and especially the absolute risk reduction might be higher in a population with higher prevalence of multiply injured patients. The relation with injury severity is also of high interest, but likely complex. One may expect to find the strongest effect of PHTLS on injuries with intermediate severity. With low injury severity mortality is very low and with high injury severity mortality might be inevitable. However, due to the limited mortality in the study population we are unable to analytically pursue this in detail. The presented estimates from all the subgroup analyses are highly uncertain and must be interpreted with great caution. 5. Conclusion This population-based study of lethal or hospitalized injuries indicates that PHTLS training of ambulance crew members may be associated with reduced mortality in trauma patients, but the precision in this estimate was low due to the overall low mortality. Although our study indicates a relative risk reduction, the predicted absolute risk reduction in this population was low. Conflicts of interest Dr. Blomberg reported that he has been an instructor in PHTLS since 2002. No other conflicts of interest were reported. Acknowledgments This project was funded by Uppsala University and the Laerdal Foundation for Acute Medicine. No funding organization or sponsor was involved in the design and conduct of the study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.resuscitation.2012.02.018. References 1. Krug EG, Sharma GK, Lozano R. The global burden of injuries. Am J Public Health 2000;90:523–6. 2. Trunkey DD, Lim Jr RC, Blaisdell FW. Traumatic injury. A health care crisis. West J Med 1974;120:92–4. 3. Lerner EB, Moscati RM. The golden hour: scientific fact or medical “urban legend”? Acad Emerg Med 2001;8:758–60. 4. American College of Surgeons. Atls advanced trauma life support. Lippincott Williams & Wilkins; 2006. 5. Collicott PE, Hughes I. Training in advanced trauma life support. JAMA 1980;243:1156–9. 6. Soreide K. Three decades (1978–2008) of advanced trauma life support (atls) practice revised and evidence revisited. Scand J Trauma Resusc Emerg Med 2008;16:19. 7. Jayaraman S, Sethi D. Advanced trauma life support training for hospital staff. Cochrane Database Syst Rev 2009:CD004173.
8. Drimousis PG, Theodorou D, Toutouzas K, et al. Advanced trauma life support certified physicians in a non trauma system setting: is it enough? Resuscitation 2011;82:180–4. 9. Salomone JP, Pons PT. Phtls prehospital trauma life support; 2007. 10. Ali J, Adam RU, Gana TJ, Williams JI. Trauma patient outcome after the prehospital trauma life support program. J Trauma 1997;42:1018–21 [discussion 1021–1012]. 11. Arreola-Risa C, Mock C, Herrera-Escamilla AJ, Contreras I, Vargas J. Costeffectiveness and benefit of alternatives to improve training for prehospital trauma care in mexico. Prehosp Disaster Med 2004;19:318–25. 12. Jayaraman S, Sethi D. Advanced trauma life support training for ambulance crews. Cochrane Database Syst Rev 2010:CD003109. 13. Gedeborg R, Engquist H, Berglund L, Michaelsson K. Identification of incident injuries in hospital discharge registers. Epidemiology 2008;19:860–7. 14. World Health Organization. International statistical classification of diseases and health related problems icd-10. Geneva: World Health Organization; 2005. 15. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A. The swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol 2009;24:659–67. 16. Cryer C, Langley JD. Studies need to make explicit the theoretical and case definitions of injury. Inj Prev 2008;14:74–7. 17. Karlsten R, Sjoqvist BA. Telemedicine and decision support in emergency ambulances in uppsala. J Telemed Telecare 2000;6:1–7. 18. National association of emergency medical technicians (naemt).