Bmj Military Health最新文献

Introduction: Morbidity and mortality from pelvic ring injuries can be mitigated by early and effective external pelvic stabilisation. The field-expedient pelvic splint (FEPS) is a recently described technique to improvise an effective pelvic binder for an austere environment. This technique has not been biomechanically validated. We hypothesise that the FEPS will be biomechanically equivalent to a commercially available pelvic binder routinely used in the austere environment.

Methods: Compressive force generation of the FEPS was measured using a commercial load frame. A SAM Pelvic Sling was used as a control. The FEPS was tested for initial force generation, persistence of force generation over a 6-hour longitudinal test period and force generation after repeated assembly/disassembly.

Results: The FEPS generated 203N (±7N) with one windlass turn and 420N (±34N) with two windlass turns. The SAM Pelvic Sling generated 197N (±11N) of force. There was no significant difference between FEPS after one windlass crank and the SAM Pelvic Sling but the force generated by the FEPS with two windlass cranks was significantly higher than the SAM Pelvic Sling. Longitudinal testing showed that after 6 hours of continuous compression, the FEPS generated 189N (±19N) and the force generated at hour 6 was not significantly different from the initial force generated by SAM Pelvic Sling. Reusability testing showed no significant difference with force generation by the FEPS after repeated assembly/disassembly with one crank of the windlass but there was a significantly increased force generation by FEPS after repeated use trials with two cranks of the windlass.

Conclusion: The FEPS exerted equivalent pelvic compressive forces to its commercial equivalent and this force generation persists at effective levels over a 6-hour time course. The FEPS remained effective after repeated use. The FEPS is a viable alternative in the austere or resource-limited environment for temporary pelvic stabilisation.

Introduction: The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes.

Methods: A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration.

Results: Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort.

Conclusion: The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.

Introduction: Travel to resource-limited settings is a known risk for acquisition of extended-spectrum β-lactamase-producing Enterobacterales (ESBL-PE) and carbapenem-resistant Enterobacterales (CRE), which are both associated with increased morbidity and mortality. We investigated the ESBL-PE and CRE baseline prevalence in British service personnel (SP).

Methods: SP provided faecal samples for research projects in several different settings, between September 2021 and April 2022. Bacterial colonies from faecal isolates were recovered from incubated ChromID ESBL plates (bioMérieux, Marcy-l'Étoile, France) and DNA extracted using Qiagen DNeasy extraction kits (Qiagen, UK). PCR to identify β-lactamase and CRE encoding genes was performed using the Rotor-Gene Q (RGQ) (Qiagen, UK), with positivity detected by RGQ software. Phenotypic assessment of antimicrobial susceptibility was not performed.

Results: Out of 250 personnel approached, 239 (85.5% men, median (IQR) age 31 (26-37) years) provided faecal samples suitable for analysis. The ESBL prevalence was 40/239 (16.7%), with ESBL-producing Escherichia coli detected in 39 (16.3%) samples and ESBL-producing Klebsiella pneumoniae in 1 (0.4%) sample. Combinations including Temoniera, sulfhydryl reagent variable (SHV), cefotaxime hydrolysing β-lactamase (Munich) (CTX-M) 1 and CTX-M 9 genes were detected in 18 (7.5%), 33 (13.8%) 16 (6.7%) and 8 (3.3%) samples, respectively. E. coli samples had mixtures of all four genotypes with SHV predominating. One (0.4%) sample carried all four gene types and the only K. pneumoniae sample carried a single SHV gene. No CRE were detected.

Conclusions: The prevalence of ESBL-PE in cohorts of SP closely matches that of civilian populations in England; however, we noted differences in ESBL genotype distribution. Potential exposure risks for SP from international travel and occupational trauma emphasise the need for repeated surveillance to characterise and detect changes in acquisition epidemiology and carriage of ESBL. Such prospective data have important antimicrobial stewardship implications in optimising clinical outcomes, controlling resistance and guiding empirical antibiotic formulary policy recommendations.

The physical capacity of male and female warfighters is challenged on the modern battlefield by heavy loads and high-intensity work. When designing training programmes for warfighters, approaches for developing strength and power alongside endurance must be considered. Strength training often requires facilities that may not be available during deployments while multiple stressors may impair or decrease overall performance. Understanding the effect of military environments on warfighter performance and acknowledging the variation in demands for individuals during field training and deployments, including possible sex differences, is essential to promote the development of adequate physical reserves (strength, power and endurance), attenuate risk for injury and promote health during and after military careers. The purpose of this narrative review is to discuss considerations for programming physical training in a military environment where 'one size does not fit all'. In addition, a brief description of physiological contributions (neural and muscular) to strength development is included.

Introduction: Military forces receive training in various high-altitude warfare techniques to safeguard border regions and lives. Skiing is one of such practices where research evidences are limited. Therefore, a study was conducted to continuously monitor the physiological status of soldiers during military skiing practice to quantify the actual demands.

Methods: 11 Indian soldiers skied a distance of 8 km while carrying 18 kg of military equipment. Physiological status and heart rate variability (HRV) were continuously recorded throughout the exercise. The participants completed the event in about 3 hours. The icy track consisted of about 50% upward slopes, 40% downward slopes and only 10% of flat surface. The full data set was divided into four phases (45 min each), to understand the degree of changes in physiological, and autonomic responses with progress in time and intensity. Repeated measures analysis of variance was used to examine the degree of significance between the phases.

Results: Physiological factors including HR rose consistently from the first to the fourth phase, increasing from 116 to 150 beats/min. The peak acceleration was maintained within 0.4-0.6 g and ground speed varied from 0 to 14.8 km/hour, respectively. The time domain parameters decreased steadily from pre-exercise to the first half of the event, then slightly increased at the beginning of the second half before decreasing again during the final part. The low frequency increased during the first half and remained low until the end of the second half, while the high frequency exhibited an exactly opposite pattern.

Conclusion: The HRV data indicated parasympathetic withdrawal and sympathetic activation in first half, primarily due to the uphill climb. The third phase featured with the flatter surface, which increased the speed and led to partial parasympathetic activation. The final incline caused a rise in physiological responses and sympathetic dominance. Persistent cold, exposure to hypoxia and job requirements ensured that the physiological variables remained at a 'moderately' high level.

A ubiquitous problem facing military organisations is musculoskeletal injury (MSKI) risk identification. Recently, two research groups, each with their own funding, collaborated to address this problem. Combining their respective areas of expertise in biomechanics and physiological biomarkers, the group explored this problem in the laboratory and in the field. They have developed a machine learning model in a US Marine Corps (USMC) officer cadet cohort that identifies MSKI risk from a single jump test, identified a minimum inertial measurement unit sensor array to quantity jump and squat performance and have identified sex differences in overuse, lower-limb injury risk. This machine learning model was able to correctly predict lift to place within 4 kg using a testing data set and less than 1 kg in the training set of data. Such collaborative approaches are encouraged to address complicated research problems. To assemble an effective team, consider forming groups that best complement each other's areas of expertise and prioritise securing separate funding to ensure each group can act independently. By doing this, the group has assessed the suitability and feasibility of various wearable technologies, used machine learning to gain insights into USMC physiological training adaptations, and developed an understanding of MSKI risk profiles within this cohort.