All trauma is not created equal: Redefining severe trauma for combat injuries

Revolutionizing Combat Casualty Care: The Power of Digital Twins in Optimizing Casualty Care Through Passive Data Collection

The potential impact of large-scale combat operations and multidomain operations against peer adversaries poses significant challenges to the Military Health System including large volumes of critically ill and injured casualties, prolonged care times in austere care contexts, limited movement, contested logistics, and denied communications. These challenges contribute to the probability of higher casualty mortality and risk that casualty care hinders commanders' forward momentum or opportunities for overmatch on the battlefield. Novel technical solutions and associated concepts of operation that fundamentally change the delivery of casualty care are necessary to achieve desired medical outcomes that include maximizing Warfighter battle-readiness, minimizing return-to-duty time, optimizing medical evacuation that clears casualties from the battlefield while minimizing casualty morbidity and mortality, and minimizing resource consumption across the care continuum. These novel solutions promise to "automate" certain aspects of casualty care at the level of the individual caregiver and the system level, to unburden our limited number of providers to do more and make better (data-driven) decisions. In this commentary, we describe concepts of casualty digital twins-virtual representations of a casualty's physical journey through the roles of care-and how they, combined with passive data collection about casualty status, caregiver actions, and real-time resource use, can lead to human-machine teaming and increasing automation of casualty care across the care continuum while maintaining or improving outcomes. Our path to combat casualty care automation starts with mapping and modeling the context of casualty care in realistic environments through passive data collection of large amounts of unstructured data to inform machine learning models. These context-aware models will be matched with patient physiology models to create casualty digital twins that better predict casualty needs and resources required and ultimately inform and accelerate decision-making across the continuum of care. We will draw from the experience of the automotive industry as an exemplar for achieving automation in health care and inculcate automation as a mechanism for optimizing the casualty care survival chain.

Mental health outcomes of male UK military personnel deployed to Afghanistan and the role of combat injury: analysis of baseline data from the ADVANCE cohort study

BACKGROUND

The long-term psychosocial outcomes of UK armed forces personnel who sustained serious combat injuries during deployment to Afghanistan are largely unknown. We aimed to assess rates of probable post-traumatic stress disorder (PTSD), depression, anxiety, and mental health-associated multimorbidity in a representative sample of serving and ex-serving UK military personnel with combat injuries, compared with rates in a matched sample of uninjured personnel.

METHODS

This analysis used baseline data from the ADVANCE cohort study, in which injured individuals were recruited from a sample of UK armed forces personnel who were deployed to Afghanistan and had physical combat injuries, according to records provided by the UK Ministry of Defence. Participants from the uninjured group were frequency-matched by age, rank, regiment, deployment, and role on deployment. Participants were recruited through postal, email, and telephone invitations. Participants completed a comprehensive health assessment, including physical health assessment and self-reported mental health measures (PTSD Checklist, Patient Health Questionnaire-9, and Generalised Anxiety Disorder-7). The mental health outcomes were rates of PTSD, depression, anxiety, and mental health-associated multimorbidity in the injured and uninjured groups. The ADVANCE study is ongoing and is registered with the ISRCTN registry, ISRCTN57285353.

FINDINGS

579 combat-injured participants (161 with amputation injuries and 418 with non-amputation injuries) and 565 uninjured participants were included in the analysis. Participants had a median age of 33 years (IQR 30-37 years) at the time of assessment. 90·3% identified as White and 9·7% were from all other ethnic groups. The rates of PTSD (16·9% [n=89] vs 10·5% [n=53]; adjusted odds ratio [AOR] 1·67 [95% CI 1·16-2·41], depression (23·6% [n=129] vs 16·8% [n=87]; AOR 1·46 [1·08-2·03]), anxiety (20·8% [n=111] vs 13·5% [n=71]; AOR 1·56 [1·13-2·24]) and mental health-associated multimorbidity (15·3% [n=81] vs 9·8% [n=49]; AOR 1·62 [1·12-2·49]) were greater in the injured group than the uninjured group. Minimal differences in odds of reporting any poor mental health outcome were noted between the amputation injury subgroup and the uninjured group (AOR range 0·77-0·97), whereas up to double the odds were noted for the non-amputation injury subgroup compared with the uninjured group (AOR range 1·74-2·02).

INTERPRETATION

Serious physical combat injuries were associated with poor mental health outcomes. However, the type of injury sustained influenced this relationship. Regardless of injury, this cohort represents a group who present with greater rates of PTSD than the general population, as well as increased psychological burden from multimorbidity.

FUNDING

The ADVANCE Charity.

Association between combat-related traumatic injury and cardiovascular risk

Objective

The association between combat-related traumatic injury (CRTI) and cardiovascular risk is uncertain. This study aimed to investigate the association between CRTI and both metabolic syndrome (MetS) and arterial stiffness.

Methods

This was a prospective observational cohort study consisting of 579 male adult UK combat veterans (UK-Afghanistan War 2003–2014) with CRTI who were frequency-matched to 565 uninjured men by age, service, rank, regiment, deployment period and role-in-theatre. Measures included quantification of injury severity (New Injury Severity Score (NISS)), visceral fat area (dual-energy X-ray absorptiometry), arterial stiffness (heart rate-adjusted central augmentation index (cAIx) and pulse wave velocity (PWV)), fasting venous blood glucose, lipids and high-sensitivity C reactive protein (hs-CRP).

Results

Overall the participants were 34.1±5.4 years, with a mean (±SD) time from injury/deployment of 8.3±2.1 years. The prevalence of MetS (18.0% vs 11.8%; adjusted risk ratio 1.46, 95% CI 1.10 to 1.94, p<0.0001) and the mean cAIx (17.61%±8.79% vs 15.23%±8.19%, p<0.0001) were higher among the CRTI versus the uninjured group, respectively. Abdominal waist circumference, visceral fat area, triglycerides, estimated insulin resistance and hs-CRP levels were greater and physical activity and high-density lipoprotein-cholesterol lower with CRTI. There were no significant between-group differences in blood glucose, blood pressure or PWV. CRTI, injury severity (↑NISS), age, socioeconomic status (SEC) and physical activity were independently associated with both MetS and cAIx.

Conclusions

CRTI is associated with an increased prevalence of MetS and arterial stiffness, which are also influenced by age, injury severity, physical activity and SEC. The longitudinal impact of CRTI on clinical cardiovascular events needs further examination.

Influencing Factors and Prevention of Sepsis or Acute Kidney Injury in 85 Patients with Severe Trauma

Severe trauma can cause systemic reactions, leading to massive bleeding, shock, asphyxia, and disturbance of consciousness. At the same time, patients with severe trauma are at high risk of sepsis and acute renal injury. The occurrence of complications will increase the difficulty of clinical treatment, improve the mortality rate, and bring heavy physical and mental burdens and economic pressure to patients and their families. It is of great clinical significance to understand the high risk factors of sepsis and AKI and actively formulate prevention and treatment measures. In this study, the clinical data of 85 patients with severe trauma were analyzed by univariate and multivariate logistic regression to identify the risk factors leading to sepsis or AKI and analyze the prevention and treatment strategies. The results showed that multiple injuries, APACHE II score on admission, SOFA score on admission, and mechanical ventilation were independent influencing factors of sepsis in patients with severe trauma, while hemorrhagic shock, APACHE II score on admission, CRRT, and sepsis were independent influencing factors of AKI in patients with severe trauma. Severe trauma patients complicated with sepsis or AKI will increase the risk of death. In the course of treatment, prevention and intervention should be given as far as possible to reduce the incidence of complications.

Injury scoring systems for blast injuries: a narrative review

Injury scoring systems can be used for triaging, predicting morbidity and mortality, and prognosis in mass casualty incidents. Recent conflicts and civilian incidents have highlighted the unique nature of blast injuries, exposing deficiencies in current scoring systems. Here, we classify and describe deficiencies with current systems used for blast injury. Although current scoring systems highlight survival trends for populations, there are several major limitations. The reliable prediction of mortality on an individual basis is inaccurate. Other limitations include the saturation effect (where scoring systems are unable to discriminate between high injury score individuals), the effect of the overall injury burden, lack of precision in discriminating between mechanisms of injury, and a lack of data underpinning scoring system coefficients. Other factors influence outcomes, including the level of healthcare and the delay between injury and presentation. We recommend that a new score incorporates the severity of injuries with the mechanism of blast injury. This may include refined or additional codes, severity scores, or both, being added to the Abbreviated Injury Scale for high-frequency, blast-specific injuries; weighting for body regions associated with a higher risk for death; and blast-specific trauma coefficients. Finally, the saturation effect (maximum value) should be removed, which would enable the classification of more severe constellations of injury. An early accurate assessment of blast injury may improve management of mass casualty incidents.

Machine Learning for Military Trauma: Novel Massive Transfusion Predictive Models in Combat Zones

BACKGROUND

Damage control resuscitation has become the standard of care in military and civilian trauma. Early identification of blood product requirements may aid in optimizing the clinical decision-making process while improving trauma related outcomes. This study aimed to assess and compare multiple machine learning models for predicting patients at highest risk for massive transfusion on the battlefield.

METHODS

Supervised machine learning approaches using logistic regression, support vector machine, neural network, and random forest techniques were used to create predictive models for massive transfusion using standard prehospital and arrival data points from the Department of Defense Trauma Registry, 2008-2016. Seventy percent of the population was used for model development and performance was validated using the remaining 30%. Models were tested for accuracy and compared by standard performance statistics.

RESULTS

A total of 22,158 patients (97% male, 58% penetrating injury, median age 25-29 y/o, average Injury Severity Score 9, with an overall mortality of 3%) were included in the analysis. Massive transfusion was required by 7.4% of patients. Overall accuracy was found to be above 90% in all models tested. Following cross validation and model training, the random forest model outperformed the alternatively tested models for precision, recall, and area under the curve.

CONCLUSION

Machine learning techniques may allow for more optimal and rapid identification of combat trauma patients at highest risk for massive transfusion. These powerful approaches may uncover novel correlations and help improve triage, activation of massive transfusion resources, and trauma-related outcomes. Further research seeking to optimize and apply these algorithms to trauma-centered research should be pursued.

Shock index as a prognosticator for emergent surgical intervention and mortality in trauma patients in Johannesburg: A retrospective cohort study

Introduction

Trauma is the leading cause of morbidity and mortality worldwide with exsanguination being the primary preventable cause through early surgical intervention. We assessed two popular trauma scoring systems, injury severity scores (ISS) and shock index (SI) to determine the optimal cut off values that may predict the need for emergent surgical intervention (ESI) and in-hospital mortality.

Methods

A retrospective analysis of patient records from a tertiary hospital's trauma unit for the year 2019 was done. Descriptive statistics, univariate and multivariate logistic regression analyses were performed. Receiver operator characteristic (ROC) curve analysis was conducted and area under the curve (AUC) reported for predicting the need for ESI in all study participants, as well as in patients with penetrating injuries alone, based on continuous variables of ISS, SI or a combination of ISS and SI. The Youdin Index was applied to determine the optimal ISS and SI cut off values.

Results

A total of 1964 patients’ records were included, 89.0% were male and the median age (IQR) was 30 (26–37) years. Penetrating injuries accounted for 65.9% of all injuries. ISS and SI were higher in the ESI group with median (IQR) 11 (10–17) and 0.74 (0.60–0.95), respectively. The overall mortality rate was 4.5%. The optimal cut-off values for ESI and mortality by ISS (AUC) were 9 (0.74) and 12 (0.86) (p = 0.0001), with optimal values for SI (AUC) being 0.72 (0.60), and 0.91 (0.68) (p = 0.0001), respectively.

Conclusion

ISS and SI are significant, independent prognosticators for the need of ESI and in-hospital mortality.

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The ISS cut-off of 15 for severe trauma may underestimate the severity of trauma within our local South African population.

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Our study reports an optimal SI cut off value of ≥0.72 for emergency surgical intervention.

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At an optimal SI cut off of ≥0.91 for in-hospital mortality, the odds of demise were 6.7 times higher.

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ISS was a stronger predictor than SI of both ESI and in-hospital mortality in our study.

A Comparative Study Between Two Combat Injury Severity Scores

INTRODUCTION

In recent years, specific trauma scoring systems have been developed for military casualties. The objective of this study was to examine the discrepancies in severity scores of combat casualties between the Abbreviated Injury Scale 2005-Military (mAIS) and the Military Combat Injury Scale (MCIS) and a review of the current literature on the application of trauma scoring systems in the military setting.

METHODS

A cross-sectional, descriptive, and retrospective study was conducted between May 1, 2005, and December 31, 2014. The study population consisted of all combat casualties attended in the Spanish Role 2 deployed in Herat (Afghanistan). We used the New Injury Severity Score (NISS) as reference score. Severity of each injury was calculated according to mAIS and MCIS, respectively. The severity of each casualty was calculated according to the NISS based on the mAIS (Military New Injury Severity Score-mNISS) and MCIS (Military Combat Injury Scale-New Injury Severity Score-MCIS-NISS). Casualty severity were grouped by severity levels (mild-scores: 1-8, moderate-scores: 9-15, severe-scores: 16-24, and critical-scores: 25-75).

RESULTS

Nine hundred and eleven casualties were analyzed. Most were male (96.37%) with a median age of 27 years. Afghan patients comprised 71.13%. Air medevac was the main casualty transportation method (80.13). Explosion (64.76%) and gunshot wound (34.68%) mechanisms predominated. Overall mortality was 3.51%. Median mNISS and MCIS-NISS were similar in nonsurvivors (36 [IQR, 25-49] vs. [IQR, 25-48], respectively) but different in survivors, 9 (IQR, 4-17) vs. 5 (IQR, 2-13), respectively (P < .0001). The mNISS and MCIS-NISS were discordant in 34.35% (n = 313). Among cases with discordant severity scores, the median difference between mNISS and MCIS-NISS was 9 (IQR, 4-16); range, 1 to 57.

CONCLUSION

Our study findings suggest that discrepancies in injury severity levels may be observed in one in three of the casualties when using mNISS and MCIS-NISS.