Jaques Reifman

Background: Lower-leg wounds and fractures are some of the most common types of traumatic injuries sustained by U.S. Service members during combat operations. Existing guidelines for on-field management of lower-leg injuries recommend stabilizing the injury and preventing weight-bearing on the affected leg. However, adhering to these recommendations could be challenging because the casualty may need to walk independently to obtain medical care. In this exploratory experimental and computational study, we assessed the biomechanical characteristics of a hands-free crutch device to aid in the mobility of healthy individuals with a hypothetical lower-leg injury. Methods: We conducted an experimental study where we collected anthropometric, body-motion, ground reaction force, spatiotemporal, and electromyography data from 10 young, healthy men who walked with and without the Freedom Leg hands-free crutch on an instrumented treadmill. Using these data, we developed individualized musculoskeletal and finite-element models and computed biomechanical parameters, including joint kinematics and kinetics, muscle activity and metabolic power demand, and tibial stress, for each participant under each of the two walking conditions. We evaluated the suitability of the device by comparing the model-predicted biomechanical parameters between the two conditions. Results: Compared to walking unassisted, walking with the Freedom Leg reduced the peak knee joint reaction force (JRF) by 47%, peak ankle JRF by 98%, and peak tibial stress by 59% in the crutch-supported leg. However, it also introduced unintended consequences in the contralateral leg, increasing the peak hip JRF by 19%, peak hip flexion moment by 50%, and peak hip extension moment by 89% as well as the mean metabolic power demand in representative hip and knee muscles. Conclusion: The Freedom Leg improved mobility by effectively reducing weight-bearing and stabilizing the crutch-supported leg. However, changes in gait patterns also introduced unintended biomechanical consequences in the contralateral leg. This exploratory study provides insights into the biomechanical effects of walking with the Freedom Leg in healthy, young individuals and may inform the development and assessment of future hands-free crutch devices to improve mobility of Service members with lower-leg injuries.

Prehospital pain management is challenging and is expected to become even more complex in future large-scale combat operations, where mass-casualty events and evacuation delay may be inevitable. An improved understanding of how analgesic drugs affect the physiological response of trauma patients can enhance treatment efficacy of combat casualties. We previously developed and validated a cardio-respiratory (CR) model for humans that accounts for vital-sign responses to hemorrhagic injuries, resuscitation with six fluid types, airway obstruction, and ketamine analgesia. Here, we extended the model to include the effect of fentanyl on vital signs by integrating existing fentanyl pharmacokinetic-pharmacodynamic models with the neuronal controller of the model. We calibrated and validated the extended model using experimental data from eight studies involving intravenous fentanyl administration (0.71–50.00 μg/kg) to healthy humans and swine with hemorrhagic injury. The model predictions reasonably captured the trend of the experimental data, with root mean square errors (RMSEs) between model predictions and measured data of 0.83 L/min for minute ventilation (MV), 1.20 mmHg for end-tidal carbon dioxide, 0.09 L for tidal volume, and 1.61 mmHg for mean arterial pressure, all of which were within 3–12% of their baseline values. For plasma fentanyl concentration, we obtained RMSEs of 0.79 μg/L in humans and 25.83 μg/L in swine. In simulations, we observed that as hemorrhage increased from 0 to 40% of blood volume, the fentanyl-induced decrease in MV increased from 19 to 34% of its baseline value prior to administration due to reduced fentanyl clearance. Similarly, in simulations of airway obstruction, the fentanyl-induced decrease in MV was 26% of its baseline value after a 100% obstruction compared to only 19% for a no-obstruction condition. Given that most combat casualties receive either fentanyl or ketamine for pain management, the ability to predict and quantify the physiological effects of these drugs will allow us to generate relevant synthetic datasets of diverse battlefield scenarios.

Objective: This exploratory study investigated the ability of biomechanical gait parameters to predict and stratify simulated tibial stress-fracture (SF) risk in young, healthy adults. Methods: We analyzed 30 modifiable gait parameters (both kinematic and kinetic) for 21 men and 20 women when they ran with external loads (0, 11.3, or 22.7 kg) and the associated tibial SF risks during a simulated 10-week basic combat training in the U.S. military. To identify informative parameters and predict SF risk of the tibia, we separately developed personalized linear mixed-effects models for men and women. To stratify the SF risk, we developed logistic regression (LR) models. Results: Out of all the 30 parameters, ankle joint reaction force was the most predictive for both men and women, followed by hip external rotation angle for men and hip adduction moment for women. Using these parameters, LR models yielded area under the curve values of 0.81-0.95. For kinematic parameters alone, ankle dorsiflexion angle was the most predictive for both men and women, followed by hip external rotation angle for men and hip flexion angle for women. The associated LR models yielded reasonable performance for women (0.72-0.82) but weaker performance for men (0.53-0.71). Conclusion: We identified modifiable gait parameters predictive of simulated tibial SF risk in men and women and developed models based on kinematic and kinetic parameters that adequately predicted at-risk individuals. In the future, upon independent validation, this work has the potential to help identify recruits who may benefit from individualized training regimens and proactive interventions to reduce injury risk.

Prolonged exposure to Arctic-like conditions can lead to adverse physiological outcomes, significantly affecting the health, performance, and operational readiness of civilians and military personnel. In this study, we extended our previously developed, anatomically realistic, three-dimensional thermoregulatory virtual human model to more comprehensively predict changes in core body temperature and skin temperature of peripheral body regions (dorsum hand, fingertip, face, dorsum foot, and toe-tip) and the cheek under extreme cold-air temperatures down to −40.0 °C and wind speeds ranging from 0.1 to 3.0 m·s ⁻¹ . To assess model performance, we computed the root mean square error (RMSE) between model predictions and experimental data as well as the fraction of model predictions that lie within two standard errors of the mean experimental data from eight cold-air exposure studies involving 75 unique subjects. Overall, we observed a reasonable agreement between model predictions and the experimental measurements, with average RMSEs of about 0.2 °C for core body temperature, 0.5 to 1.8 °C for mean skin, 1.6 to 2.7 °C for fingertip, 1.2 to 3.5 °C for toe-tip, and 2.1 to 2.7 °C for cheek skin temperature. As such, the extended model can serve as a valuable tool to inform the development of guidelines to help mitigate the risk of cold-related injuries during prolonged exposures to extreme-cold environments.

Musculoskeletal injuries, a major impediment to medical military readiness, are often associated with muscle fatigue caused by prolonged, repeated activities, including running and walking with load carriage. Here, we investigated the biomechanics of women running without a load and walking with load carriage to identify kinetic and kinematic parameters affected by exertion-related changes under both conditions. We collected motion-capture data and computed tomography images from 20 young, healthy women running without a load for 5 km at a subject-specific pace and walking with a 22.7-kg load for 5 km at a speed of 1.5 m/s. We developed individualized musculoskeletal models for each condition and characterized changes in kinetic and kinematic parameters for each subject between the start (0 km) and end of each 5-km session. We found that peak trunk flexion angle increased for both conditions (effect size ≥ 0.80), with the same trend for 18 of the 20 subjects. Peak hip extension, ankle dorsiflexion, and subtalar inversion internal moments had the same directional changes in both conditions, although not always meeting the significance threshold adjusted for multiple comparisons or yielding an effect size ≥ 0.80. In conclusion, this exploratory study identified exertional effects in women running without a load or walking with load carriage that resulted in biomechanical gait changes in the same direction, which could be used to develop countermeasures to reduce the risk of musculoskeletal injury.

Fluid resuscitation immediately following a hemorrhagic injury improves clinical outcome. However, future military conflicts are expected to result in mass-casualty incidents with limited availability of fluid-resuscitation resources. Here, we developed and assessed the performance of a reinforcement learning AI method that optimized clinical outcome and fluid allocation under constrained resources. We generated a large cohort of synthetic trauma casualties using a validated cardio-respiratory computational model, simulating vital-sign time-series data for realistic battlefield scenarios involving hemorrhage, tourniquet application, and fluid resuscitation. For each casualty, we simulated three intervention options—infusion or no infusion of one fluid unit every 30 min over 90 min—and assessed whether the interventions restored the vital signs to “healthy” levels. Using these data, we trained the reinforcement learning model to predict the optimal sequence of interventions that maximized the number of casualties restored while minimizing fluid utilization. Using independent simulated data, we found that the AI model was twice as efficient and restored more than twice as many casualties as the current standard of care across varying numbers of casualties and resource limitations. These results highlight the model’s potential to enable personalized interventions, enhance treatment efficiency, and support automated medical decision-making in resource-constrained environments.

Airway obstruction is the second leading cause of potentially survivable death on the battlefield. Managing airway obstruction resulting from severe traumatic injuries to the head and neck, which can distort the airway anatomy, poses significant challenges to combat medics. The medic’s ability to make quick and effective interventions to secure the airway in austere, tactical environments is also highly dependent on their training and experience as well as the availability of advanced medical equipment. Artificial intelligence (AI) algorithms can help augment the competency and capability of medics to care for combat casualties by enhancing their training, assessing their skills, and helping identify the most appropriate medical interventions in real time that are likely to result in desired clinical outcomes. However, the training and assessment of AI algorithms require massive amounts of real-world, vital-sign data. Because such data are not currently available for casualties with airway obstruction, an alternative approach is to rely on relevant synthetic data generated by high-fidelity computational physiological models. Here, by adding new respiratory control and respiratory mechanics components, we extended our previously developed and validated human cardio-respiratory (CR) model for representing hemorrhagic injury to account for the physiological effects of airway obstruction on vital signs. We calibrated and validated the extended CR model using data from six human studies and two pig studies, which reported vital-sign changes in response to airway obstruction, changes in arterial oxygen (O ₂ ) and carbon dioxide (CO ₂ ) pressure, changes in the fraction of inspired O ₂ and CO ₂ , and hemorrhage followed by ventilation changes. On average, the extended CR model achieved good prediction accuracy, with root mean square errors of 1.3 L/min for minute ventilation, 1.6 breaths/min for respiratory rate, 4.5% for oxygen saturation, 3.5 mmHg for end-tidal CO ₂ , 11.6 mmHg for systolic blood pressure, 7.8 mmHg for mean arterial pressure, and 9.8 beats/min for heart rate. With this enhancement, the extended CR model can now be used to generate realistic synthetic trauma datasets for the two leading causes of potentially survivable battlefield deaths, hemorrhagic injury and airway obstruction, and help develop AI decision-support tools for combat medics.

In large-scale combat operations, decision-support systems based on artificial intelligence technology can augment the capability and capacity of medics to care for combat casualties. The development and assessment of such systems require large amounts of vital-sign data, which are impractical to obtain from clinical or experimental studies but can be generated using high-fidelity computational models. We previously developed and validated a human cardio-respiratory model (the CR model) that accounts for both cardiovascular and respiratory responses to hemorrhagic injuries and resuscitation with six commonly used fluid types. Here, we extended the CR model to represent the effect of ketamine analgesia on the vital signs by integrating two existing pharmacokinetic-pharmacodynamic models of ketamine [one representing the effect of ketamine on mean arterial pressure (MAP) and another on cardiac output], with two feedback controllers described within the CR model. We validated the extended model using experimental data of ketamine administration in three human studies for healthy individuals. Simulation results showed that the model captured the general trend of the experimental data for MAP and heart rate (HR) with root mean square errors of 6.17 mmHg and 7.51 beats/min, respectively. In addition, in simulations of hemorrhagic injury, ketamine administration rapidly increased MAP and cardiac output, but the magnitude of the increase decreased with hemorrhage severity. MAP increased by 40%, 30%, and 15% of its baseline value in the presence of no, moderate, and severe hemorrhage, respectively. Given that most wounded Service members receive pain medication in the form of ketamine, the ability to account for its effects on vital signs will allow us to generate more comprehensive synthetic data of combat casualties.

Hemorrhage is the leading cause of preventable death on the battlefield, yet combat medics lack clinical decision support systems to help stratify hemorrhage risk in trauma casualties. We previously trained the Automated Processing of the Physiological Registry for Assessment of Injury Severity — Hemorrhage Risk Index (APPRAISE-HRI) software to associate patterns in vital signs (heart rate and blood pressure) collected from trauma patients with three HRI levels: I (low), II (average), or III (high). To independently validate APPRAISE-HRI and obtain U.S. Food and Drug Administration (FDA) clearance, we collected trauma registry and continuous vital sign data from 5895 trauma patients (543 with hemorrhagic injuries and 5352 controls) in an emergency department or during prehospital transport to one of eight medical centers. The study outcome was hemorrhagic injury, defined by documented injuries and blood transfusion. Using the likelihood ratio to assess the ability of APPRAISE-HRI to stratify hemorrhage risk, we found that hemorrhagic patients were 6.88 times as likely as controls to be at level III, strongly suggesting the presence of hemorrhage at this level. Similarly, hemorrhagic patients were 0.18 times as likely as controls to be at level I, suggesting the absence of hemorrhage at this level. Hemorrhagic patients were almost as likely as controls to be at level II (0.70 times as likely). Subsequently, the U.S. Department of Defense obtained FDA 510(k) clearance for the artificial intelligence–enabled APPRAISE-HRI Class II device (K233249), the first software as a medical device approved for assessing hemorrhage risk in trauma patients, allowing for triage and identification of casualties who need immediate attention and evacuation. (Funded by the U.S. Army Medical Materiel Development Activity and the Combat Casualty Care Program Area Directorate (CCCPAD) of the U.S. Army Medical Research and Development Command (USAMRDC), Fort Detrick, MD and others.)

Objective: Musculoskeletal injuries pose a health threat to U.S. Service members. In particular, the physical demands of walking and running with load carriage contribute to a high incidence of musculoskeletal injuries of the lower back. Active ankle exoskeleton devices are promising technologies that may help mitigate the impact of load carriage on the incidence of these injuries. However, the safe extended use of these devices requires an understanding of their beneficial or detrimental effects on the lumbar spine. In this pilot study, we investigated the impact of walking with an ankle exoskeleton device on lumbar biomechanical responses.Methods: We collected motion-capture data and computed tomography images for five young, healthy men walking with a 22.7 kg (50-lb) load for 5 km at a speed of 1.3 m/s, with and without an active ankle exoskeleton (ExoBoot EB60). We developed individualized musculoskeletal and finite-element models to characterize the effects of walking distance and ExoBoot use on the trunk flexion angle, joint reaction force at the L4-L5 joint, and stress on the L4-L5 intervertebral disc annulus.Results: While not statistically significant, we found that the peak trunk flexion angle and the peak annulus stress increased by 16% and 12%, respectively, after walking 5 km with the ExoBoot, and by 34% and 25%, respectively, without it. Similarly, the peak L4-L5 joint reaction force minimally increased by 4% with the ExoBoot, while it increased by 22% without the device.Conclusion: ExoBoot use likely attenuates the effect of fatigue on the lumbar spine induced by walking with load carriage.