A machine learning framework to classify musculoskeletal injury risk groups in military service members

BackgroundMusculoskeletal injuries (MSKIs) are endemic in military populations. Thus, it is essential to identify and mitigate MSKI risks. Time-to-event machine learning models utilizing self-reported questionnaires or existing data (e.g., electronic health records) may aid in creating efficient risk screening tools.MethodsA total of 4,222 U.S. Army Service members completed a self-report MSKI risk screen as part of their unit's standard in-processing. Additionally, participants' MSKI and demographic data were abstracted from electronic health record data. Survival machine learning models (Cox proportional hazard regression (COX), COX with splines, conditional inference trees, and random forest) were deployed to develop a predictive model on the training data (75%; n = 2,963) for MSKI risk over varying time horizons (30, 90, 180, and 365 days) and were evaluated on the testing data (25%; n = 987). Probability of predicted risk (0.00–1.00) from the final model stratified Service members into quartiles based on MSKI risk.ResultsThe COX model demonstrated the best model performance over the time horizons. The time-dependent area under the curve ranged from 0.73 to 0.70 at 30 and 180 days. The index prediction accuracy (IPA) was 12% better at 180 days than the IPA of the null model (0 variables). Within the COX model, “other” race, more self-reported pain items during the movement screens, female gender, and prior MSKI demonstrated the largest hazard ratios. When predicted probability was binned into quartiles, at 180 days, the highest risk bin had an MSKI incidence rate of 2,130.82 ± 171.15 per 1,000 person-years and incidence rate ratio of 4.74 (95% confidence interval: 3.44, 6.54) compared to the lowest risk bin.ConclusionSelf-reported questionnaires and existing data can be used to create a machine learning algorithm to identify Service members' MSKI risk profiles. Further research should develop more granular Service member-specific MSKI screening tools and create MSKI risk mitigation strategies based on these screenings

Static and dynamic single leg postural control performance during dual-task paradigms

ABSTRACTCombining dynamic postural control assessments and cognitive tasks may give clinicians a more accurate indication of postural control under sport-like conditions compared to single-task assessments. We examined postural control, cognitive and squatting performance of healthy individuals during static and dynamic postural control assessments in single- and dual-task paradigms. Thirty participants (female = 22, male = 8; age = 20.8 ± 1.6 years, height = 157.9 ± 13.0 cm, mass = 67.8 ± 20.6 kg) completed single-leg stance and single-leg squat assessments on a force plate individually (single-task) and concurrently (dual-task) with two cognitive assessments, a modified Stroop test and the Brooks Spatial Memory Test. Outcomes included centre of pressure speed, 95% confidence ellipse, squat depth and speed and cognitive test measures (percentage of correct answers and reaction time). Postural control performance varied between postural control assessments and testing paradigms. Participants did not squat..

Toward Facilitating the Collection and Utilization of Patient-reported Outcomes in the Military Health System: Lessons Learned from a Pragmatic Clinical Trial on Physical Therapy Management for Low Back Pain

In pursuit of delivering \u27the right care to the right patient at the right time,\u27 the Military Health System (MHS) advocates for collecting and using patient-reported outcomes (PROs) to help demonstrate value-based care.1 PROs identify patients’ perceptions of their health, function, and well-being, which can enhance patient-centered communication and guide data-driven healthcare. 2 The MHS recognizes the value of incorporating PRO data into clinical decision-making and has established a number of platforms for PRO collection across health conditions (eg, behavioral health, traumatic brain injury, musculoskeletal injuries). The Military Orthopedics Tracking Injuries and Outcomes Network (MOTION)3 was started as a research endeavor specific to collecting PROs relevant to postsurgical conditions, which later expanded to cover rehabilitation settings and all musculoskeletal injuries. In MHS physical therapy clinics, the Defense Health Agency’s Clinical Assessment Management Portal (CAMP), a digital PRO collection platform, enables point-of-care capture of MOTION-recommended PROs

Perfusion Measures and Outcomes (PERForm) registry: First annual report

Background: The Perfusion Measures and Outcomes (PERForm) registry was established in 2010 to advance cardiopulmonary bypass (CPB) practices and outcomes. The registry is maintained through the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative and is the official registry of the American Society of Extracorporeal Technology. Methods: This first annual PERForm registry report summarizes patient characteristics as well as CPB-related practice patterns in adult (≥18 years of age) patients between 2019 and 2022 from 42 participating hospitals. Data from PERForm are probabilistically matched to institutional surgical registry data. Trends in myocardial protection, glucose, anticoagulation, temperature, anemia (hematocrit), and fluid management are summarized. Additionally, trends in equipment (hardware/disposables) utilization and employed patient safety practices are reported. Results: A total of 40,777 adult patients undergoing CPB were matched to institutional surgical registry data from 42 hospitals. Among these patients, 54.9% underwent a CABG procedure, 71.6% were male, and the median (IQR) age was 66.0 [58.0, 73.0] years. Overall, 33.1% of the CPB procedures utilized a roller pump for the arterial pump device, and a perfusion checklist was employed 99.6% of the time. The use of conventional ultrafiltration decreased over the study period (2019 vs. 2022; 27.1% vs. 24.9%) while the median (IQR) last hematocrit on CPB has remained stable [27.0 (24.0, 30.0) vs. 27.0 (24.0, 30.0)]. Pump sucker termination before protamine administration increased over the study period: (54.8% vs. 75.9%). Conclusion: Few robust clinical registries exist to collect data regarding the practice of CPB. Although data submitted to the PERForm registry demonstrate overall compliance with published perfusion evidence-based guidelines, noted opportunities to advance patient safety and outcomes remain

High School Football Injury Rates and Services by Athletic Trainer Employment Status

Context Reported injury rates and services in sports injury surveillance may be influenced by the employment setting of the certified athletic trainers (ATs) reporting these data. Objective To determine whether injury rates and the average number of AT services per injury in high school football varied by AT employment status. Design Cross-sectional study. Setting We used data from the National Athletic Treatment, Injury and Outcomes Network and surveyed ATs about their employment setting. Patients or Other Participants Forty-four responding ATs (37.9% of all National Athletic Treatment, Injury and Outcomes Network participants) worked at high schools with football programs and were included in this study. Fourteen ATs were full-time employees of the high school, and 30 ATs were employed as outreach ATs (ie, full-time and part-time ATs from nearby clinics, hospitals, and graduate school programs). Main Outcome Measure(s) We calculated injury rates per 1000 athlete-exposures and average number of AT services per injury. Results Reported injury rates and services per injury were greater among full-time school employees compared with outreach ATs. However, injury rates did not differ when restricted to time-loss injuries only. Conclusions Our findings suggest that ATs who are full-time school employees may be able to identify and care for more patients with injuries

High School Football Injury Rates and Services by Athletic Trainer Employment Status

Context Reported injury rates and services in sports injury surveillance may be influenced by the employment setting of the certified athletic trainers (ATs) reporting these data. Objective To determine whether injury rates and the average number of AT services per injury in high school football varied by AT employment status. Design Cross-sectional study. Setting We used data from the National Athletic Treatment, Injury and Outcomes Network and surveyed ATs about their employment setting. Patients or Other Participants Forty-four responding ATs (37.9% of all National Athletic Treatment, Injury and Outcomes Network participants) worked at high schools with football programs and were included in this study. Fourteen ATs were full-time employees of the high school, and 30 ATs were employed as outreach ATs (ie, full-time and part-time ATs from nearby clinics, hospitals, and graduate school programs). Main Outcome Measure(s) We calculated injury rates per 1000 athlete-exposures and average number of AT services per injury. Results Reported injury rates and services per injury were greater among full-time school employees compared with outreach ATs. However, injury rates did not differ when restricted to time-loss injuries only. Conclusions Our findings suggest that ATs who are full-time school employees may be able to identify and care for more patients with injuries

Musculoskeletal Injury Risk Stratification: A Traffic Light System for Military Service Members

Risk factor identification is a critical first step in informing musculoskeletal injury (MSKI) risk mitigation strategies. This investigation aimed to determine if a self-reported MSKI risk assessment can accurately identify military service members at greater MSKI risk and determine whether a traffic light model can differentiate service members’ MSKI risks. A retrospective cohort study was conducted using existing self-reported MSKI risk assessment data and MSKI data from the Military Health System. A total of 2520 military service members (2219 males: age 23.49 ± 5.17 y, BMI 25.11 ± 2.94 kg/m2; and 301 females: age 24.23 ± 5.85 y, BMI 25.59 ± 3.20 kg/m2, respectively) completed the MSKI risk assessment during in-processing. The risk assessment consisted of 16 self-report items regarding demographics, general health, physical fitness, and pain experienced during movement screens. These 16 data points were converted to 11 variables of interest. For each variable, service members were dichotomized as at risk or not at risk. Nine of the 11 variables were associated with a greater MSKI risk and were thus considered as risk factors for the traffic light model. Each traffic light model included three color codes (i.e., green, amber, and red) to designate risk (i.e., low, moderate, and high). Four traffic light models were generated to examine the risk and overall precision of different cut-off values for the amber and red categories. In all four models, service members categorized as amber [hazard ratio (HR) = 1.38–1.70] or red (HR = 2.67–5.82) were at a greater MSKI risk. The traffic light model may help prioritize service members who require individualized orthopedic care and MSKI risk mitigation plans

Kinematic and neuromuscular relationships between lower extremity clinical movement assessments\u3csup\u3e*\u3c/sup\u3e

© 2017 Informa UK Limited, trading as Taylor & Francis Group. Lower extremity injuries have immediate and long-term consequences. Lower extremity movement assessments can assist with identifying individuals at greater injury risk and guide injury prevention interventions. Movement assessments identify similar movement characteristics and evidence suggests large magnitude kinematic relationships exist between movement patterns observed across assessments; however, the magnitude of the relationships for electromyographic (EMG) measures across movement assessments remains largely unknown. This study examined relationships between lower extremity kinematic and EMG measures during jump landings and single leg squats. Lower extremity three-dimensional kinematic and EMG data were sampled from healthy adults (males = 20, females = 20) during the movement assessments. Pearson correlations examined the relationships of the kinematic and EMG measures and paired samples t-tests compared mean kinematic and EMG measures between the assessments. Overall, significant moderate correlations were observed for lower extremity kinematic (ravg = 0.41, rrange = 0.10–0.61) and EMG (ravg = 0.47, rrange = 0.32–0.80) measures across assessments. Kinematic and EMG measures were greater during the jump landings. Jump landings and single leg squats place different demands on the body and necessitate different kinematic and EMG patterns, such that these measures are not highly correlated between assessments. Clinicians should, therefore, use multiple assessments to identify aberrant movement and neuromuscular control patterns so that comprehensive interventions can be implemented

Trunk and Lower Extremity Movement Patterns, Stress Fracture Risk Factors, and Biomarkers of Bone Turnover in Military Trainees.

CONTEXT: Military service members commonly sustain lower extremity stress fractures (SFx). How SFx risk factors influence bone metabolism is unknown. Understanding how SFx risk factors influence bone metabolism may help to optimize risk-mitigation strategies. OBJECTIVE: To determine how SFx risk factors influence bone metabolism. DESIGN: Cross-sectional study. SETTING: Military service academy. PATIENTS OR OTHER PARTICIPANTS: Forty-five men (agepre = 18.56 ± 1.39 years, heightpre = 176.95 ± 7.29 cm, masspre = 77.20 ± 9.40 kg; body mass indexpre = 24.68 ± 2.87) who completed Cadet Basic Training (CBT). Individuals with neurologic or metabolic disorders were excluded. INTERVENTION(S): We assessed SFx risk factors (independent variables) with (1) the Landing Error Scoring System (LESS), (2) self-reported injury and physical activity questionnaires, and (3) physical fitness tests. We assessed bone biomarkers (dependent variables; procollagen type I amino-terminal propeptide [PINP] and cross-linked collagen telopeptide [CTx-1]) via serum. MAIN OUTCOME MEASURE(S): A markerless motion-capture system was used to analyze trunk and lower extremity biomechanics via the LESS. Serum samples were collected post-CBT; enzyme-linked immunosorbent assays determined PINP and CTx-1 concentrations, and PINP : CTx-1 ratios were calculated. Linear regression models demonstrated associations between SFx risk factors and PINP and CTx-1 concentrations and PINP : CTx-1 ratio. Biomarker concentration mean differences with 95% confidence intervals were calculated. Significance was set a priori using α ≤ .10 for simple and α ≤ .05 for multiple regression analyses. RESULTS: The multiple regression models incorporating LESS and SFx risk factor data predicted the PINP concentration (R2 = 0.47, P = .02) and PINP : CTx-1 ratio (R2 = 0.66, P = .01). The PINP concentration was increased by foot internal rotation, trunk flexion, CBT injury, sit-up score, and pre- to post-CBT mass changes. The CTx-1 concentration was increased by heel-to-toe landing and post-CBT mass. The PINP : CTx-1 ratio was increased by foot internal rotation, lower extremity sagittal-plane displacement (inversely), CBT injury, sit-up score, and pre- to post-CBT mass changes. CONCLUSIONS: Stress fracture risk factors accounted for 66% of the PINP : CTx-1 ratio variability, a potential surrogate for bone health. Our findings provide insight into how SFx risk factors influence bone health. This information can help guide SFx risk-mitigation strategies