Is the Lifetime Malignancy Risk in United States Military Personnel Sustaining Combat-related Trauma Increased Because of Radiation Exposure From Diagnostic Imaging?

BACKGROUND: Patients with complex polytrauma in the military and civilian settings are often exposed to substantial diagnostic medical radiation because of serial imaging studies for injury diagnosis and subsequent management. This cumulative radiation exposure may increase the risk of subsequent malignancy. This is particularly true for combat-injured servicemembers who receive care at a variety of facilities worldwide. Currently, there is no coordinated effort to track the amount of radiation exposure each servicemember receives, nor a surveillance program to follow such patients in the long term. It is important to assess whether military servicemembers are exposed to excessive diagnostic radiation to mitigate or prevent such occurrences and monitor for carcinogenesis, when necessary. The cumulative amount of radiation exposure for combat-wounded and noncombat-wounded servicemembers has not been described, and it remains unknown whether diagnostic radiation exposure meets thresholds for an increased risk of carcinogenesis. QUESTIONS/PURPOSES: We performed this study to (1) quantify the amount of exposure for combat-wounded servicemembers based on medical imaging in the first year after injury and compare those exposures with noncombat-related trauma, and (2) determine whether the cumulative dose of radiation correlates to the Injury Severity Score (ISS) across the combat-wounded and noncombat-wounded population combined. METHODS: We performed a retrospective study of servicemembers who sustained combat or noncombat trauma and were treated at Walter Reed National Military Medical Center from 2005 to 2018. We evaluated patients using the Department of Defense Trauma Registry. After consolidating redundant records, the dataset included 3812 unique servicemember encounters. Three percent (104 of 3812) were excluded because of missing radiation exposure data in the electronic medical record. The final cohort included 3708 servicemembers who had combat or noncombat injury trauma, with a mean age at the time of injury of 26 ± 6 years and a mean ISS of 18 ± 12. The most common combat trauma mechanisms of injury were blast (in 65% [2415 of 3708 patients]), followed by high-velocity gunshot wounds (in 22% [815 of 3708 patients]). We calculated the cumulative diagnostic radiation dose exposure at 1 year post-traumatic injury in patients with combat-related trauma and those with noncombat trauma. We did this by multiplying the number of imaging studies by the standardized effective radiation dose for each imaging study type. We then performed analysis of variance for four data subsets (battle combat trauma, nonbattle civilian trauma, high ISS, and high radiation exposure [> 50 mSv]) independently. To evaluate whether the total number of imaging studies, radiation exposure, and ISS values differed between battle-wounded and nonbattle-wounded patients, we performed a pairwise t-test. RESULTS: The mean radiation exposure for combat-related injuries was 35 ± 26 mSv while the mean radiation exposure for noncombat-related injuries was 22 ± 33 mSv in the first year after injury. In the first year after trauma, 44% of patients (1626 of 3708) were exposed to high levels of radiation that were greater than 20 mSv, and 23% (840 of 3708) were exposed to very high levels of radiation that were greater than 50 mSv. Servicemembers with combat trauma-related injuries had eight more imaging studies than those who sustained noncombat injuries. Servicemembers with combat trauma injuries (35 ± 26 mSv) were exposed to more radiation (approximately 4 mSv) than patients treated for noncombat injuries (22 ± 33 mSv) (p = 0.01). We found that servicemembers with combat injuries had a higher ISS than servicemembers with noncombat trauma (p < 0.001). We found a positive correlation between radiation exposure and ISS for servicemembers. The positive relationship between radiation exposure and ISS held for combat trauma (r 2 = 0.24; p < 0.001), noncombat trauma (r 2 = 0.20; p < 0.001), servicemembers with a high ISS (r 2 = 0.10; p < 0.001), and servicemembers exposed to high doses of radiation (r 2 = 0.09; p < 0.001). CONCLUSION: These data should be used during clinical decision-making and patient counseling at military treatment facilities and might provide guidance to the Defense Health Agency. These recommendations will help determine whether the benefits of further imaging outweigh the risk of carcinogenesis. If not, we need to develop interdisciplinary clinical practice guidelines to reduce or minimize radiation exposure. It is important for treating physicians to seriously weigh the risk and benefits of every imaging study ordered because each test does not come without a cumulative risk. LEVEL OF EVIDENCE: Level III, therapeutic study.

Survival of anterior cruciate ligament reconstructions in active-duty military populations.

PURPOSE: Anterior cruciate ligament tears and anterior cruciate ligament reconstruction (ACLR) are common in young athletes. The modifiable and non-modifiable factors contributing to ACLR failure and reoperation are incompletely understood. The purpose of this study was to determine ACLR failure rates in a physically high-demand population and identify the patient-specific risk factors, including prolonged time between diagnosis and surgical correction, that portend failure. METHODS: A consecutive series of military service members with ACLR with and without concomitant procedures (meniscus [M] and/or cartilage [C]) done at military facilities between 2008 and 2011 was completed via the Military Health System Data Repository. This was a consecutive series of patients without a history of knee surgery for two years prior to the primary ACLR. Kaplan-Meier survival curves were estimated and evaluated with Wilcoxon test. Cox proportional hazard models calculated hazard ratios (HR) with 95% confidence intervals (95% CI) to identify demographic and surgical factors that influenced ACLR failure. RESULTS: Of the 2735 primary ACLRs included in the study, 484/2,735 (18%) experienced ACLR failure within four years, including (261/2,735) (10%) undergoing revision ACLR and (224/2,735) (8%) due to medical separation. The factors that increased failure include Army Service (HR 2.19, 95% CI 1.67, 2.87), > 180 days from injury to ACLR (HR 1.550, 95% CI 1.157, 2.076), tobacco use (HR 1.429 95% CI 1.174, 1.738), and younger patient age (HR 1.024, 95% CI 1.004, 1.044). CONCLUSION: The overall clinical failure rate of service members with ACLR is 17.7% with minimum four-year follow-up, where more patients are likely to fail due to revision surgery than medical separation. The cumulative probability of survival at 4 years was 78.5%. Smoking cessation and treating ACLR patients promptly are modifiable risk factors impacting either graft failure or medical separation. LEVEL OF EVIDENCE: Level III.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool.

BACKGROUND: Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry. METHODS: We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989-2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models' clinical utility. We characterized the magnitude and direction of model features. RESULTS: The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV). CONCLUSIONS: We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

A Machine-Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery.

PURPOSE: To develop a machine-learning algorithm and clinician-friendly tool predicting the likelihood of prolonged opioid use (>90 days) following hip arthroscopy. METHODS: The Military Data Repository was queried for all adult patients undergoing arthroscopic hip surgery between 2012 and 2017. Demographic, health history, and prescription records were extracted for all included patients. Opioid use was divided into preoperative use (30-365 days before surgery), perioperative use (30 days before surgery through 14 days after surgery), postoperative use (14-90 days after surgery), and prolonged postoperative use (90-365 days after surgery). Six machine-learning algorithms (Naïve Bayes, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Elastic Net Regularization, and artificial neural network) were developed. Area under the receiver operating curve and Brier scores were calculated for each model. Decision curve analysis was applied to assess clinical utility. Local-Interpretable Model-Agnostic Explanations were used to demonstrate factor weights within the selected model. RESULTS: A total of 6,760 patients were included, of whom 2,762 (40.9%) filled at least 1 opioid prescription >90 days after surgery. The artificial neural network model showed superior discrimination and calibration with area under the receiver operating curve = 0.71 (95% confidence interval 0.68-0.74) and Brier score = 0.21 (95% confidence interval 0.20-0.22). Postsurgical opioid use, age, and preoperative opioid use had the most influence on model outcome. Lesser factors included the presence of a psychological comorbidity and strong history of a substance use disorder. CONCLUSIONS: The artificial neural network model shows sufficient validity and discrimination for use in clinical practice. The 5 identified factors (age, preoperative opioid use, postoperative opioid use, presence of a mental health comorbidity, and presence of a preoperative substance use disorder) accurately predict the likelihood of prolonged opioid use following hip arthroscopy. LEVEL OF EVIDENCE: III, retrospective comparative prognostic trial.

Survival in Pediatric, Adolescent, and Young Adult Patients With Sarcoma in the Military Health System: Comparison With the SEER Population.

BACKGROUND: We sought to compare survival outcomes of sarcomas in the pediatric and adolescent/young adult populations with universal care access in the Military Health System (MHS) to those from the United States general population. METHODS: We compared data from the Department of Defense's (DoD) Automated Central Tumor Registry (ACTUR) and the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program on the overall survival of patients 24 years or younger with histologically or microscopically confirmed sarcoma between diagnosed between January 1, 1987, and December 31, 2013. The Kaplan-Meier survival curves were used to compare survival between the 2 patient populations. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing ACTUR relative to SEER. RESULTS: The final analysis included 309 and 1236 bone sarcoma cases and 465 and 1860 soft tissue sarcoma cases from ACTUR and SEER, respectively. Cox proportional hazards analysis showed soft tissue sarcoma patients in ACTUR had significantly better overall (HR=0.73, 95% CI=0.55-0.98) and 5-year overall (HR=0.63, 95% CI=0.46-0.86) survival compared with SEER patients, but no significant difference in overall or 5-year overall survival between ACTUR and SEER patients with bone sarcoma. CONCLUSION: Survival data from the ACTUR database demonstrated significantly improved overall survival for soft tissue sarcomas and equivalent survival in bone sarcomas compared with that reported by SEER.

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

BACKGROUND: PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate. QUESTION/PURPOSES: Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions. METHODS: After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]). RESULTS: The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use. CONCLUSIONS: We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools. LEVEL OF EVIDENCE: Level III, therapeutic study.

Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civilians and military service members, and it is difficult to anticipate which patients are at risk for prolonged opioid use. QUESTIONS/PURPOSES: (1) To build a cross-validated model that predicts risk of prolonged opioid use after a specific orthopaedic procedure (ACL reconstruction), (2) To describe the relationships between prognostic and outcome variables, and (3) To determine the clinical utility of a predictive model using a decision curve analysis (as measured by our predictive system's ability to effectively identify high-risk patients and allow for preventative measures to be taken to ensure a successful procedure process). METHODS: We used the Military Analysis and Reporting Tool (M2) to search the Military Health System Data Repository for all patients undergoing arthroscopically assisted ACL reconstruction (Current Procedure Terminology code 29888) from January 2012 through December 2015 with a minimum of 90 days postoperative follow-up. In total, 10,919 patients met the inclusion criteria, most of whom were young men on active duty. We obtained complete opioid prescription filling histories from the Military Health System Data Repository's pharmacy records. We extracted data including patient demographics, military characteristics, and pharmacy data. A total of 3.3% of the data was missing. To curate and impute all missing variables, we used a random forest algorithm. We shuffled and split the data into 80% training and 20% hold-out sets, balanced by outcome variable (Outcome90Days). Next, the training set was further split into training and validation sets. Each model was built on the training data set, tuned with the validation set as applicable, and finally tested on the separate hold-out dataset. We chose four predictive models to develop, at the end choosing the best-fit model for implementation. Logistic regression, random forest, Bayesian belief network, and gradient boosting machine models were the four chosen models based on type of analysis (classification). Each were trained to estimate the likelihood of prolonged opioid use, defined as any opioid prescription filled more than 90 days after anterior cruciate reconstruction. After this, we tested the models on our holdout set and performed an area under the curve analysis concordance statistic, calculated the Brier score, and performed a decision curve analysis for validation. Then, we chose the method that produced the most suitable analysis results and, consequently, predictive power across the three calculations. Based on the calculations, the gradient boosting machine model was selected for future implementation. We systematically selected features and tuned the gradient boosting machine to produce a working predictive model. We performed area under the curve, Brier, and decision curve analysis calculations for the final model to test its viability and gain an understanding of whether it is possible to predict prolonged opioid use. RESULTS: Four predictive models were successfully developed using gradient boosting machine, logistic regression, Bayesian belief network, and random forest methods. After applying the Boruta algorithm for feature selection based on a 100-tree random forest algorithm, features were narrowed to a final seven features. The most influential features with a positive association with prolonged opioid use are preoperative morphine equivalents (yes), particular pharmacy ordering sites locations, shorter deployment time, and younger age. Those observed to have a negative association with prolonged opioid use are particular pharmacy ordering sites locations, preoperative morphine equivalents (no), longer deployment, race (American Indian or Alaskan native) and rank (junior enlisted).On internal validation, the models showed accuracy for predicting prolonged opioid use with AUC greater than our benchmark cutoff 0.70; random forest were 0.76 (95% confidence interval 0.73 to 0.79), 0.76 (95% CI 0.73 to 0.78), 0.73 (95% CI 0.71 to 0.76), and 0.72 (95% CI 0.69 to 0.75), respectively. Although the results from logistic regression and gradient boosting machines were very similar, only one model can be used in implementation. Based on our calculation of the Brier score, area under the curve, and decision curve analysis, we chose the gradient boosting machine as the final model. After selecting features and tuning the chosen gradient boosting machine, we saw an incremental improvement in our implementation model; the final model is accurate, with a Brier score of 0.10 (95% CI 0.09 to 0.11) and area under the curve of 0.77 (95% CI 0.75 to 0.80). It also shows the best clinical utility in a decision curve analysis. CONCLUSIONS: These scores support our claim that it is possible to predict which patients are at risk of prolonged opioid use, as seen by the appropriate range of hold-out analysis calculations. Current opioid guidelines recommend preoperative identification of at-risk patients, but available tools for this purpose are crude, largely focusing on identifying the presence (but not relative contributions) of various risk factors and screening for depression. The power of this model is that it will permit the development of a true clinical decision-support tool, which risk-stratifies individual patients with a single numerical score that is easily understandable to both patient and surgeon. Probabilistic models provide insight into how clinical factors are conditionally related. Not only will this gradient boosting machine be used to help understand factors contributing to opiate misuse after ACL reconstruction, but also it will allow orthopaedic surgeons to identify at-risk patients before surgery and offer increased support and monitoring to prevent opioid abuse and dependency. LEVEL OF EVIDENCE: Level III, therapeutic study.

Preoperative Smoking Cessation as a Durable Form of Long-Term Smoking Cessation.

Smokers who undergo total joint arthroplasty (TJA) face increased rates of medical and surgical complications that can be reduced by preoperative smoking cessation. We investigated the long-term durability of preoperative smoking cessation among TJA patients. Twenty-seven TJA patients who were identified as having an active history of smoking at the preoperative appointment before TJA consented to telephone survey about their perioperative and current smoking status. Average time from operation to survey was 3.7 years. Of the 27 patients, 21 (77.8%) were identified as having quit smoking prior to surgery. Of these 21 patients, 10 (47.6%) self-reported continued abstinence from smoking at the time of survey. Our cessation rate was significantly lower than reported long-term smoking cessation rates with standard therapies (p < 0.001). Our results suggest that preoperative counseling and a requirement for smoking-cessation prior to elective TJA may have long-term durability that exceeds that of popular reported methods. (Journal of Surgical Orthopaedic Advances 29(2):103-105, 2020).

Bone Sarcoma Survival in the US Military Health System: Comparison With the SEER Program.

BACKGROUND: Access to care is associated with cancer survival. The US Military Health System (MHS) provides universal health care to all beneficiaries. However, it is unknown whether survival among patients with bone sarcoma in a health system providing universal care is better than that in the general population. The aim of the study was to compare survival of patients with bone sarcoma in the US MHS with that of the US general population. METHODS: The MHS data were obtained from the Department of Defense Automated Central Tumor Registry (ACTUR). The US general population data were obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) registry. Adult patients were defined as those aged 25 years or older with a histologically confirmed musculoskeletal bone sarcoma diagnosed from January 1, 1987, to December 31, 2013. Kaplan-Meier survival curves and multivariable Cox proportional hazards models were used to compare the overall survival of the two populations. RESULTS: The final analysis included 2,273 bone sarcoma cases from ACTUR and 9,092 bone sarcoma cases from SEER. ACTUR patients had significant lower 5-year all-cause death (hazard ratio = 0.72; 95% CI, 0.66 to 0.78) after adjustment for the potential confounders. ACTUR patients with bone sarcoma also exhibited significantly lower risk of all-cause death during the entire follow-up period than the SEER patients (hazard ratio = 0.75; 95% CI, 0.6 to 0.81). CONCLUSIONS: MHS beneficiaries with bone sarcoma may have longer survival than SEER patients. Our findings support the role of universal access to high-quality care in improving bone sarcoma outcomes.

Team Approach: Osseointegration Amputation Surgery.

¼ The purpose of this article was to review the multidisciplinary, team-based approach necessary for the optimal management of patients with limb loss undergoing osseointegration surgery.¼ In this study, we describe the interdisciplinary process of screening, counseling, and surgical and rehabilitation considerations with an emphasis on principles rather than specific implants or techniques.¼ Integrated perioperative management and long-term surveillance are crucial to ensure the best possible outcomes.¼ We hope this model will service as an implant-agnostic guide to others seeking to development an osseointegration center of excellence.

Periprosthetic Joint Infection in Patients With Arthroplasty Undergoing Perioperative Colonoscopy.

Importance: Periprosthetic joint infection (PJI) is a rare but devastating complication. Most patients undergoing total joint arthroplasty (TJA) also need routine screening colonoscopy, in which transient bacteremia may be a potential source for hematogenous PJI. Patients and surgeons must decide on an optimal time span or sequence for these 2 generally elective procedures, but no such guidelines currently exist. Objective: To evaluate associations of colonoscopy with the risk of post-TJA PJI for the development of clinical practice recommendations for colonoscopy screening in patients undergoing TJA. Design, Setting, and Participants: This retrospective cohort study of Military Health System (MHS) beneficiaries older than 45 years who underwent TJA from January 1, 2010, to December 31, 2016, used propensity score matching and logistic regression to evaluate associations of colonoscopy with PJI risk. Statistical analyses were conducted between January and October 2023. Exposure: Colonoscopy status was defined by Current Procedural Terminology code for diagnostic colonoscopy within 6 months before or 6 months after TJA. Main Outcomes and Measures: Periprosthetic joint infection status was defined by a PJI International Classification of Diseases code within 1 year after TJA and within 1 year from the post-TJA index colonoscopy date. Results: Analyses included 243 671 patients (mean [SD] age, 70.4 [10.0] years; 144 083 [59.1%] female) who underwent TJA in the MHS from 2010 to 2016. In the preoperative colonoscopy cohort, 325 patients (2.8%) had PJI within 1 year postoperatively. In the postoperative colonoscopy cohort, 138 patients (1.8%) had PJI within 1 year from the index colonoscopy date. In separate analyses of colonoscopy status within 6 months before and 6 months after TJA, younger age, male sex, and several chronic health conditions (diabetes, kidney disease, and pulmonary disease) were each associated with higher PJI risk. However, no association was found with PJI risk for perioperative colonoscopy preoperatively (adjusted odds ratio, 1.10; 95% CI, 0.98-1.23) or postoperatively (adjusted odds ratio, 0.90; 95% CI, 0.74-1.08). Conclusions and Relevance: In this large retrospective cohort of patients undergoing TJA, perioperative screening colonoscopy was not associated with PJI and should not be delayed for periprocedural risk. However, health conditions were independently associated with PJI and should be medically optimized.

Technical Trick: Cryoneurolysis for Subacute Pain Mitigation in Patients With Limb Loss.

SUMMARY: Pain after amputation is often managed by target muscle reinnervation (TMR) with the added benefit that TMR also provides improved myoelectric terminal device control. However, as TMR takes several months for the recipient muscles to reliably reinnervate, this technique does not address pain within the subacute postoperative period during which pain chronification, sensitization, and opioid dependence and misuse may occur. Cryoneurolysis, described herein, uses focused, extreme temperatures to essentially "freeze" the nerve, blocking nociception, and improving pain in treated nerves potentially reducing the chances of pain chronification, sensitization, and substance dependence or abuse.

Multiple Myeloma in a Young Adult: A Case Report.

CASE: We present a 27-year-old man who presented with worsened back pain after a fall from a ladder. Unrelenting pain prompted the discovery through imaging and biopsy of a large pelvic plasmacytoma along with innumerable axial and appendicular lytic osseous lesions. The patient was soon thereafter diagnosed with light chain multiple myeloma (MM) and underwent measurable residual disease response-adapted consolidation treatment. CONCLUSION: The typical age at diagnosis for MM is 60 to 70 years. This case emphasizes the need to consider MM when diagnosing patients younger than 30 years, especially those with numerous bony lesions.

Bone Homeostasis and Physiology in Normal and Orthopaedic Disease Conditions.

Some of the most common human systemic diseases-both benign and malignant-affect bone regulation, formation, and homeostasis (the cellular balance regulated by osteocytes, osteoblasts, and osteoclasts). This review discusses our current understanding of the molecular components and mechanisms that are responsible for homeostasis and interactions resulting in dysregulation (dysfunction due to the loss of the dynamic equilibrium of bone homeostasis). Knowledge of key pathways in bone biology can improve surgeon understanding, clinical recognition, and treatment of bone homeostasis-related diseases.

Rates and Predictors of Surgery for Lumbar Disc Herniation Between the Military and Civilian Health Care Systems.

STUDY DESIGN: Retrospective review (level of evidence III). OBJECTIVE: Surgical care patterns for lumbar disc herniation (LDH), a common musculoskeletal condition of high relevance to the Military Health System (MHS), have not been described or compared across the direct care and purchased care MHS components. This study aimed to describe surgery rates in MHS beneficiaries who were diagnosed with LDH in direct care versus purchased care and to evaluate characteristics associated with the location of surgery. Differences in care patterns for LDH may suggest unexpected variation within the centrally managed MHS. METHODS: We described 1-year rates of surgery among beneficiaries who were diagnosed with LDH in direct care versus purchased care. Among beneficiaries who were diagnosed in direct care and had surgery, multivariable logistic regression models were used to identify characteristics associated with surgery location. RESULTS: We identified 726,638 MHS beneficiaries who were diagnosed with LDH in direct care or purchased care during the 9-year study period. One-year surgery rates were 10.1% in beneficiaries who were diagnosed in direct care versus 11.3% in beneficiaries who were diagnosed in purchased care. Among the 7467 patients who were diagnosed in direct care and had surgery within 1 year, characteristics associated with lower probability of surgery in purchased care versus direct care included diagnosing facility type (hospital with a neurosurgery or spine specialty versus clinic (odds ratio [OR], 0.12 (95% CI, 0.10-0.15)), Navy versus Army (OR, 0.24 (95% CI, 0.21-0.28)), and diagnosing facility specialty (Medical Expense and Performance Reporting System) (surgical care (OR, 0.33 (95% CI, 0.27-0.40)) and orthopedic care (OR, 0.39 (95% CI, 0.33-0.46)) versus primary care. The presence of comorbidities was associated with higher probability of surgery in purchased care versus direct care (OR, 1.20 (95% CI, 1.06-1.36)). CONCLUSIONS: The 1-year rate of surgery for LDH was modestly higher in beneficiaries who were diagnosed in purchased care versus direct care. Among patients who were diagnosed in direct care, several patient-level and facility-level characteristics were associated with receiving surgery in purchased care, suggesting potentially unexpected variation in care utilization across components of the MHS.

Racial and Gender Diversity of Physicians Accepted to American Military Orthopaedic and Surgical Residencies: An 18-Year Analysis.

Orthopaedic surgery ranks among the least racially and gender diverse medical/surgical specialties. United States military surgeons train in military or military-funded residency positions to care for a markedly diverse population; however, the composition and diversity of these training programs have not been previously assessed. The purpose of this study was to analyze the trends of physician diversity in military orthopaedics in comparison with other surgical specialties over time. Methods: We performed a retrospective cohort study evaluating matriculation into first year of residency training in US military surgical training programs between 2002 and 2020. In total, 9,124 applicants were reviewed. We collected matriculant self-reported race/ethnicity and sex and the medical/specialty program. We considered under-represented minorities as those who reported their race as African American, Indian/Alaskan Native, and Native, other, or who reported ethnicity as Hispanic. We calculated changes in persons accepted to training positions over time and used linear regression to model trends in diversity among orthopaedic matriculating residents when compared with other surgical subspecialities over time. Results: Across all surgical subspecialities, the average change in percent women was 0.94% per year for the study period (p < 0.01). The average annual percent women entering orthopaedic surgery residency programs was 14% for the 18-year study period. Across all surgical subspecialties, the average change for accepted applicants from groups underrepresented in medicine (URiM) was 1.01% per year for the study period (p < 0.01). The average annual percent URiM entering orthopaedic surgery residency programs was 17% for the 18-year study period. The annual change of women and URiM entering military orthopaedic residencies was 0.10% and 1.52%, respectively. Conclusions: Despite statistically significant improvements, recruitment efforts as used to date fall far short of reversing sexual, racial, and ethnic disparities in military orthopaedic residencies. Orthopaedics has a lower representation of both women and physicians with minority backgrounds when compared with many surgical subspecialties. Additional interventions are still necessary to increase diversity for military orthopaedic surgeons.

Beach-Chair Versus Lateral Decubitus Positioning for Primary Arthroscopic Anterior Shoulder Stabilization: A Consecutive Series of 641 Shoulders.

BACKGROUND: There are limited data comparing the beach-chair (BC) versus lateral decubitus (LD) position for arthroscopic anterior shoulder stabilization. PURPOSE: To identify predictors of instability recurrence and revision after anterior shoulder stabilization and evaluate surgical position and glenoid bone loss as independent predictors of recurrence and revision at short- and midterm follow-ups. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: A consecutive series of 641 arthroscopic anterior stabilization procedures were performed from 2005 to 2019. All shoulders were evaluated for glenohumeral bone loss on magnetic resonance imaging. The primary outcomes of interest were recurrence and revision. Multivariable logistic regression models were used to assess the relationships of outcomes with age, position, glenoid bone loss group, and track. RESULTS: A total of 641 shoulders with a mean age of 22.3 years (SD, 4.45 years) underwent stabilization and were followed for a mean of 6 years. The overall 1-year recurrent instability rate was 3.3% (21/641) and the revision rate was 2.8% (18/641). At 1 year, recurrence was observed in 2.3% (11/487) and 6.5% (10/154) of BC and LD shoulders, respectively. The 5-year recurrence and revision rates were 15.7% (60/383) and 12.8% (49/383), respectively. At 5 years, recurrence was observed in 16.4% (48/293) and 13.3% (12/90) of BC and LD shoulders, respectively. Multivariable modeling demonstrated that surgical position was not associated with a risk of recurrence after 1 year (odds ratio [OR] for LD vs BC, 1.39; P = .56) and 5 years (OR for LD vs BC, 1.32; P = .43), although younger age at index surgery was associated with a higher risk of instability recurrence (OR, 1.73 per SD [4.1 years] decrease in age; P < .03). After 1 and 5 years, surgical position results were similar in a separate multivariable logistic regression model of revision surgery as the dependent variable, when adjusted for age, surgical position, bone loss group, and track. At 5 years, younger age was an independent risk factor for revision: OR 1.68 per SD (4.1 years) decrease in age (P < .05). CONCLUSION: Among fellowship-trained orthopaedic surgeons, there was no difference in rates of recurrence and revision surgery after performing arthroscopic anterior stabilization in either the BC or the LD position at 1- and 5-year follow-ups. In multivariable analysis, younger age, but not surgical position, was an independent risk factor for recurrence.