Venous malformation may be a feature of EXT1-related hereditary multiple exostoses: A report of two unrelated probands.

Hereditary multiple exostoses (HME), also known as hereditary multiple osteochondroma (HMO), is an autosomal dominant disorder caused by pathogenic variants in exostosin-1 or -2 (EXT1 or EXT2). It is characterized by the formation of multiple benign growing osteochondromas (exostoses) that most commonly affect the long bones; however, it may also occur throughout the body. Although many of these lesions are clinically asymptomatic, some can lead to chronic pain and skeletal deformities and interfere with adjacent neurovascular structures. Here, we report two unrelated probands that presented with a clinical and molecular diagnosis of HME with venous malformation, a clinical feature not previously reported in individuals with HME.

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.

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.

What Factors Are Associated With Local Metastatic Lesion Progression After Intramedullary Nail Stabilization?

BACKGROUND: Pathologic fracture of the long bones is a common complication of bone metastases. Intramedullary nail stabilization can be used prophylactically (for impending fractures) or therapeutically (for completed fractures) to preserve mobility and quality of life. However, local disease progression may occur after such treatment, and there is concern that surgical instrumentation and the intramedullary nail itself may seed tumor cells along the intramedullary tract, ultimately leading to loss of structural integrity of the construct. Identifying factors associated with local disease progression after intramedullary nail stabilization would help surgeons predict which patients may benefit from alternative surgical strategies. QUESTIONS/PURPOSES: (1) Among patients who underwent intramedullary nail stabilization for impending or completed pathologic fractures of the long bones, what is the risk of local progression, including progression of the existing lesion and development of a new lesion around the nail? (2) Among patients who experience local progression, what proportion undergo reoperation? (3) What patient characteristics and treatment factors are associated with postoperative local progression? (4) What is the difference in survival rates between patients who experienced local progression and those with stable local disease? METHODS: Between January 2013 and December 2019, 177 patients at our institution were treated with an intramedullary nail for an impending or completed pathologic fracture. We excluded patients who did not have a pathologic diagnosis of metastasis before fixation, who were younger than 18 years of age, who presented with a primary soft tissue mass that eroded into bone, and who experienced nonunion from radiation osteitis or an avulsion fracture rather than from metastasis. Overall, 122 patients met the criteria for our study. Three fellowship-trained orthopaedic oncology surgeons involved in the care of these patients treated an impending or pathologic fracture with an intramedullary nail when a long bone lesion either fractured or was deemed to be of at least 35% risk of fracture within 3 months, and in patients with an anticipated duration of overall survival of at least 6 weeks (fractured) or 3 months (impending) to yield palliative benefit during their lifetime. The most common primary malignancy was multiple myeloma (25% [31 of 122]), followed by lung carcinoma (16% [20 of 122]), breast carcinoma (15% [18 of 122]), and renal cell carcinoma (12% [15 of 122]). The most commonly involved bone was the femur (68% [83 of 122]), followed by the humerus (27% [33 of 122]) and the tibia (5% [6 of 122]). A competing risk analysis was used to determine the risk of progression in our patients at 1 month, 3 months, 6 months, and 12 months after surgery. A proportion of patients who ultimately underwent reoperation due to progression was calculated. A univariate analysis was performed to determine whether lesion progression was associated with various factors, including the age and sex of the patient, use of adjuvant therapies (radiation therapy at the site of the lesion, systemic therapy, and antiresorptive therapy), histologic tumor type, location of the lesion, and fracture type (impending or complete). Patient survival was assessed with a Kaplan-Meier curve. A p value < 0.05 was considered significant. RESULTS: The cumulative incidence of local tumor progression (with death as a competing risk) at 1 month, 3 months, 6 months, and 12 months after surgery was 1.9% (95% confidence interval 0.3% to 6.1%), 2.9% (95% CI 0.8% to 7.5%), 3.9% (95% CI 1.3% to 8.9%), and 4.9% (95% CI 1.8% to 10.3%), respectively. Of 122 patients, 6% (7) had disease progression around the intramedullary nail and 0.8% (1) had new lesions at the end of the intramedullary nail. Two percent (3 of 122) of patients ultimately underwent reoperation because of local progression. The only factors associated with progression were a primary tumor of renal cell carcinoma (odds ratio 5.1 [95% CI 0.69 to 29]; p = 0.03) and patient age (difference in mean age 7.7 years [95% CI 1.2 to 14]; p = 0.02). We found no associations between local disease progression and the presence of visceral metastases, other skeletal metastases, radiation therapy, systemic therapy, use of bisphosphonate or receptor activator of nuclear factor kappa-B ligand inhibitor, type of fracture, or the direction of nail insertion. There was no difference in survivorship curves between those with disease progression and those with stable local disease (= 0.36; p = 0.54). CONCLUSION: Our analysis suggests that for this population of patients with metastatic bone disease who have a fracture or impeding fracture and an anticipated survival of at least 6 weeks (completed fracture) or 3 months (impending fracture), the risk of experiencing local progression of tumor growth and reoperations after intramedullary nail stabilization seems to be low. Lesion progression was not associated with the duration of survival, although this conclusion is limited by the small number of patients in the current study and the competing risks of survival and local progression. Based on our data, patients who present with renal cell carcinoma should be cautioned against undergoing intramedullary nailing because of the risk of postoperative lesion progression. LEVEL OF EVIDENCE: Level III, therapeutic study.

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.

Longitudinal Analysis of Circulating Markers of Bone Turnover Across Multiple Decades in Osteoporotic Women.

PURPOSE: The objective of this study was to analyze changes in serum markers of bone turnover across multiple decades in osteoporotic women compared with nonosteoporotic controls, to determine their utility as potential predictors for osteoporosis. Early prediction of those at risk for osteoporosis can enable early intervention before the irreversible loss of critical bone mass. METHODS: Serum samples were obtained from 20 women given the diagnosis of osteoporosis after age 46 years and 20 age-matched women with normal bone mineral density from 4 time points in their life (ages 25-31, 32-38, 39-45, and 46-60 years). Serum levels of bone turnover markers (propeptide of type I collagen, parathyroid hormone, bone-specific alkaline phosphatase, osteocalcin, C-terminal telopeptide of type I collagen, sclerostin, osteoprotegerin, osteopontin, and 25-OH vitamin D) were measured using commercially available arrays and kits. We used logistic regression to assess these individual serum markers as potential predictors of osteoporosis, and mixed-effects modeling to assess the change in bone turnover markers between osteoporotic and control groups over time, then performed fivefold cross-validation to assess the classification ability of the models. RESULTS: Markers of bone turnover, bone-specific alkaline phosphatase, C-terminal telopeptide of type I collagen, sclerostin, and osteocalcin were all independent predictors at multiple time points; osteopontin was an independent predictor in the 39- to 45-year age group. Receiver operating characteristic analyses demonstrated moderately strong classification ability at all time points. Sclerostin levels among groups diverged over time and were higher in the control group than the osteoporotic group, with significant differences observed at time points 3 and 4. CONCLUSIONS: Serum markers of bone turnover may be used to estimate the likelihood of osteoporosis development in individuals over time. Although prospective validation is necessary before recommending widespread clinical use, this information may be used to identify patients at risk for developing low bone mineral density long before traditional screening would ostensibly take place. TYPE OF STUDY/LEVEL OF EVIDENCE: Diagnostic II.

Can a Bayesian belief network for survival prediction in patients with extremity metastases (PATHFx) be externally validated in an Asian cohort of 356 surgically treated patients?

BACKGROUND AND PURPOSE: Predicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis. PATIENTS AND METHODS: We included 356 patients who underwent surgery for skeletal extremity metastasis in a tertiary center in Taiwan between 2014 and 2019 to validate PATHFx's survival predictions at 6 different time points. Model performance was assessed by concordance index (c-index), calibration analysis, decision curve analysis (DCA), Brier score, and model consistency (MC). RESULTS: The c-indexes for the 1-, 3-, 6-, 12-, 18-, and 24-month survival estimations were 0.71, 0.66, 0.65, 0.69, 0.68, and 0.67, respectively. The calibration analysis demonstrated positive calibration intercepts for survival predictions at all 6 timepoints, indicating PATHFx tended to underestimate the actual survival. The Brier scores for the 6 models were all less than their respective null model's. DCA demonstrated that only the 6-, 12-, 18-, and 24-month predictions appeared useful for clinical decision-making across a wide range of threshold probabilities. The MC was < 0.9 when the 6- and 12-month models were compared with the 12-month and 18-month models, respectively. INTERPRETATION: In this Asian cohort, PATHFx's performance was not as encouraging as those of prior validation studies. Clinicians should be cognizant of the potential decline in validity of any tools designed using data outside their particular patient population. Developers of survival prediction tools such as PATHFx might refine their algorithms using data from diverse, contemporary patients that is more reflective of the world's population.

Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?

BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As the demand for TKA rises, reducing the risk of revision TKA is becoming increasingly important. Predictive tools based on machine-learning algorithms could reform clinical practice. Few attempts have been made to combine machine-learning algorithms with data from nationwide arthroplasty registries and, to the authors' knowledge, none have tried to predict the likelihood of early revision TKA. QUESTION/PURPOSES: We used the Danish Knee Arthroplasty Registry to build models to predict the likelihood of revision TKA within 2 years of primary TKA and asked: (1) Which preoperative factors were the most important features behind these models' predictions of revision? (2) Can a clinically meaningful model be built on the preoperative factors included in the Danish Knee Arthroplasty Registry? METHODS: The Danish Knee Arthroplasty Registry collects patients' characteristics and surgical information from all arthroplasties conducted in Denmark and thus provides a large nationwide cohort of patients undergoing TKA. As training dataset, we retrieved all preoperative variables of 25,104 primary TKAs from 2012 to 2015. The same variables were retrieved from 6170 TKAs conducted in 2016, which were used as a hold-out year for temporal external validation. If a patient received bilateral TKA, only the first knee to receive surgery was included. All patients were followed for 2 years, with removal, exchange, or addition of an implant defined as TKA revision. We created four different predictive models to find the best performing model, including a regression-based model using logistic regression with least shrinkage and selection operator (LASSO), two classification tree models (random forest and gradient boosting model) and a supervised neural network. For comparison, we created a noninformative model predicting that all observations were unrevised. The four machine learning models were trained using 10-fold cross-validation on the training dataset after adjusting for the low percentage of revisions by over-sampling revised observations and undersampling unrevised observations. In the validation dataset, the models' performance was evaluated and compared by density plot, calibration plot, accuracy, Brier score, receiver operator characteristic (ROC) curve and area under the curve (AUC). The density plot depicts the distribution of probabilities and the calibration plot graphically depicts whether the predicted probability resembled the observed probability. The accuracy indicates how often the models' predictions were correct and the Brier score is the mean distance from the predicted probability to the observed outcome. The ROC curve is a graphical output of the models' sensitivity and specificity from which the AUC is calculated. The AUC can be interpreted as the likelihood that a model correctly classified an observation and thus, a priori, an AUC of 0.7 was chosen as threshold for a clinically meaningful model. RESULTS: Based the model training, age, postfracture osteoarthritis and weight were deemed as important preoperative factors within the machine learning models. During validation, the models' performance was not different from the noninformative models, and with AUCs ranging from 0.57 to 0.60, no models reached the predetermined AUC threshold for a clinical useful discriminative capacity. CONCLUSION: Although several well-known presurgical risk factors for revision were coupled with four different machine learning methods, we could not develop a clinically useful model capable of predicting early TKA revisions in the Danish Knee Arthroplasty Registry based on preoperative data. CLINICAL RELEVANCE: The inability to predict early TKA revision highlights that predicting revision based on preoperative information alone is difficult. Future models might benefit from including medical comorbidities and an anonymous surgeon identifier variable or may attempt to build a postoperative predictive model including intra- and postoperative factors as these may have a stronger association with early TKA revisions.

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.

Healing of Pathologic Humeral Fractures in Patients with Metastatic Disease: Consideration for Operative Fixation in Patients.

We compared short-term outcomes after operative versus nonoperative treatment of pathologic humeral fractures. We hypothesized that patients who underwent operative fixation would heal faster and have better pain control. A retrospective review was conducted of 25 patients who underwent operative fixation and 6 who received nonoperative treatment from 2005-2017. Operative patients healed significantly earlier than nonoperative patients (p = 0.02). At 16-week follow-up, radiographs showed evidence of healing in 24 of 25 operatively treated patients and 2 of 6 nonoperatively treated patients (p < 0.01). Pain improved during the inpatient stay in 24 of 25 operatively treated patients and none of the nonoperatively treated patients (p < 0.01). All operatively treated patients returned to self-reported baseline motor function by final follow-up, whereas none of the nonoperatively treated patients returned to baseline (p = 0.01). Operative treatment was associated with earlier healing, pain control and return to function compared with nonoperative treatment of pathologic humeral fractures. Level of Evidence: 3. (Journal of Surgical Orthopaedic Advances 29(3):177-181, 2020).

Intrawound Antibiotic Powder Decreases Frequency of Deep Infection and Severity of Heterotopic Ossification in Combat Lower Extremity Amputations.

BACKGROUND: Amputations sustained owing to combat-related blast injuries are at high risk for deep infection and development of heterotopic ossification, which can necessitate reoperation and place immense strain on the patient. Surgeons at our institution began use of intrawound antibiotic powder at the time of closure in an effort to decrease the rate of these surgical complications after initial and revision amputations, supported by compelling clinical evidence and animal models of blast injuries. Antibiotic powder may be useful in reducing the risk of these infections, but human studies on this topic thus far have been inconclusive. PURPOSE: We sought to determine whether administration of intrawound antibiotic powder at the time of closure would (1) decrease the risk of subsequent deep infections of major lower-extremity combat-related amputations, and (2) limit formation and decrease severity of heterotopic ossification common in the combat-related traumatic residual limb. METHODS: Between 2009 and 2015, 252 major lower extremity initial and revision amputations were performed by a single surgeon. Revision cases were excluded if performed specifically to address deep infection, leaving 223 amputations (88.5%) for this retrospective analysis. We reviewed medical records to collect patient information, returns to the operating room for subsequent infection, and microbiologic culture results. We also reviewed radiographs taken at least 3 months after surgery to determine the presence and severity of heterotopic ossification using the Walter Reed classification system. We grouped cases according to whether limbs underwent initial or revision amputations, and whether the limbs had a history of a prior infection. Apart from the use of antibiotic powder and duration of followup, the groups did not differ in terms of age, mechanism of injury, or sex. We then calculated the absolute risk reduction for infection and heterotopic ossification and the number needed to treat to prevent an infection. RESULTS: Overall, administration of antibiotic powder resulted in a 13% absolute risk reduction of deep infection (14 of 82 [17%] versus 42 of 141 [30%]; p = 0.03; 95% CI, 0.20%-24.72%). In revision amputation surgery, the absolute risk reduction of infection with antibiotic powder use was 16% overall (eight of 58 versus 17 of 57; 95% CI, 1.21%-30.86%), and 25% for previously infected limbs (eight of 46 versus 14 of 33; 95% CI, 4.93%-45.14%). The number needed to treat to prevent one additional deep infection in amputation surgery is eight in initial amputations, seven in revision amputations, and four for revision amputation surgery on previously infected limbs. With the numbers available, we observed no reduction in the risk of heterotopic ossification with antibiotic powder use, but severity was decreased in the treatment group in terms of the number of residual limbs with moderate or severe heterotopic ossification (three of 12 versus 19 of 34; p = 0.03). CONCLUSIONS: Our findings show that administration of intrawound antibiotic powder reduces deep infection in residual limbs of combat amputees, particularly in the setting of revision amputation surgery in apparently aseptic residual limbs at the time of the surgery. Furthermore, administration of antibiotic powder for amputations at time of initial closure decreases the severity of heterotopic ossification formation, providing a low-cost adjunct to decrease the risk of two complications common to amputation surgery.Level of Evidence Level III, therapeutic study.

Proximal Femur Hounsfield Units on CT Colonoscopy Correlate With Dual-energy X-ray Absorptiometry.

BACKGROUND: Quantifying bone mineral density (BMD) on CT using commercial software demonstrates good-to-excellent correlations with dual-energy x-ray absorptiometry (DEXA) results. However, previous techniques to measure Hounsfield units (HUs) within the proximal femur demonstrate less successful correlation with DEXA results. An effective method of measuring HUs of the proximal femur from CT colonoscopy might allow for opportunistic osteoporosis screening. QUESTIONS/PURPOSES: (1) Do proximal femur HU measurements from CT colonoscopy correlate with proximal femur DEXA results? (2) How effective is our single HU measurement technique in estimating the likelihood of overall low BMD? (3) Does the relationship between our comprehensive HU measurement and DEXA results change based on age, sex, or time between studies? METHODS: This retrospective study investigated the measurement of HU of the femur obtained on CT colonoscopy studies compared with DEXA results. Between 2010 and 2017, five centers performed 9085 CT colonoscopy studies; of those, 277 (3%) also had available DEXA results and were included in this study, whereas 8809 (97%) were excluded for inadequate CT imaging, lack of DEXA screening, or lack of proximal femur DEXA results. The median number of days between CT colonoscopy and DEXA scan was 595 days; no patient was excluded based on time between scans because bone remodeling is a long-term process and this allowed subgroup analysis based on time between scans. Two reviewers performed HU measurements at four points within the proximal femur on the CT colonoscopy imaging and intraclass correlation coefficients were used to evaluate interrater reliability. We used Pearson correlation coefficients to compare the comprehensive (average of eight measurements) and a single HU measurement with each DEXA result-proximal femur BMD, proximal femur T-score, femoral neck BMD, and femoral neck T-score-to identify the best measurement technique within this study. Based on their lowest DEXA T-score, we stratified patients to a diagnosis of osteoporosis, osteopenia, or normal BMD. We then calculated the area under the receiver operator characteristic curves (AUCs) to evaluate the classification ability of a single HU value to identify possible threshold(s) for detecting low BMD. For each subgroup analysis, we calculated Pearson correlation coefficients between DEXA and HUs and evaluated each subgroup's contribution to the overall predictive model using an interaction test in a linear regression model. RESULTS: The Pearson correlation coefficient between both the comprehensive and single HU measurements was highest compared with the proximal femur T-score at 0.75 (95% confidence interval [CI], 0.69-0.80) and 0.74 (95% CI, 0.68-0.79), respectively. Interobserver reliability, measured with intraclass correlation coefficients, for the comprehensive and single HU measurements was 0.97 (95% CI, 0.72-0.99) and 0.96 (95% CI, 0.89-0.98), respectively. Based on DEXA results, 20 patients were osteoporotic, 167 had osteopenia, and 90 patients had normal BMD. The mean comprehensive HU for patients with osteoporosis was 70 ± 30 HUs; for patients with osteopenia, it was 110 ± 36 HUs; and for patients with normal BMD, it was 158 ± 43 HUs (p < 0.001). The AUC of the single HU model was 0.82 (95% CI, 0.77-0.87). A threshold of 214 HUs is 100% sensitive and 59 HUs is 100% specific to identify low BMD; a threshold of 113 HUs provided 73% sensitivity and 76% specificity. When stratified by decade age groups, each decade age group demonstrated a positive correlation between the comprehensive HU and proximal femur T-score, ranging between 0.71 and 0.83 (95% CI, 0.59-0.91). Further subgroup analysis similarly demonstrated a positive correlation between the comprehensive HU and proximal femur T-score when stratified by > 6 months or < 6 months between CT and DEXA (0.75; 95% CI, 0.62-0.84) as well as when stratified by sex (0.70-0.76; 95% CI, 0.48-0.81). The linear regression model demonstrated that the overall positive correlation coefficient between HUs and the proximal femur T-score is not influenced by any subgroup. CONCLUSIONS: Our measurement technique provides a reproducible measurement of HUs within the proximal femur HUs on CT colonoscopy. Hounsfield units of the proximal femur based on this technique can predict low BMD. These CT scans are frequently performed before initial DEXA scans are done and therefore may lead to earlier recognition of low BMD. Future research is needed to validate these results in larger studies and to determine if these results can anticipate future fracture risk. LEVEL OF EVIDENCE: Level III, diagnostic study.

Trauma induced heterotopic ossification patient serum alters mitogen activated protein kinase signaling in adipose stem cells.

Post-traumatic heterotopic ossification (HO) is the formation of ectopic bone in non-osseous structures following injury. The precise mechanism for bone development following trauma is unknown; however, early onset of HO may involve the production of pro-osteogenic serum factors. Here we evaluated serum from a cohort of civilian and military patients post trauma to determine early induction gene signatures in orthopaedic trauma induced HO. To test this, human adipose derived stromal/stem cells (hASCs) were stimulated with human serum from patients who developed HO following trauma and evaluated for a gene panel with qPCR. Pathway gene analysis ontology revealed that hASCs stimulated with serum from patients who developed HO had altered gene expression in the activator protein 1 (AP1) and AP1 transcriptional targets pathways. Notably, there was a significant repression in FOS gene expression in hASCs treated with serum from individuals with HO. Furthermore, the mitogen-activated protein kinase (MAPK) signaling pathway was activated in hASCs following serum exposure from individuals with HO. Serum from both military and civilian patients with trauma induced HO had elevated downstream genes associated with the MAPK pathways. Stimulation of hASCs with known regulators of osteogenesis (BMP2, IL6, Forskolin, and WNT3A) failed to recapitulate the gene signature observed in hASCs following serum stimulation, suggesting non-canonical mechanisms for gene regulation in trauma induced HO. These findings provide new insight for the development of HO and support ongoing work linking the systemic response to injury with wound specific outcomes.

Characterization of Brown Adipose-Like Tissue in Trauma-Induced Heterotopic Ossification in Humans.

Heterotopic ossification (HO), the abnormal formation of bone within soft tissues, is a major complication after severe trauma or amputation. Transient brown adipocytes have been shown to be a critical regulator of this process in a mouse model of HO. In this study, we evaluated the presence of brown fat within human HO lesions. Most of the excised tissue samples displayed histological characteristics of bone, fibroproliferative cells, blood vessels, and adipose tissue. Immunohistochemical analysis revealed extensive expression of uncoupling protein 1 (UCP1), a definitive marker of brown adipocytes, within HO-containing tissues but not normal tissues. As seen in the brown adipocytes observed during HO in the mouse, these UCP1+ cells also expressed the peroxisome proliferator-activated receptor γ coactivator 1α. However, further characterization showed these cells, like their mouse counterparts, did not express PR domain containing protein 16, a key factor present in brown adipocytes found in depots. Nor did they express factors present in beige adipocytes. These results identify a population of UCP1+ cells within human tissue undergoing HO that do not entirely resemble either classic brown or beige adipocytes, but rather a specialized form of brown adipocyte-like cells, which have a unique function. These cells may offer a new target to prevent this unwanted bone.

Bone and Soft-Tissue Tumors for the General Orthopaedic Surgeon: Diagnosis, Management, and Avoiding Errors.

Orthopaedic surgeons will encounter a variety of bone and soft-tissue lesions throughout their careers. Although most of these lesions will be benign and nonaggressive, many others will require management that is time consuming, challenging, and stressful for surgeons and patients. Errors in management, if they occur, may delay diagnosis and hinder efforts to perform limb-sparing surgery. A systematic approach to the evaluation of adult and pediatric patients with bone or soft-tissue tumors will help general orthopaedic surgeons correctly diagnose and manage tumors, understand when to refer patients to an orthopaedic oncologist, and avoid management errors.

Can We Estimate Short- and Intermediate-term Survival in Patients Undergoing Surgery for Metastatic Bone Disease?

BACKGROUND: Objective means of estimating survival can be used to guide surgical decision-making and to risk-stratify patients for clinical trials. Although a free, online tool ( www.pathfx.org ) can estimate 3- and 12-month survival, recent work, including a survey of the Musculoskeletal Tumor Society, indicated that estimates at 1 and 6 months after surgery also would be helpful. Longer estimates help justify the need for more durable and expensive reconstructive options, and very short estimates could help identify those who will not survive 1 month and should not undergo surgery. Thereby, an important use of this tool would be to help avoid unsuccessful and expensive surgery during the last month of life. QUESTIONS/PURPOSES: We seek to provide a reliable, objective means of estimating survival in patients with metastatic bone disease. After generating models to derive 1- and 6-month survival estimates, we determined suitability for clinical use by applying receiver operator characteristic (ROC) (area under the curve [AUC] > 0.7) and decision curve analysis (DCA), which determines whether using PATHFx can improve outcomes, but also discerns in which kinds of patients PATHFx should not be used. METHODS: We used two, existing, skeletal metastasis registries chosen for their quality and availability. Data from Memorial Sloan-Kettering Cancer Center (training set, n = 189) was used to develop two Bayesian Belief Networks trained to estimate the likelihood of survival at 1 and 6 months after surgery. Next, data from eight major referral centers across Scandinavia (n = 815) served as the external validation set-that is, as a means to test model performance in a different patient population. The diversity of the data between the training set from Memorial Sloan-Kettering Cancer Center and the Scandinavian external validation set is important to help ensure the models are applicable to patients in various settings with differing demographics and treatment philosophies. We considered disease-specific, laboratory, and demographic information, and the surgeon's estimate of survival. For each model, we calculated the area under the ROC curve (AUC) as a metric of discriminatory ability and the Net Benefit using DCA to determine whether the models were suitable for clinical use. RESULTS: On external validation, the AUC for the 1- and 6-month models were 0.76 (95% CI, 0.72-0.80) and 0.76 (95% CI, 0.73-0.79), respectively. The models conferred a positive net benefit on DCA, indicating each could be used rather than assume all patients or no patients would survive greater than 1 or 6 months, respectively. CONCLUSIONS: Decision analysis confirms that the 1- and 6-month Bayesian models are suitable for clinical use. CLINICAL RELEVANCE: These data support upgrading www.pathfx.org with the algorithms described above, which is designed to guide surgical decision-making, and function as a risk stratification method in support of clinical trials. This updating has been done, so now surgeons may use any web browser to generate survival estimates at 1, 3, 6, and 12 months after surgery, at no cost. Just as short estimates of survival help justify palliative therapy or less-invasive approaches to stabilization, more favorable survival estimates at 6 or 12 months are used to justify more durable, complicated, and expensive reconstructive options.

Can a Bayesian Belief Network Be Used to Estimate 1-year Survival in Patients With Bone Sarcomas?

BACKGROUND: Extremity sarcoma has a preponderance to present late with advanced stage at diagnosis. It is important to know why these patients die early from sarcoma and to predict those at high risk. Currently we have mid- to long-term outcome data on which to counsel patients and support treatment decisions, but in contrast to other cancer groups, very little on short-term mortality. Bayesian belief network modeling has been used to develop decision-support tools in various oncologic diagnoses, but to our knowledge, this approach has not been applied to patients with extremity sarcoma. QUESTIONS/PURPOSES: We sought to (1) determine whether a Bayesian belief network could be used to estimate the likelihood of 1-year mortality using receiver operator characteristic analysis; (2) describe the hierarchal relationships between prognostic and outcome variables; and (3) determine whether the model was suitable for clinical use using decision curve analysis. METHODS: We considered all patients treated for primary bone sarcoma between 1970 and 2012, and excluded secondary metastasis, presentation with local recurrence, and benign tumors. The institution's database yielded 3499 patients, of which six (0.2%) were excluded. Data extracted for analysis focused on patient demographics (age, sex), tumor characteristics at diagnosis (size, metastasis, pathologic fracture), survival, and cause of death. A Bayesian belief network generated conditional probabilities of variables and survival outcome at 1 year. A lift analysis determined the hierarchal relationship of variables. Internal validation of 699 test patients (20% dataset) determined model accuracy. Decision curve analysis was performed comparing net benefit (capped at 85.5%) for all threshold probabilities (survival output from model). RESULTS: We successfully generated a Bayesian belief network with five first-degree associates and describe their conditional relationship with survival after the diagnosis of primary bone sarcoma. On internal validation, the resultant model showed good predictive accuracy (area under the curve [AUC] = 0.767; 95% CI, 0.72-0.83). The factors that predict the outcome of interest, 1-year mortality, in order of relative importance are synchronous metastasis (6.4), patient's age (3), tumor size (2.1), histologic grade (1.8), and presentation with a pathologic fracture (1). Patient's sex, tumor location, and inadvertent excision were second-degree associates and not directly related to the outcome of interest. Decision curve analysis shows that clinicians can accurately base treatment decisions on the 1-year model rather than assuming all patients, or no patients, will survive greater than 1 year. For threshold probabilities less than approximately 0.5, the model is no better or no worse than assuming all patients will survive. CONCLUSIONS: We showed that a Bayesian belief network can be used to predict 1-year mortality in patients presenting with a primary malignancy of bone and quantified the primary factors responsible for an increased risk of death. Synchronous metastasis, patient's age, and the size of the tumor had the largest prognostic effect. We believe models such as these can be useful as clinical decision-support tools and, when properly externally validated, provide clinicians and patients with information germane to the treatment of bone sarcomas. CLINICAL RELEVANCE: Bone sarcomas are difficult to treat requiring multidisciplinary input to strategize management. An evidence-based survival prediction can be a powerful adjunctive to clinicians in this scenario. We believe the short-term predictions can be used to evaluate services, with 1-year mortality already being a quality indicator. Mortality predictors also can be incorporated in clinical trials, for example, to identify patients who are least likely to experience the side effects of experimental toxic chemotherapeutic agents.

Can A Multivariate Model for Survival Estimation in Skeletal Metastases (PATHFx) Be Externally Validated Using Japanese Patients?

BACKGROUND: Objective survival estimates are important when treating or studying outcomes in patients with skeletal metastases. One decision-support tool, PATHFx (www.pathfx.org) is designed to predict each patient's postsurgical survival trajectory at 1, 3, 6, and 12 months in patients undergoing stabilization for skeletal metastases. PATHFx has been externally validated in various western centers, but it is unknown whether it may be useful in Asian patient populations. QUESTIONS/PURPOSES: We asked (1) whether the PATHFx models are as predictive in Japanese patients by estimating the area under the receiver operator characteristic curve (AUC); we considered an AUC greater than 0.7 as an adequate predictive value. We also (2) performed decision curve analysis at various times to determine whether and how PATHFx should be used clinically at those times. PATIENTS AND METHODS: A Bayesian model is a statistical method to explore conditional, probabilistic relationships between variables to estimate the likelihood of an outcome using observed data. We applied the PATHFx Bayesian models to an independent dataset containing the records of patients who underwent skeletal stabilization for metastatic bone disease at one of five Japanese referral centers and had a followup longer than 12 months for survivors. Of 270 patients in the database, we excluded nine patients from analysis because their followup was less than 12 months, and finally we included 261 patients in the analysis. Data examined included age at the time of surgery, sex, indication for surgery (impending fracture or completed pathologic fracture), number of bone metastases (solitary or multiple), presence or absence of visceral or lymph node metastases, preoperative hemoglobin concentration, absolute lymphocyte count, and the primary oncologic diagnosis. We performed receiver operating characteristic curve analysis and estimated the AUC as a measure of discriminatory ability. Decision curve analysis was performed to determine if and how the models should be used in the clinical setting. RESULTS: The AUCs for the 1-, 3-, 6-, and 12-month models were 0.77 (95% CI, 0.63-0.86), 0.80 (95% CI, 0.72-0.87), 0.83 (95% CI, 0.77-0.89), and 0.80 (95% CI, 0.75-0.86), respectively. Decision analysis indicated that the models conferred a positive net benefit (above the lines assuming none or all survive at each time) although the CIs of the AUC for 1 month were wide, suggesting that this dataset could not adequately predict 1-month survival. CONCLUSIONS: Our findings show PATHFx is suitable for clinical use in Japan and may be used to guide surgical decision making or as a risk stratification method in support of clinical trials involving Japanese patients at 3, 6, and 12 months. More studies will be necessary to confirm the validity of the 1-month survival predictions of this mode. Other patient populations will need to be studied to confirm its usefulness in other non-Western and non-Japanese populations. LEVEL OF EVIDENCE: Level II, prognostic study.

The Effect of Surgical Technique and Spacer Texture on Bone Regeneration: A Caprine Study Using the Masquelet Technique.

BACKGROUND: The Masquelet-induced-membrane technique is a commonly used method for treating segmental bone defects. However, there are no established clinical standards for management of the induced membrane before grafting. QUESTIONS/PURPOSES: Two clinically based theories were tested in a chronic caprine tibial defect model: (1) a textured spacer that increases the induced-membrane surface area will increase bone regeneration; and (2) surgical scraping to remove a thin tissue layer of the inner induced-membrane surface will enhance bone formation. METHODS: Thirty-two skeletally mature female goats were assigned to four groups: smooth spacer with or without membrane scraping and textured spacer with or without membrane scraping. During an initial surgical procedure (unilateral, left tibia), a defect was created excising bone (5 cm), periosteum (9 cm), and muscle (10 g). Segments initially were stabilized with an intramedullary rod and an antibiotic-impregnated polymethylmethacrylate spacer with a smooth or textured surface. Four weeks later, the spacer was removed and the induced-membrane was either scraped or left intact before bone grafting. Bone formation was assessed using micro-CT (total bone volume in 2.5-cm central defect region) as the primary outcome; radiographs and histologic analysis as secondary outcomes, with the reviewer blinded to the treatment groups of the samples being assessed 12 weeks after grafting. All statistical tests were performed using a linear mixed effects model approach. RESULTS: Micro-CT analysis showed greater bone formation in defects with scraped induced membrane (mean, 3034.5 mm3; median, 1928.0 mm3; quartile [Q]1-Q3, 273.3-2921.1 mm3) compared with defects with intact induced membrane (mean, 1709.5 mm3; median, 473.8 mm3; Q1-Q3, 132.2-1272.3 mm3; p = 0.034). There was no difference in bone formation between textured spacers (mean, 2405.5 mm3; median, 772.7 mm3; Q1-Q3, 195.9-2743.8 mm3) and smooth spacers (mean, 2473.2 mm3; median, 1143.6 mm3; Q1-Q3, 230.2-451.1 mm3; p = 0.917). CONCLUSIONS: Scraping the induced-membrane surface to remove the innermost layer of the induced-membrane increased bone regeneration. A textured spacer that increased the induced-membrane surface area had no effect on bone regeneration. CLINICAL RELEVANCE: Scraping the induced membrane during the second stage of the Masquelet technique may be a rapid and simple means of improving healing of segmental bone defects, which needs to be confirmed clinically.