Search

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 (r2 = 0.24; p < 0.001), noncombat trauma (r2 = 0.20; p < 0.001), servicemembers with a high ISS (r2 = 0.10; p < 0.001), and servicemembers exposed to high doses of radiation (r2 = 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Volume 479, Number 3 Reply to the Letter to the Editor 637 Letter to the Editor Copyright © 2020 by the Association of Bone and Joint Surgeons. Downloaded from http://journals.lww.com/clinorthop by BhDMf5ePHKbH4TTImqenVA+lpWIIBvonhQl60EtgtdlLYrLzSPu+hQedJnbNaXBf on 04/05/2021 Downloaded from http://journals.lww.com/clinorthop by BhDMf5ePHKbH4TTImqenVA+lpWIIBvonhQl60EtgtdlLYrLzSPu+hQedJnbNaXBf on 04/05/2021 Clin Orthop Relat Res (2021) 479:636-638 DOI 10.1097/CORR.0000000000001632 Letter to the Editor Published online: 4 January 2021 Copyright © 2020 by the Association of Bone and Joint Surgeons Reply to the Letter to the Editor: Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use after ACL Reconstruction?. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 () 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 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. [ABSTRACT FROM AUTHOR]

The article offers information on the clinical importance of incidental findings on staging Computed tomography (CT) scans in patients with sarcoma. Further discusses the presence of metastatic disease in patients with soft tissue and bone sarcomas remains the single most important determining factor of long-term survival; and detection of the indeterminate nodules during staging represented metastatic disease.

Background: Heterotopic ossification (HO) affects the majority of combat-related lower extremity wounds involving severe fracture and amputation. Defining the timing of early osteogenic-related genes may help identify candidate prophylactic agents and guide the timing of prophylactic therapy after blast and other combat-related extremity injuries. Questions/purposes: Using a recently developed animal model of combat-related HO, we sought to determine (1) the timing of early chondrogenesis, cartilage formation, and radiographic ectopic bone development; and (2) the early cartilage and bone-related gene and protein patterns in traumatized soft tissue. Methods: We used an established rat HO model consisting of blast exposure, controlled femur fracture, crush injury, and transfemoral amputation through the zone of injury. Postoperatively, rats were euthanized on Days 3 to 28. We assessed evidence of early ectopic bone formation by micro-CT and histology and performed proteomic and gene expression analysis. Results: All rats showed radiographic evidence of HO within 28 days. Key chondrogenic ( collagen type I alpha 1 [ COL1α1], p = 0.016) and osteogenic-related genes ( Runt-related transcription factor 2 [ RUNX-2], p = 0.029; osteoclacin [ OCN], p = 0.032; phosphate-regulating neutral endopeptidase, X-linked [ PHEX], p = 0.0290, and POU domain class 5 transcription factor [ POU5F], p = 0.016) and proteins ( Noggin [ NOG], p = 0.04, OCN, p = 0.02, RUNX- 2, p = 0.04, and substance P-1 [ SP-1], p = 0.01) in the injured soft tissue, normalized to the contralateral limb and/or sham-treated naïve rats, increased on Days 3 to 14 postinjury. By 14 days, foci of hypertrophic chondrocytes, hyaline cartilage, and woven bone were present in the soft tissue surrounding the amputation site. Conclusions: We found that genes that regulate early chondrogenic and osteogenic signaling and bone development ( COL1α1, RUNX-2, OCN, PHEX, and POU5F1) are induced early during the tissue reparative/healing phase in a rat model simulating a combat-related extremity injury. Clinical Relevance: The ability to correlate molecular events with histologic and morphologic changes will assist researchers and clinicians to understand HO and hence formulate therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Heterotopic ossification (HO) is common after combat-related amputations and surgical excision remains the only definitive treatment for persistently symptomatic HO. There is no consensus in the literature regarding the timing of surgery, and recurrence frequency, reexcision, and complications have not been reported in large numbers of patients. Questions/purposes: (1) What are the rates of symptomatic recurrence resulting in reexcision and other complications resulting in reoperation in patients with HO? (2) Is either radiographic or symptomatic recurrence dependent on timing and type of initial surgery, the experience of the surgeon in performing the procedure, the severity of preexcision HO, the presence of concomitant neurologic injury, or the use of postoperative HO prophylaxis? Methods: Between March 2005 and March 2013 our institution treated 994 patients with 1377 combat-related major extremity amputations; of those, 172 amputations underwent subsequent excision of symptomatic HO. The mechanism of injury resulting in nearly all amputations (n = 168) was blast-related trauma. We reviewed medical records and radiographs to collect initial grade of HO, radiographic recurrence, complete compared with partial excision, concomitant neurologic injury, timing to initial surgery, surgeon experience, and use of postexcision prophylaxis with our primary study outcome being a return to the operating room (OR) for repeat excision of symptomatic HO. All 172 combat-related amputations were considered for this study irrespective of followup, which was noted to be robust, with 157 (91%) amputations having at least 6 months clinical followup by an orthopaedic surgeon or physiatrist (median, 20 months; range, 0-88 months). Results: Eleven of 172 patients (6.5%) underwent reexcision of HO, and 67 complications resulting in return to the OR occurred in 53 patients (31%) of patients. Multivariate analysis of our primary outcome measure showed more frequent symptomatic recurrences requiring reexcision when initial excision was performed as a partial excision (p = 0.03; odds ratio [OR], 5.0; 95% confidence interval [CI], 1.2-29.6) or when the initial excision was performed within 180 days of injury (p = 0.047; OR, 4.1; 95% CI, 1.02-16.6). There was no association between symptomatic recurrence and HO grade, central nervous system injury, experience of the attending surgeon, or postoperative prophylaxis. Radiographic recurrence was observed when partial excisions (eight of 30 [27%]) were done compared with complete excisions (five of 77 [7%]; p = 0.008). Conclusions: HO is common after combat-related amputations, and patients undergoing surgical excision of HO for this indication often have complications that result in repeat surgical procedures. Partial excisions of immature lesions more often resulted in both symptomatic and radiographic recurrence. The likelihood of a patient undergoing reexcision can be minimized by performing a complete excision at least 180 days from injury to surgery with no evidence of a reduced risk of reexcision by waiting longer than 270 days. Level of Evidence: Level III, therapeutic study. [ABSTRACT FROM AUTHOR]

Background: Heterotopic ossification (HO) develops in a majority of combat-related amputations wherein early bacterial colonization has been considered a potential early risk factor. Our group has recently developed a small animal model of trauma-induced HO that incorporates many of the multifaceted injury patterns of combat trauma in the absence of bacterial contamination and subsequent wound colonization. Questions/purposes: We sought to determine if (1) the presence of bioburden ( Acinetobacter baumannii and methicillin-resistant Staphylococcus aureus [MRSA]) increases the magnitude of ectopic bone formation in traumatized muscle after amputation; and (2) what persistent effects bacterial contamination has on late microbial flora within the amputation site. Methods: Using a blast-related HO model, we exposed 48 rats to blast overpressure, femur fracture, crush injury, and subsequent immediate transfemoral amputation through the zone of injury. Control injured rats (n = 8) were inoculated beneath the myodesis with phosphate-buffered saline not containing bacteria (vehicle) and treatment rats were inoculated with 1 × 10 colony-forming units of A baumannii (n = 20) or MRSA (n = 20). All animals formed HO. Heterotopic ossification was determined by quantitative volumetric measurements of ectopic bone at 12-weeks postinjury using micro-CT and qualitative histomorphometry for assessment of new bone formation in the residual limb. Bone marrow and muscle tissue biopsies were collected from the residual limb at 12 weeks to quantitatively measure the bioburden load and to qualitatively determine the species-level identification of the bacterial flora. Results: At 12 weeks, we observed a greater volume of HO in rats infected with MRSA (68.9 ± 8.6 mm; 95% confidence interval [CI], 50.52-85.55) when compared with A baumannii (20.9 ± 3.7 mm; 95% CI, 13.61-28.14; p < 0.001) or vehicle (16.3 ± 3.2 mm; 95% CI, 10.06-22.47; p < 0.001). Soft tissue and marrow from the residual limb of rats inoculated with A baumannii tested negative for A baumannii infection but were positive for other strains of bacteria (1.33 × 10 ± 0.89 × 10; 95% CI, −0.42 × 10-3.08 × 10 and 1.25 × 10 ± 0.69 × 10; 95% CI, −0.13 × 10-2.60 × 10 colony-forming units in bone marrow and muscle tissue, respectively), whereas tissue from MRSA-infected rats contained MRSA only (4.84 × 10 ± 3.22 × 10; 95% CI, −1.47 × 10-11.1 × 10 and 2.80 × 10 ± 1.73 × 10; 95% CI, −0.60 × 10-6.20 × 10 in bone marrow and muscle tissue, respectively). Conclusions: Our findings demonstrate that persistent infection with MRSA results in a greater volume of ectopic bone formation, which may be the result of chronic soft tissue inflammation, and that early wound colonization may be a key risk factor. Clinical Relevance: Interventions that mitigate wound contamination and inflammation (such as early débridement, systemic and local antibiotics) may also have a beneficial effect with regard to the mitigation of HO formation and should be evaluated with that potential in mind in future preclinical studies. [ABSTRACT FROM AUTHOR]

Background: To prevent symptomatic heterotopic ossification (HO) and guide primary prophylaxis in patients with combat wounds, physicians require risk stratification methods that can be used early in the postinjury period. There are no validated models to help guide clinicians in the treatment for this common and potentially disabling condition. Questions/purposes: We developed three prognostic models designed to estimate the likelihood of wound-specific HO formation and compared them using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) to determine (1) which model is most accurate; and (2) which technique is best suited for clinical use. Methods: We obtained muscle biopsies from 87 combat wounds during the first débridement in the United States, all of which were evaluated radiographically for development of HO at a minimum of 2 months postinjury. The criterion for determining the presence of HO was the ability to see radiographic evidence of ectopic bone formation within the zone of injury. We then quantified relative gene expression from 190 wound healing, osteogenic, and vascular genes. Using these data, we developed an Artificial Neural Network, Random Forest, and a Least Absolute Shrinkage and Selection Operator (LASSO) Logistic Regression model designed to estimate the likelihood of eventual wound-specific HO formation. HO was defined as any HO visible on the plain film within the zone of injury. We compared the models accuracy using area under the ROC curve (area under the curve [AUC]) as well as DCA to determine which model, if any, was better suited for clinical use. In general, the AUC compares models based solely on accuracy, whereas DCA compares their clinical utility after weighing the consequences of under- or overtreatment of a particular disorder. Results: Both the Artificial Neural Network and the LASSO logistic regression models were relatively accurate with AUCs of 0.78 (95% confidence interval [CI], 0.72-0.83) and 0.75 (95% CI, 0.71-0.78), respectively. The Random Forest model returned an AUC of only 0.53 (95% CI, 0.48-0.59), marginally better than chance alone. Using DCA, the Artificial Neural Network model demonstrated the highest net benefit over the broadest range of threshold probabilities, indicating that it is perhaps better suited for clinical use than the LASSO logistic regression model. Specifically, if only patients with greater than 25% risk of developing HO received prophylaxis, for every 100 patients, use of the Artificial Network Model would result in six fewer patients who unnecessarily receive prophylaxis compared with using the LASSO regression model while not missing any patients who might benefit from it. Conclusions: Our findings suggest that it is possible to risk-stratify combat wounds with regard to eventual HO formation early in the débridement process. Using these data, the Artificial Neural Network model may lead to better patient selection when compared with the LASSO logistic regression approach. Future prospective studies are necessary to validate these findings while focusing on symptomatic HO as the endpoint of interest. Level of Evidence: Level III, prognostic study. [ABSTRACT FROM AUTHOR]

Background: Open calcaneus fractures can be limb threatening and almost universally result in some measure of long-term disability. A major goal of initial management in patients with these injuries is setting appropriate expectations and discussing the likelihood of limb salvage, yet there are few tools that assist in predicting the outcome of this difficult fracture pattern. Questions/purposes: We developed two decision support tools, an artificial neural network and a logistic regression model, based on presenting data from severe combat-related open calcaneus fractures. We then determined which model more accurately estimated the likelihood of amputation and which was better suited for clinical use. Methods: Injury-specific data were collected from wounded active-duty service members who sustained combat-related open calcaneus fractures between 2003 and 2012. One-hundred fifty-five open calcaneus fractures met inclusion criteria. Median followup was 3.5 years (interquartile range: 1.5, 5.1 years), and amputation rate was 44%. We developed an artificial neural network designed to estimate the likelihood of amputation, using information available on presentation. For comparison, a conventional logistic regression model was developed with variables identified on univariate analysis. We determined which model more accurately estimated the likelihood of amputation using receiver operating characteristic analysis. Decision curve analysis was then performed to determine each model's clinical utility. Results: An artificial neural network that contained eight presenting features resulted in smaller error. The eight features that contributed to the most predictive model were American Society of Anesthesiologist grade, plantar sensation, fracture treatment before arrival, Gustilo-Anderson fracture type, Sanders fracture classification, vascular injury, male sex, and dismounted blast mechanism. The artificial neural network was 30% more accurate, with an area under the curve of 0.8 (compared to 0.65 for logistic regression). Decision curve analysis indicated the artificial neural network resulted in higher benefit across the broadest range of threshold probabilities compared to the logistic regression model and is perhaps better suited for clinical use. Conclusions: This report demonstrates an artificial neural network was capable of accurately estimating the likelihood of amputation. Furthermore, decision curve analysis suggested the artificial neural network is better suited for clinical use than logistic regression. Once properly validated, this may provide a tool for surgeons and patients faced with combat-related open calcaneus fractures in which decisions between limb salvage and amputation remain difficult. Level of Evidence: Level IV, prognostic study. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Background: In the acute postoperative period, fluid collections are common in lower extremity amputations. Whether these fluid collections increase the risk of infection is unknown. Questions/purposes: The purposes of this study were to determine (1) the percentage of patients who develop postoperative fluid collections in posttraumatic amputations and the natural course of the collection; (2) whether patients who develop these collections are at increased risk for infection; and to ask (3) are there objective clinical or radiologic signs that are associated with likelihood of infection when a fluid collection is present? Methods: We performed a review of all 300 patients injured in combat operations who sustained at least one major lower extremity amputation (at or proximal to the tibiotalar joint) and were treated definitively at our institution between March 2005 and April 2009. We segregated the groups based on whether cross-sectional imaging was performed less than 3 months (early group) after closure, greater than 3 months (late group) after closure, or not at all (control group, baseline frequency of infection). Our primary study cohort where those patients with a fluid collection in the first three months. The clinical course was reviewed and the primary outcome was a return to the operating room for irrigation and débridement with positive cultures. For those patients with cross-sectional imaging, we also collected objective clinical parameters within 24 hours of the scan (white blood cell count, maximum temperature, presence of bacteremia, tachycardia, oxygen desaturation), extremity examination (presence of erythema, warmth, and/or drainage), and characteristics of the fluid collections seen (size of the fluid collection, enhancement, complexity (simple versus loculated), surrounding edema, skin changes, tract formation, presence of air, and changes within the bone itself). The presence of a fluid collection on imaging was analyzed to determine whether it was associated with infection. We further analyzed clinical parameters, objective physical examination findings at the extremity, and characteristics of the fluid collection to determine if there were other parameters associated with infection. Results: Over half (55%) of the limbs demonstrated fluid collection in the early postoperative period and the prevalence decreased in the late group (11%; p = 0.001). There was no association between the presence of a fluid collection and infection. However, there was an association between objective clinical signs at the extremity (erythema and/or drainage) and infection (p < 0.001) in our primary study cohort. Conclusions: Fluid collections are common in combat-related amputations in the immediate postoperative period and become smaller and less frequent over time. In the absence of extremity erythema and wound drainage, imaging of a residual limb to evaluate for the presence of a fluid collection appears to be of little clinical use. Level of Evidence: Level III, diagnostic study. See the Guidelines for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Background: After a decade of war in Iraq and Afghanistan, we have observed an increase in combat-related injury survival and a paradoxical increase in injury severity, mainly because of the effects of blasts. These severe injuries have a devastating effect on each patient's immune system resulting in massive upregulation of the systemic inflammatory response. By examining inflammatory mediators, preliminary data suggest that it may be possible to correlate complications such as wound failure and heterotopic ossification (HO) with distinct systemic and local inflammatory profiles, but this is a relatively new topic. Questions/purposes: We asked whether systemic or local markers of inflammation could be used as an objective means, independent of demographic and subjective factors, to estimate the likelihood of (1) HO and/or (2) wound failure (defined as wounds requiring surgical débridement after definitive closure, or wounds that were not closed or covered within 21 days of injury) in patients sustaining combat wounds. Methods: Two hundred combat wounded active-duty service members who sustained high-energy extremity injuries were prospectively enrolled between 2008 and 2012. Of these 200 patients, 189 had adequate followups to determine the presence or absence of HO, and 191 had adequate followups to determine the presence or absence of wound failure. In addition to injury-specific and demographic data, we quantified 24 cytokines and chemokines during each débridement. Patients were followed clinically for 6 weeks, and radiographs were obtained 3 months after definitive wound closure. Associations were investigated between these markers and wound failure or HO, while controlling for known confounders. Results: The presence of an amputation (p < 0.001; odds ratio [OR], 6.1; 95% CI. 1.63-27.2), Injury Severity Score (p = 0.002; OR, 33.2; 95% CI, 4.2-413), wound surface area (p = 0.001; OR, 1.01; 95% CI, 1.002-1.009), serum interleukin (IL)-3 (p = 0.002; OR, 2.41; 95% CI, 1.5-4.5), serum IL-12p70 (p = 0.01; OR, 0.49; 95% CI, 0.27-0.81), effluent IL-3 (p = 0.02; OR, 1.75; 95% CI, 1.2-2.9), and effluent IL-13 (p = 0.006; OR, 0.67; 95% CI, 0.50-0.87) were independently associated with HO formation. Injury Severity Score (p = 0.05; OR, 18; 95% CI, 5.1-87), wound surface area (p = 0.05; OR, 28.7; 95% CI, 1.5-1250), serum procalcitonin ([ProCT] (p = 0.03; OR, 1596; 95% CI, 5.1-1,758,613) and effluent IL-6 (p = 0.02; OR, 83; 95% CI, 2.5-5820) were independently associated with wound failure. Conclusions: We identified associations between patients' systemic and local inflammatory responses and wound-specific complications such as HO and wound failure. However, future efforts to model these data must account for their complex, time dependent, and nonlinear nature. Level of Evidence: Level II, prognostic study. See the Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Background: Heterotopic ossification (HO) is a frequent complication of modern wartime extremity injuries. The biological mechanisms responsible for the development of HO in traumatic wounds remain elusive. Question/purposes: The aims of our study were to (1) characterize the expression profile of osteogenesis-related gene transcripts in traumatic war wounds in which HO developed; and (2) determine whether expression at the mRNA level correlated with functional protein expression and HO formation. Methods: Biopsy specimens from 54 high-energy penetrating extremity wounds obtained at the initial and final surgical débridements were evaluated. The levels of selected osteogenic-related gene transcripts from RNA extracts were assessed by quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) analysis. As a result of its key role in osteogenesis, the concentration of BMP-2 in the effluent of 29 wounds also was determined. Results: The transcripts of 13 genes (ALPL [p = 0.006], BMP-2 [p < 0.001], BMP-3 [p = 0.06], COL2A1 [p < 0.001], COLL10A1 [p < 0.001], COL11A1 [p = 0.006], COMP [p = 0.02], CSF2 [p = 0.003], CSF3 [p = 0.012], MMP8 [p < 0.001], MMP9 [p = 0.014], SMAD1 [p = 0.024], and VEGFA [p = 0.017]) were upregulated greater than twofold in wounds in which HO developed compared with wounds in which it did not develop. Gene transcript expression of BMP-2 also correlated directly with functional protein expression in the wounds that formed HO (p = 0.029). Conclusions: Important differences exist in the osteogenic gene expression profile of wounds in which HO developed compared with wounds in which it did not develop. The upregulation of multiple osteogenesis-related gene transcripts indicates the presence of a proosteogenic environment necessary for ectopic bone formation in traumatic wounds. Clinical Relevance: Understanding the osteogenic environment associated with war wounds may allow for the development of novel therapeutic strategies for HO. [ABSTRACT FROM AUTHOR]

Background: Successful treatment of pathologic femur fractures can preserve a patient's independence and quality of life. The choice of implant depends on several disease- and patient-specific variables; however, its durability must generally match the patient's estimated life expectancy. Failures do occur, however, it is unclear which implants are associated with greater risk of failure. Questions/Purposes: We evaluated patients with femoral metastases in whom implants failed to determine (1) the rate of reoperation; (2) the timing of and most common causes for failure; and (3) incidence of perioperative complications and death. Methods: From a prospectively collected registry, we identified 93 patients operated on for failed treatment of femoral metastases from 1990 to 2010. We excluded five patients who subsequently underwent amputations leaving 88 who underwent salvage procedures. These included intramedullary nails (n = 11), endoprostheses (n = 61), and plate fixation (n = 16). The primary outcome was reoperation after salvage treatment. Results: Seventeen of the 88 patients (19%) required subsequent reoperation a median of 10 months (interquartile range, 4-14) from the time of salvage surgery: 15 for material failure, one for local progression of tumor, and one for a combination of these. Five patients died within 4 weeks of surgery. Although perioperative complications were higher in the endoprosthesis group and dislocations occurred, overall treatment failures after salvage surgery were lower in the that group (four of 61) compared the group with plate fixation (eight of 16) and intramedullary nail groups (five of 11). Conclusions: Despite relatively common perioperative complications, salvage using endoprostheses may be associated with fewer treatment failures as compared with internal fixation. Level of Evidence: Level III, therapeutic study. See Guidelines for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Background : To avoid complications associated with under- or overtreatment of patients with skeletal metastases, doctors need accurate survival estimates. Unfortunately, prognostic models for patients with skeletal metastases of the extremities are lacking, and physician-based estimates are generally inaccurate. Questions/purposes: We developed three types of prognostic models and compared them using calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis to determine which one is best suited for clinical use. Methods: A training set consisted of 189 patients who underwent surgery for skeletal metastases. We created models designed to predict 3- and 12-month survival using three methods: an Artificial Neural Network (ANN), a Bayesian Belief Network (BBN), and logistic regression. We then performed crossvalidation and compared the models in three ways: calibration plots plotting predicted against actual risk; area under the ROC curve (AUC) to discriminate the probability that a patient who died has a higher predicted probability of death compared to a patient who did not die; and decision curve analysis to quantify the clinical consequences of over- or undertreatment. Results: All models appeared to be well calibrated, with the exception of the BBN, which underestimated 3-month survival at lower probability estimates. The ANN models had the highest discrimination, with an AUC of 0.89 and 0.93, respectively, for the 3- and 12-month models. Decision analysis revealed all models could be used clinically, but the ANN models consistently resulted in the highest net benefit, outperforming the BBN and logistic regression models. Conclusions: Our observations suggest use of the ANN model to aid decisions about surgery would lead to better patient outcomes than other alternative approaches to decision making. Level of Evidence: Level II, prognostic study. See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses papers presented in the tenth Extremity War Injuries and Disaster Preparedness Symposium regarding the advance of science and technology of combat casualty care.

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. [ABSTRACT FROM AUTHOR]