The Skeletal Oncology Research Group Machine Learning Algorithm (SORG-MLA) for predicting prolonged postoperative opioid prescription after total knee arthroplasty: an international validation study using 3,495 patients from a Taiwanese cohort.

BACKGROUND: Preoperative prediction of prolonged postoperative opioid use (PPOU) after total knee arthroplasty (TKA) could identify high-risk patients for increased surveillance. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) has been tested internally while lacking external support to assess its generalizability. The aims of this study were to externally validate this algorithm in an Asian cohort and to identify other potential independent factors for PPOU. METHODS: In a tertiary center in Taiwan, 3,495 patients receiving TKA from 2010-2018 were included. Baseline characteristics were compared between the external validation cohort and the original developmental cohorts. Discrimination (area under receiver operating characteristic curve [AUROC] and precision-recall curve [AUPRC]), calibration, overall performance (Brier score), and decision curve analysis (DCA) were applied to assess the model performance. A multivariable logistic regression was used to evaluate other potential prognostic factors. RESULTS: There were notable differences in baseline characteristics between the validation and the development cohort. Despite these variations, the SORG-MLA ( https://sorg-apps.shinyapps.io/tjaopioid/ ) remained its good discriminatory ability (AUROC, 0.75; AUPRC, 0.34) and good overall performance (Brier score, 0.029; null model Brier score, 0.032). The algorithm could bring clinical benefit in DCA while somewhat overestimating the probability of prolonged opioid use. Preoperative acetaminophen use was an independent factor to predict PPOU (odds ratio, 2.05). CONCLUSIONS: The SORG-MLA retained its discriminatory ability and good overall performance despite the different pharmaceutical regulations. The algorithm could be used to identify high-risk patients and tailor personalized prevention policy.

External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort.

BACKGROUND/PURPOSE: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment plans. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was developed to predict the risk of prolonged opioid use in opioid-naive patients after lumbar spine surgery. However, its utility in a distinct country remains unknown. METHODS: A Taiwanese cohort containing 2795 patients who were 20 years or older undergoing primary surgery for lumbar decompression from 2010 to 2018 were used to validate the SORG-MLA. Discrimination (area under receiver operating characteristic curve [AUROC] and area under precision-recall curve [AUPRC]), calibration, overall performance (Brier score), and decision curve analysis were applied. RESULTS: Among 2795 patients, the prolonged opioid prescription rate was 5.2%. The validation cohort were older, more inpatient disposition, and more common pharmaceutical history of NSAIDs. Despite the differences, the SORG-MLA provided a good discriminative ability (AUROC of 0.71 and AURPC of 0.36), a good overall performance (Brier score of 0.044 compared to that of 0.039 in the developmental cohort). However, the probability of prolonged opioid prescription tended to be overestimated (calibration intercept of -0.07 and calibration slope of 1.45). Decision curve analysis suggested greater clinical net benefit in a wide range of clinical scenarios. CONCLUSION: The SORG-MLA retained good discriminative abilities and overall performances in a geologically and medicolegally different region. It was suitable for predicting patients in risk of prolonged postoperative opioid use in Taiwan.

Albumin and Survival in Extremity Metastatic Bone Disease: An Analysis of Two Independent Datasets.

INTRODUCTION: Numerous prognostication models have been developed to estimate survival in patients with extremity metastatic bone disease, but few include albumin despite albumin's role in malnutrition and inflammation. The purpose of this study was to examine two independent datasets to determine the value for albumin in prognosticating survival in this population. MATERIALS AND METHODS: Extremity metastatic bone disease patients undergoing surgical management were identified from two independent populations. Population 1: Retrospective chart review at two tertiary care centers. Population 2: A large, national, North American multicenter surgical registry with 30-day follow-up. Bivariate and multivariate analyses were used to examine albumin's value for prognostication at 1-, 3-, and 12-month after surgery. RESULTS: In Population 1, 1,090 patients were identified with 1-, 3-, and 12-month mortality rates of 95 (8.8%), 305 (28.9%), and 639 (62.0%), respectively. In Population 2, 1,675 patients were identified with one-month postoperative mortality rates of 148 (8.8%). In both populations, hypoalbuminemia was an independent prognostic factor for mortality at 30 days. In the institutional set, hypoalbuminemia was additionally associated with 3- and 12-month mortality. CONCLUSIONS: Hypoalbuminemia is a marker for mortality in extremity metastatic bone disease. Further consideration of this marker could improve existing prognostication models in this population. LEVEL OF EVIDENCE: III.

Do Cohabitants Reliably Complete Questionnaires for Patients in a Terminal Cancer Stage when Assessing Quality of Life, Pain, Depression, and Anxiety?

BACKGROUND: Patients with bone metastases often are unable to complete quality of life (QoL) questionnaires, and cohabitants (such as spouses, domestic partners, offspring older than 18 years, or other people who live with the patient) could be a reliable alternative. However, the extent of reliability in this complicated patient population remains undefined, and the influence of the cohabitant's condition on their assessment of the patient's QoL is unknown. QUESTIONS/PURPOSES: (1) Do QoL scores, measured by the 5-level EuroQol-5D (EQ-5D-5L) version and the Patient-reported Outcomes Measurement Information System (PROMIS) version 1.0 in three domains (anxiety, pain interference, and depression), reported by patients differ markedly from scores as assessed by their cohabitants? (2) Do cohabitants' PROMIS-Depression scores correlate with differences in measured QoL results? METHODS: This cross-sectional study included patients and cohabitants older than 18 years of age. Patients included those with presence of histologically confirmed bone metastases (including lymphoma and multiple myeloma), and cohabitants must have been present at the clinic visit. Patients were eligible for inclusion in the study regardless of comorbidities, prognosis, prior surgery, or current treatment. Between June 1, 2016 and March 1, 2017 and between October 1, 2017 and February 26, 2018, all 96 eligible patients were approached, of whom 49% (47) met the selection criteria and were willing to participate. The included 47 patient-cohabitant pairs independently completed the EQ-5D-5L and the eight-item PROMIS for three domains (anxiety, pain, and depression) with respect to the patients' symptoms. The cohabitants also completed the four-item PROMIS-Depression survey with respect to their own symptoms. RESULTS: There were no clinically important differences between the scores of patients and their cohabitants for all questionnaires, and the agreement between patient and cohabitant scores was moderate to strong (Spearman correlation coefficients ranging from 0.52 to 0.72 on the four questionnaires; all p values < 0.05). However, despite the good agreement in QoL scores, an increased cohabitant's depression score was correlated with an overestimation of the patient's symptom burden for the anxiety and depression domains (weak Spearman correlation coefficient of 0.33 [95% confidence interval 0.08 to 0.58]; p = 0.01 and moderate Spearman correlation coefficient of 0.52 [95% CI 0.29 to 0.74]; p < 0.01, respectively). CONCLUSION: The present findings support that cohabitants might be reliable raters of the QoL of patients with bone metastases. However, if a patient's cohabitant has depression, the cohabitant may overestimate a patient's symptoms in emotional domains such as anxiety and depression, warranting further research that includes cohabitants with and without depression to elucidate the effect of depression on the level of agreement. For now, clinicians may want to reconsider using the cohabitant's judgement if depression is suspected. CLINICAL RELEVANCE: These findings suggest that a cohabitant's impressions of a patient's quality of life are, in most instances, accurate; this is potentially helpful in situations where the patient cannot weigh in. Future studies should employ longitudinal designs to see how or whether our findings change over time and with disease progression, and how specific interventions-like different chemotherapeutic regimens or surgery-may factor in.

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.

Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed.Material and methods - We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics.Results - Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635-26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73-0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis.Interpretation - ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.

Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines.Material and methods - We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting.Results - We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43-89), with 6 items being reported in less than 4/18 of the studies.Interpretation - Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review.

BACKGROUND: Machine learning (ML) is a subdomain of artificial intelligence that enables computers to abstract patterns from data without explicit programming. A myriad of impactful ML applications already exists in orthopaedics ranging from predicting infections after surgery to diagnostic imaging. However, no systematic reviews that we know of have compared, in particular, the performance of ML models with that of clinicians in musculoskeletal imaging to provide an up-to-date summary regarding the extent of applying ML to imaging diagnoses. By doing so, this review delves into where current ML developments stand in aiding orthopaedists in assessing musculoskeletal images. QUESTIONS/PURPOSES: This systematic review aimed (1) to compare performance of ML models versus clinicians in detecting, differentiating, or classifying orthopaedic abnormalities on imaging by (A) accuracy, sensitivity, and specificity, (B) input features (for example, plain radiographs, MRI scans, ultrasound), (C) clinician specialties, and (2) to compare the performance of clinician-aided versus unaided ML models. METHODS: A systematic review was performed in PubMed, Embase, and the Cochrane Library for studies published up to October 1, 2019, using synonyms for machine learning and all potential orthopaedic specialties. We included all studies that compared ML models head-to-head against clinicians in the binary detection of abnormalities in musculoskeletal images. After screening 6531 studies, we ultimately included 12 studies. We conducted quality assessment using the Methodological Index for Non-randomized Studies (MINORS) checklist. All 12 studies were of comparable quality, and they all clearly included six of the eight critical appraisal items (study aim, input feature, ground truth, ML versus human comparison, performance metric, and ML model description). This justified summarizing the findings in a quantitative form by calculating the median absolute improvement of the ML models compared with clinicians for the following metrics of performance: accuracy, sensitivity, and specificity. RESULTS: ML models provided, in aggregate, only very slight improvements in diagnostic accuracy and sensitivity compared with clinicians working alone and were on par in specificity (3% (interquartile range [IQR] -2.0% to 7.5%), 0.06% (IQR -0.03 to 0.14), and 0.00 (IQR -0.048 to 0.048), respectively). Inputs used by the ML models were plain radiographs (n = 8), MRI scans (n = 3), and ultrasound examinations (n = 1). Overall, ML models outperformed clinicians more when interpreting plain radiographs than when interpreting MRIs (17 of 34 and 3 of 16 performance comparisons, respectively). Orthopaedists and radiologists performed similarly to ML models, while ML models mostly outperformed other clinicians (outperformance in 7 of 19, 7 of 23, and 6 of 10 performance comparisons, respectively). Two studies evaluated the performance of clinicians aided and unaided by ML models; both demonstrated considerable improvements in ML-aided clinician performance by reporting a 47% decrease of misinterpretation rate (95% confidence interval [CI] 37 to 54; p < 0.001) and a mean increase in specificity of 0.048 (95% CI 0.029 to 0.068; p < 0.001) in detecting abnormalities on musculoskeletal images. CONCLUSIONS: At present, ML models have comparable performance to clinicians in assessing musculoskeletal images. ML models may enhance the performance of clinicians as a technical supplement rather than as a replacement for clinical intelligence. Future ML-related studies should emphasize how ML models can complement clinicians, instead of determining the overall superiority of one versus the other. This can be accomplished by improving transparent reporting, diminishing bias, determining the feasibility of implantation in the clinical setting, and appropriately tempering conclusions. LEVEL OF EVIDENCE: Level III, diagnostic study.

Development and Internal Validation of Machine Learning Algorithms for Preoperative Survival Prediction of Extremity Metastatic Disease.

BACKGROUND: A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models. QUESTIONS/PURPOSES: The purposes of this study were (1) to develop machine learning algorithms for 90-day and 1-year survival in patients who received surgical treatment for a bone metastasis of the extremity, and (2) to use these algorithms to identify those clinical factors (demographic, treatment related, or surgical) that are most closely associated with survival after surgery in these patients. METHODS: All 1090 patients who underwent surgical treatment for a long-bone metastasis at two institutions between 1999 and 2017 were included in this retrospective study. The median age of the patients in the cohort was 63 years (interquartile range [IQR] 54 to 72 years), 56% of patients (610 of 1090) were female, and the median BMI was 27 kg/m (IQR 23 to 30 kg/m). The most affected location was the femur (70%), followed by the humerus (22%). The most common primary tumors were breast (24%) and lung (23%). Intramedullary nailing was the most commonly performed type of surgery (58%), followed by endoprosthetic reconstruction (22%), and plate screw fixation (14%). Missing data were imputed using the missForest methods. Features were selected by random forest algorithms, and five different models were developed on the training set (80% of the data): stochastic gradient boosting, random forest, support vector machine, neural network, and penalized logistic regression. These models were chosen as a result of their classification capability in binary datasets. Model performance was assessed on both the training set and the validation set (20% of the data) by discrimination, calibration, and overall performance. RESULTS: We found no differences among the five models for discrimination, with an area under the curve ranging from 0.86 to 0.87. All models were well calibrated, with intercepts ranging from -0.03 to 0.08 and slopes ranging from 1.03 to 1.12. Brier scores ranged from 0.13 to 0.14. The stochastic gradient boosting model was chosen to be deployed as freely available web-based application and explanations on both a global and an individual level were provided. For 90-day survival, the three most important factors associated with poorer survivorship were lower albumin level, higher neutrophil-to-lymphocyte ratio, and rapid growth primary tumor. For 1-year survival, the three most important factors associated with poorer survivorship were lower albumin level, rapid growth primary tumor, and lower hemoglobin level. CONCLUSIONS: Although the final models must be externally validated, the algorithms showed good performance on internal validation. The final models have been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/extremitymetssurvival/. Pending external validation, clinicians may use this tool to predict survival for their individual patients to help in shared treatment decision making. LEVEL OF EVIDENCE: Level III, therapeutic study.

Prognostic value of serum alkaline phosphatase in spinal metastatic disease.

BACKGROUND: Determination of the appropriateness of invasive management in patients with spinal metastatic disease requires accurate pre-operative estimation of survival. The purpose of this study was to examine serum alkaline phosphatase as a prognostic marker in spinal metastatic disease. METHODS: Chart reviews from two tertiary care centres were used to identify spinal metastatic disease patients. Bivariate and multivariate analyses were used to determine if serum alkaline phosphatase was an independent prognostic marker for survival. RESULTS: Overall, 732 patients were included with 90-day and 1-year survival of n = 539 (74.9%) and n = 324 (45.7%), respectively. The 1-year survival of patients in the first quartile of alkaline phosphatase (≤73 IU/L) was 78 (57.8%) compared to 31 (24.0%) for patients in the fourth quartile (>140 IU/L). Preoperative serum alkaline phosphatase levels were significantly elevated in patients with multiple spine metastases, non-spine bone metastasis, and visceral metastasis but not in patients with brain metastasis. On multivariate analysis, elevated serum alkaline phosphatase was identified as an independent prognostic factor for survival in spinal metastatic disease. CONCLUSION: Serum alkaline phosphatase is associated with preoperative metastatic tumour burden and is a biomarker for overall survival in spinal metastatic disease.

Allograft reconstruction of the humerus: Complications and revision surgery.

BACKGROUND AND OBJECTIVES: Allograft reconstruction of the humerus after resection is preferred by many because of bone stock restoration and biologic attachment of ligaments and muscles to the allograft, theoretically obtaining superior stability and functionality. Our aim was to assess the prevalence of complications and the incidence and etiology for revision surgery in humeral allograft reconstructions. METHODS: We included patients 18 years and older who underwent wide resection and allograft reconstruction of the humerus for primary and metastatic lesions at our institution between 1990 and 2013. Our primary outcome measures were complications and revision surgery. We used competing risk regression to assess allograft survival. RESULTS: Of the 84 patients we included, 47 patients (51%) underwent allograft reconstructions of the proximal humerus, 30 (36%) intercalary, and seven (8%) of the distal humerus. Fifty-one patients (61%) had at least one complication after surgery. Eighteen patients (21%) underwent revision surgery. The 5-year allograft survival was 71%. CONCLUSION: Although allograft reconstructions of the humerus are a valuable option in the orthopedic oncologist's armamentarium, surgeons should mind the accompanying complication rates. Allograft fractures seem to be the main issue for proximal and distal allografts, often leading to revision surgery. Intercalary allografts are mostly troubled by nonunions.

Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

PURPOSE: We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis. METHODS: The National Surgical Quality Improvement Program (NSQIP) database was used to select patients that underwent surgical treatment for degenerative spondylolisthesis between 2009 and 2016. Our primary outcome measure was non-home discharge which was defined as any discharge not to home for which we grouped together all non-home discharge destinations including rehabilitation facility, skilled nursing facility, and unskilled nursing facility. We used Akaike information criterion to select the most appropriate model based on the outcomes of the stepwise backward logistic regression. Four machine learning algorithms were developed to predict discharge placement and were assessed by discrimination, calibration, and overall performance. RESULTS: Nine thousand three hundred and thirty-eight patients were included. Median age was 63 (interquartile range [IQR] 54-71), and 63% (n = 5,887) were female. The non-home discharge rate was 18.6%. Our models included age, sex, diabetes, elective surgery, BMI, procedure, number of levels, ASA class, preoperative white blood cell count, and preoperative creatinine. The Bayes point machine was considered the best model based on discrimination (AUC = 0.753), calibration (slope = 1.111; intercept = - 0.002), and overall model performance (Brier score = 0.132). CONCLUSION: This study has shown that it is possible to create a predictive machine learning algorithm with both good accuracy and calibration to predict discharge placement. Using our methodology, this type of model can be developed for many other conditions and (elective) treatments. These slides can be retrieved under Electronic Supplementary Material.

Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis. METHODS: We used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance score. RESULTS: We included 28,600 patients with a median age of 67 (interquartile range 58-74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic = 0.751), calibration (slope = 0.933; intercept = 0.037), and overall performance (Brier score = 0.131). CONCLUSIONS: A machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower costs. These slides can be retrieved under Electronic Supplementary Material.

High Risk of Symptomatic Venous Thromboembolism After Surgery for Spine Metastatic Bone Lesions: A Retrospective Study.

BACKGROUND: Cancer and spinal surgery are both considered risk factors for venous thromboembolism (VTE). However, the risk of symptomatic VTE for patients undergoing surgery for spine metastases remains undefined. QUESTIONS/PURPOSES: The purposes of this study were to: (1) identify the proportion of patients who develop symptomatic VTE within 90-days of surgical treatment for spine metastases; (2) identify the factors associated with the development of symptomatic VTE among patients receiving surgery for spine metastases; (3) assess the association between the development of postoperative symptomatic VTE and 1-year survival among patients who underwent surgery for spine metastases; and (4) assess if chemoprophylaxis increases the risk of wound complications among patients who underwent surgery for spine metastases. METHODS: Between 2002 and 2014, 637 patients at two hospitals underwent spine surgery for metastases. We considered eligible for analysis adult patients whose procedures were to treat cervical, thoracic, or lumbar metastases (including lymphoma and multiple myeloma). At followup after 90 days and 1 year, respectively, 21 of 637 patients (3%) and 41 of 637 patients (6%) were lost to followup. In general, we used 40 mg of enoxaparin or 5000 IUs subcutaneous heparin every 12 hours. Patients on preoperative chemoprophylaxis continued their initial medication postoperatively. All chemoprophylaxis was started 48 hours after surgery and continued day to day but was discontinued if a bleeding complication developed. Low-molecular-weight heparin (including enoxaparin and dalteparin, in general dosages of respectively 40 mg and 5000 IUs daily) was the most commonly used chemoprophylaxis in 308 patients (48%). Subcutaneous heparin was injected into 127 patients (20%); aspirin was used for 92 patients (14%); and warfarin was administered in 21 patients (3.3%). No form of chemoprophylaxis was prescribed for 89 patients (14%). The primary outcome variable, VTE, was defined as any symptomatic pulmonary embolism (PE) or symptomatic deep venous thromboembolism (DVT) within 90 days of surgery as determined by chart review. The secondary outcome was defined as any documented wound complication within 90 days of surgery that might be attributable to chemoprophylaxis. Statistical analysis was performed using multivariable logistic and Cox regression and Kaplan-Meier. RESULTS: Overall, 72 of 637 patients (11%) had symptomatic VTE; 38 (6%) developed a PE-eight (1.3%) of which were fatal-and 40 (6%) a DVT. After controlling for relevant confounding variables such as age, the modified Charlson Comorbidity Index, visceral metastases, and chemoprophylaxis, longer duration of surgery was independently associated with an increased risk of symptomatic VTE (odds ratio 1.15 for each additional hour of surgery; 95% confidence interval [CI], 1.04-1.28; p = 0.009). After controlling for relevant confounding variables such as age, the modified Charlson Comorbidity Index, visceral metastases, and primary tumor type, patients with symptomatic VTE had a worse 1-year survival rate (VTE, 38%; 95% CI, 27-49 versus nonVTE, 47%; 95% CI, 42-51; p = 0.044). After controlling for relevant confounding variables, no association was found between wound complications and the use of chemoprophylaxis (odds ratio, 1.34; 95% CI, 0.62-2.90; p = 0.459). The overall proportion of patients who developed a wound complication was 10% (66 of 637), including 1.1% (seven of 637) spinal epidural hematomas. CONCLUSIONS: The risk of both symptomatic PE and fatal PE is high in this patient population, and those with symptomatic VTE were less likely to survive 1-year than those who did not, though this may reflect overall infirmity as much as anything else, because many of these patients did not die from VTE-related complications. Further study, such as randomized controlled trials with consistent postoperative VTE screening comparing different chemoprophylaxis regimens, are needed to identify better VTE prevention strategies. LEVEL OF EVIDENCE: Level III, therapeutic study.

Serum alkaline phosphatase and 30-day mortality after surgery for spinal metastatic disease.

BACKGROUND: Elevated serum alkaline phosphatase has been previously studied as a biomarker for progression of metastatic disease and implicated in adverse skeletal events and worsened survival. The purpose of this study was to determine if serum alkaline phosphatase was a predictor of short-term mortality of patients undergoing surgery for spinal metastatic disease. METHODS: The American College of Surgeons National Surgical Quality Improvement Program was queried for patients undergoing spinal surgery for metastatic disease. Bivariate and multivariable analyses was undertaken to determine the relationship between serum alkaline phosphatase and 30-day mortality. RESULTS: For the 1788 patients undergoing operative intervention for spinal metastatic disease between 2009 and 2016 the 30-day mortality was 8.49% (n = 151). In patients who survived beyond 30-days after surgery, n = 1627 (91.5%) the median [interquartile range] serum alkaline phosphatase levels were 126.4 [75-138], whereas in patients who had 30-day mortality, the serum alkaline phosphatase levels were 179.8 [114-187]. The optimal cut-off for alkaline phosphatase was determined to be 113 IU/L. On multivariable analysis, elevated serum alkaline phosphatase levels were associated with 30-day mortality (OR 1.61, 95% CI 1.12-2.32, p = 0.011). CONCLUSION: Elevated preoperative serum alkaline phosphatase is a marker for 30-day mortality in patients undergoing surgery for spinal metastatic disease. Future retrospective and prospective study designs should incorporate assessment of this serum biomarker to better understand the role for serum alkaline phosphatase in improving prognostication in spinal metastatic disease.

High Risk of Venous Thromboembolism After Surgery for Long Bone Metastases: A Retrospective Study of 682 Patients.

BACKGROUND: Previous studies have shown that venous thromboembolism (VTE) is a complication associated with neoplastic disease and major orthopaedic surgery. However, many potential risk factors remain undefined. QUESTIONS/PURPOSES: (1) What proportion of patients develop symptomatic VTE after surgery for long bone metastases? (2) What factors are associated with the development of symptomatic VTE among patients receiving surgery for long bone metastases? (3) Is there an association between the development of symptomatic VTE and 1-year survival among patients undergoing surgery for long bone metastases? (4) Does chemoprophylaxis increase the risk of wound complications among patients undergoing surgery for long bone metastases? METHODS: A retrospective study identified 682 patients undergoing surgical treatment of long bone metastases between 2002 and 2013 at the Massachusetts General Hospital and Brigham and Women's Hospital. We included patients 18 years of age or older who had a surgical procedure for impending or pathologic metastatic long bone fracture. We considered the humerus, radius, ulna, femur, tibia, and fibula as long bones; metastatic disease was defined as metastases from solid organs, multiple myeloma, or lymphoma. In general, we used 40 mg enoxaparin daily for lower extremity surgery and 325 mg aspirin daily for lower or upper extremity surgery. The primary outcome was a VTE defined as any symptomatic pulmonary embolism (PE) or symptomatic deep vein thrombosis (DVT; proximal and distal) within 90 days of surgery as determined by chart review. The tertiary outcome was defined as any documented wound complication that might be attributable to chemoprophylaxis within 90 days of surgery. At followup after 90 days and 1 year, respectively, 4% (25 of 682) and 8% (53 of 682) were lost to followup. Statistical analysis was performed using multivariable logistic and Cox regression and Kaplan-Meier. RESULTS: Overall, 6% (44 of 682) of patients had symptomatic VTE; 22 patients sustained a DVT, and 22 developed a PE. After controlling for relevant confounding variables, higher preoperative hemoglobin level was independently associated (odds ratio [OR], 0.75; 95% confidence interval [CI], 0.60-0.93; p = 0.011) with decreased symptomatic VTE risk, the presence of symptomatic VTE was associated with a worse 1-year survival rate (VTE: 27% [95% CI, 14%-40%] and non-VTE: 39% [95% CI, 35%-43%]; p = 0.041), and no association was found between wound complications and the use of chemoprophylaxis (OR, 3.29; 95% CI, 0.43-25.17; p = 0.252). CONCLUSIONS: The risk of symptomatic 90-day VTE is high in patients undergoing surgery for long bone metastases. Further study would be needed to determine the VTE prevention strategy that best balances risks and benefits to address this complication. LEVEL OF EVIDENCE: Level III, therapeutic study.

Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

BACKGROUND: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to learn from data. Studies using machine learning are potentially appealing, because of its possibility to explore complex patterns in data and to improve its models over time. QUESTIONS/PURPOSES: The purposes of this study were (1) to develop machine-learning algorithms for the prediction of 5-year survival in patients with chondrosarcoma; and (2) to deploy the best algorithm as an accessible web-based app for clinical use. METHODS: All patients with a microscopically confirmed diagnosis of conventional or dedifferentiated chondrosarcoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2010. SEER covers approximately 30% of the US population and consists of demographic, tumor characteristic, treatment, and outcome data. In total, 1554 patients met the inclusion criteria. Mean age at diagnosis was 52 years (SD 17), ranging from 7 to 102 years; 813 of the 1554 patients were men (55%); and mean tumor size was 8 cm (SD 6), ranging from 0.1 cm to 50 cm. Exact size was missing in 340 of 1544 patients (22%), grade in 88 of 1544 (6%), tumor extension in 41 of 1544 (3%), and race in 16 of 1544 (1%). Data for 1-, 3-, 5-, and 10-year overall survival were available for 1533 (99%), 1512 (98%), 1487 (96%), and 977 (63%) patients, respectively. One-year survival was 92%, 3-year survival was 82%, 5-year survival was 76%, and 10-year survival was 54%. Missing data were imputed using the nonparametric missForest method. Boosted decision tree, support vector machine, Bayes point machine, and neural network models were developed for 5-year survival. These models were chosen as a result of their capability of predicting two outcomes based on prior work on machine-learning models for binary classification. The models were assessed by discrimination, calibration, and overall performance. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0 with 1.0 being perfect discrimination and 0.5 that the model is no better than chance at making a prediction. The Brier score measures the squared difference between the predicted probability and the actual outcome. A Brier score of 0 indicates perfect prediction, whereas a Brier score of 1 indicates the poorest prediction. The Brier scores of the models are compared with the null model, which is calculated by assigning each patient a probability equal to the prevalence of the outcome. RESULTS: Four models for 5-year survival were developed with c-statistics ranging from 0.846 to 0.868 and Brier scores ranging from 0.117 to 0.135 with a null model Brier score of 0.182. The Bayes point machine was incorporated into a freely available web-based application. This application can be accessed through https://sorg-apps.shinyapps.io/chondrosarcoma/. CONCLUSIONS: Although caution is warranted, because the prediction model has not been validated yet, healthcare providers could use the online prediction tool in daily practice when survival prediction of patients with chondrosarcoma is desired. Future studies should seek to validate the developed prediction model. LEVEL OF EVIDENCE: Level III, prognostic study.

Donor-site morbidity after osteochondral autologous transplantation for osteochondritis dissecans of the capitellum: a systematic review and meta-analysis.

PURPOSE: To determine the rate of donor-site morbidity after osteochondral autologous transplantation (OATS) for capitellar osteochondritis dissecans. METHODS: A literature search was performed in PubMed/MEDLINE, Embase, and Cochrane Library to identify studies up to November 6, 2016. Criteria for inclusion were OATS for capitellar osteochondritis dissecans, reported outcomes related to donor sites, ≥10 patients, ≥1 year follow-up, and written in English. Donor-site morbidity was defined as persistent symptoms (≥1 year) or cases that required subsequent intervention. Patient and harvest characteristics were described, as well as the rate of donor-site morbidity. A random effects model was used to calculate and compare weighted group proportions. RESULTS: Eleven studies including 190 patients were included. In eight studies, grafts were harvested from the femoral condyle, in three studies, from either the 5th or 6th costal-osteochondral junction. The average number of grafts was 2 (1-5); graft diameter ranged from 2.6 to 11 mm. In the knee-to-elbow group, donor-site morbidity was reported in 10 of 128 patients (7.8%), knee pain during activity (7.0%) and locking sensations (0.8%). In the rib-to-elbow group, one of 62 cases (1.6%) was complicated, a pneumothorax. The proportion in the knee-to-elbow group was 0.04 (95% CI 0.0-0.15), and the proportion in the rib-to-elbow group was 0.01 (95% CI 0.00-0.06). There were no significant differences between both harvest techniques (n.s.). CONCLUSIONS: Donor-site morbidity after OATS for capitellar osteochondritis dissecans was reported in a considerable group of patients. LEVEL OF EVIDENCE: Level IV, systematic review of level IV studies.

Reoperation After Combined Injury of the Index Finger: Repair Versus Immediate Amputation.

PURPOSE: To identify factors associated with unplanned reoperation of severely injured index fingers and to address the number of amputations after initial repair. METHODS: In this retrospective study, we included all patients older than 18 years of age who had repair or immediate amputation for combined index finger injury at 2 level I trauma centers and 1 community hospital tied to a level I trauma center between January 2004 and February 2014. Twelve patients were excluded because of inadequate follow-up. Bivariate and multivariable analyses sought factors associated with unplanned reoperation after repair and immediate amputation. RESULTS: Among 114 patients with combined injury, 75 were treated with repair and 39 with immediate amputation. A total of 41 patients had an unplanned reoperation, 33 after repair (44%) and 8 after immediate amputation (21%). In multivariable analysis, patients who had a reoperation for fingers other than the index finger were at risk for unplanned reoperation after repair. Women were more likely to have an unplanned reoperation than men, and patients who had a ray amputation were at risk for unplanned reoperation after immediate amputation. Six patients (18%) had amputation after initial repair. CONCLUSIONS: Surgeons may counsel patients that they are twice as likely to have an unplanned reoperation after a repair for combined injury of the index finger compared with an immediate amputation. Unplanned reoperations were more common among patients with injuries involving multiple fingers. Effective shared decision making is particularly important in this setting given that 1 in 5 repaired index fingers were eventually amputated. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic IV.

Social determinants of health in prognostic machine learning models for orthopaedic outcomes: A systematic review.

RATIONAL: Social determinants of health (SDOH) are being considered more frequently when providing orthopaedic care due to their impact on treatment outcomes. Simultaneously, prognostic machine learning (ML) models that facilitate clinical decision making have become popular tools in the field of orthopaedic surgery. When ML-driven tools are developed, it is important that the perpetuation of potential disparities is minimized. One approach is to consider SDOH during model development. To date, it remains unclear whether and how existing prognostic ML models for orthopaedic outcomes consider SDOH variables. OBJECTIVE: To investigate whether prognostic ML models for orthopaedic surgery outcomes account for SDOH, and to what extent SDOH variables are included in the final models. METHODS: A systematic search was conducted in PubMed, Embase and Cochrane for studies published up to 17 November 2020. Two reviewers independently extracted SDOH features using the PROGRESS+ framework (place of residence, race/ethnicity, Occupation, gender/sex, religion, education, social capital, socioeconomic status, 'Plus+' age, disability, and sexual orientation). RESULTS: The search yielded 7138 studies, of which 59 met the inclusion criteria. Across all studies, 96% (57/59) considered at least one PROGRESS+ factor during development. The most common factors were age (95%; 56/59) and gender/sex (96%; 57/59). Differential effect analyses, such as subgroup analysis, covariate adjustment, and baseline comparison, were rarely reported (10%; 6/59). The majority of models included age (92%; 54/59) and gender/sex (69%; 41/59) as final input variables. However, factors such as insurance status (7%; 4/59), marital status (7%; 4/59) and income (3%; 2/59) were seldom included. CONCLUSION: The current level of reporting and consideration of SDOH during the development of prognostic ML models for orthopaedic outcomes is limited. Healthcare providers should be critical of the models they consider using and knowledgeable regarding the quality of model development, such as adherence to recognized methodological standards. Future efforts should aim to avoid bias and disparities when developing ML-driven applications for orthopaedics.