Explore the vast range of titles available.

Abstract BACKGROUND Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality. OBJECTIVE To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. METHODS Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality. RESULTS Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/ CONCLUSION Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.

Abstract BACKGROUND Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care. OBJECTIVE To (1) develop machine learning algorithms for prediction of short-term mortality and (2) deploy these models in an open access web application. METHODS The American College of Surgeons, National Surgical Quality Improvement Program was used to identify patients that underwent operative intervention for metastatic disease. Four machine learning algorithms were developed, and the algorithm with the best performance across discrimination, calibration, and overall performance was integrated into an open access web application. RESULTS The 30-d mortality for the 1790 patients undergoing surgery for spinal metastatic disease was 8.49%. Preoperative factors used for prognostication were albumin, functional status, white blood cell count, hematocrit, alkaline phosphatase, spinal location (cervical, thoracic, lumbosacral), and severity of comorbid systemic disease (American Society of Anesthesiologist Class). In this population, machine learning algorithms developed to predict 30-d mortality performed well on discrimination (c-statistic), calibration (assessed by calibration slope and intercept), Brier score, and decision analysis. An open access web application was developed for the best performing model and this web application can be found here: https://sorg-apps.shinyapps.io/spinemets/. CONCLUSION Machine learning algorithms are promising for prediction of postoperative outcomes in spinal oncology and these algorithms can be integrated into clinically useful decision tools. As the volume of data in oncology continues to grow, creation of learning systems and deployment of these systems as accessible tools may significantly enhance prognostication and management.

Chordoma is a rare malignant tumor of bone with high morbidity and mortality. Recently, aggressive pediatric poorly differentiated chordoma with SMARCB1 loss has been described. This study summarizes the clinicopathologic features of poorly differentiated chordoma with SMARCB1 loss in the largest series to date. A search of records between 1990–2017 at MGH identified 19 patients with poorly differentiated chordoma. Immunohistochemical stains were evaluated. Kaplan–Meier survival statistics and log-rank (Mantel Cox) tests compared survival with other subtypes. The patients ( n  = 19) were diagnosed at a median age of 11 years (range: 1–29). Tumors arose in the skull base and clivus ( n  = 10/19; 53%); cervical spine ( n  = 6/19; 32%); and sacrum or coccyx ( n  = 3/19; 16%). The clinical stage of these patients (AJCC 7e) was stage 2A ( n  = 7/16; 44%); stage 2B (n = 6/16; 38%); stage 4A ( n  = 1/16; 6%); and stage 4B ( n  = 2/16; 13%). The tumors were composed of sheets of epithelioid cells with nuclear pleomorphism, abundant eosinophilic cytoplasm, and increased mitoses. Tumors were positive for cytokeratin ( n  = 18/18; 100%) and brachyury ( n  = 18/18; 100%). Patients were treated with a combination of excision, radiation therapy, and chemotherapy. No difference in overall survival, progression free survival, local control time, and metastasis free survival was identified between poorly differentiated chordoma of the skull base and of the spine. Compared to other chordoma subtypes, poorly differentiated chordoma has a significantly decreased mean overall survival after stratification by site ( p  = 0.037). Pediatric poorly differentiated chordoma has a distinct clinical and immunohistochemical profile, with characteristic SMARCB1 loss and decreased survival compared to conventional/chondroid chordoma. Recognition of this subtype is important because these malignancies should be treated aggressively with multimodality therapy.

Malignant transformation of cells is frequently associated with defective HLA class I antigen processing machinery (APM) component expression. This abnormality may have functional relevance, since it may have a negative impact on tumor cell recognition by cognate T cells. Furthermore, HLA class I APM abnormalities appear to have clinical significance, since they are associated with poor prognosis in several malignant diseases and may play a role in the resistance to immune checkpoint inhibitor-based immunotherapy. In this paper, we have reviewed the literature describing abnormalities in HLA class I APM component expression in many types of cancer. These abnormalities have been reported in all types of cancer analyzed with a frequency ranging between a minimum of 35.8% in renal cancer and a maximum of 87.9% in thyroid cancer for HLA class I heavy chains. In addition, we have described the molecular mechanisms underlying defects in HLA class I APM component expression and function by malignant cells. Lastly, we have discussed the clinical significance of HLA class I APM component abnormalities in malignant tumors.

Background Orthopaedic surgical site infections (SSIs) can delay recovery, add impairments, and decrease quality of life, particularly in patients undergoing spine surgery, in whom SSIs may also be more common. Efforts to prevent and treat SSIs of the spine rely on the identification and registration of these adverse events in large databases. The effective use of these databases to answer clinical questions depends on how the conditions in question, such as infection, are defined in the databases queried, but the degree to which different definitions of infection might cause different risk factors to be identified by those databases has not been evaluated. Questions/purposes The purpose of this study was to determine whether different definitions of SSI identify different risk factors for SSI. Specifically, we compared the International Classification of Diseases, 9th Revision (ICD-9) coding, Centers for Disease Control and Prevention (CDC) criteria for deep infection, and incision and débridement for infection to determine if each is associated with distinct risk factors for SSI. Methods In this single-center retrospective study, a sample of 5761 adult patients who had an orthopaedic spine surgery between January 2003 and August 2013 were identified from our institutional database. The mean age of the patients was 56 years (± 16 SD), and slightly more than half were men. We applied three different definitions of infection: ICD-9 code for SSI, the CDC criteria for deep infection, and incision and débridement for infection. Three hundred sixty-one (6%) of the 5761 surgeries received an ICD-9 code for SSI within 90 days of surgery. After review of the medical records of these 361 patients, 216 (4%) met the CDC criteria for deep SSI, and 189 (3%) were taken to the operating room for irrigation and débridement within 180 days of the day of surgery. Results We found the Charlson Comorbidity Index, the duration of the operation, obesity, and posterior surgical approach were independently associated with a higher risk of infection for each of the three definitions of SSI. The influence of malnutrition, smoking, specific procedures, and specific surgeons varied by definition of infection. These elements accounted for approximately 6% of the variability in the risk of developing an infection. Conclusions The frequency of SSI after spine surgery varied according to the definition of an infection, but the most important risk factors did not. We conclude that large database studies may be better suited for identifying risk factors than for determining absolute numbers of infections. Level of Evidence Level III, prognostic study. See Guidelines for Authors for a complete description of levels of evidence.

Background Tumors of the appendicular skeleton commonly affect the proximal humerus, but there is no consensus regarding the best reconstructive technique after proximal humerus resection for tumors of the shoulder. Questions/purposes We wished to perform a systematic review to determine which surgical reconstruction offers the (1) best functional outcome as measured by the Musculoskeletal Tumor Society (MSTS) score, (2) longest construct survival, and (3) lowest complication rate after proximal humerus resection for malignant or aggressive benign tumors of the shoulder. Methods We searched the literature up to June 1, 2013, from MEDLINE, EMBASE, and the Cochrane Library. Only studies reporting results in English, Dutch, or German and with followups of 80% or more of the patients at a minimum of 2 years were included. Twenty-nine studies with 693 patients met our criteria, seven studies (24%) were level of evidence III and the remainder were level IV. Studies reported on reconstruction with prostheses (n = 17), osteoarticular allografts (n = 10), and allograft-prosthesis composites (n = 11). Owing to substantial heterogeneity and bias, we narratively report our results. Results Functional scores in prosthesis studies ranged from 61% to 77% (10 studies, 141 patients), from 50% to 78% (eight studies, 84 patients) in osteoarticular graft studies, and from 57% to 91% (10 studies, 141 patients) in allograft-prosthesis composite studies. Implant survival ranged from 0.38 to 1.0 in the prosthesis group (341 patients), 0.33 to 1.0 in the osteoarticular allograft group (143 patients), and 0.33 to 1.0 in allograft-prosthesis group (132 patients). Overall complications per patient varied between 0.045 and 0.85 in the prosthesis group, 0 and 1.5 in the osteoarticular graft group, and 0.19 and 0.79 in the prosthesis-composite graft group. We observed a higher fracture rate for osteoarticular allografts, but other specific complication rates were similar. Conclusions Owing to the limitations of our systematic review, we found that allograft-prosthesis composites and prostheses seem to have similar functional outcome and survival rates, and both seem to avoid fractures that are observed with osteoarticular allografts. Further collaboration in the field of surgical oncology, using randomized controlled trials, is required to establish the superiority of any particular treatment.

Mechanical failure of medical implants, especially in orthopedic poses a significant burden to the patients and healthcare system. The majority of the implant failures are diagnosed at very late stages and are of mechanical causes. This makes the diagnosis and screening of implant failure very challenging. There have been several attempts for development of new implants and screening methods to address this issue; however, the majority of these methods focus on development of new implants or material and cannot satisfy the needs of the patients that have already been operated on. In this work we are introducing a novel screening method and investigate the feasibility of using low-intensity, low-frequency ultrasound acoustic waves for understanding of interfacial implant defects through computational simulation. In this method, we simultaneously apply and sense acoustic waves. COMSOL simulations proved the correlation between implant health condition, severity, and location of defects with measured acoustic signal. Moreover, we show that machine learning not only can detect and classify failure types, it can also assess the severity of the defects. We believe that this work can be used as a proof of concept to rationalize the development of non-invasive screening acoustic wearables for early detection of implant failure in patients with orthopedic implants.

Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. Results The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good discrimination (c-statistic = .79), almost perfect calibration (intercept = −.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/. Discussion & Conclusions We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians’ ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow.

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.