The recent impressive clinical responses to antibody-based immunotherapy have prompted the identification of clinically relevant tumor antigens that can serve as targets in solid tumors. Among them, ...B7-H3, a member of the B7 ligand family, represents an attractive target for antibody-based immunotherapy, it is overexpressed on differentiated malignant cells and cancer-initiating cells, with limited heterogeneity, and high frequency (60% of 25,000 tumor samples) in many different cancer types, but has a limited expression at low level in normal tissues. In nonmalignant tissues, B7-H3 has a predominantly inhibitory role in adaptive immunity, suppressing T-cell activation and proliferation. In malignant tissues, B7-H3 inhibits tumor antigen-specific immune responses, leading to a protumorigenic effect. B7-H3 also has nonimmunologic protumorigenic functions, such as promoting migration and invasion, angiogenesis, chemoresistance, and endothelial-to-mesenchymal transition, as well as affecting tumor cell metabolism. As a result, B7-H3 expression in tumors is associated with poor prognosis. Although experimental B7-H3 silencing reduces cancer cell malignant potential, there has been limited emphasis on the development of B7-H3-blocking antibodies, most likely because the B7-H3 receptor remains unknown. Instead, many antibody-based strategies utilizing distinct effector mechanisms to target B7-H3-expressing cancer cells have been developed. These strategies have demonstrated potent antitumor activity and acceptable safety profiles in preclinical models. Ongoing clinical trials are assessing their safety and efficacy in patients. Identification of the B7-H3 receptor will improve our understanding of its role in tumor immunity, and will suggest rational strategies to develop blocking antibodies, which may enhance the therapeutic efficacy of tumor immunity.
The field of artificial intelligence (AI) is rapidly advancing, especially with recent improvements in deep learning (DL) techniques. Augmented (AR) and virtual reality (VR) are finding their place ...in healthcare, and spine surgery is no exception. The unique capabilities and advantages of AR and VR devices include their low cost, flexible integration with other technologies, user-friendly features and their application in navigation systems, which makes them beneficial across different aspects of spine surgery. Despite the use of AR for pedicle screw placement, targeted cervical foraminotomy, bone biopsy, osteotomy planning, and percutaneous intervention, the current applications of AR and VR in spine surgery remain limited.
The primary goal of this study was to provide the spine surgeons and clinical researchers with the general information about the current applications, future potentials, and accessibility of AR and VR systems in spine surgery.
We reviewed titles of more than 250 journal papers from google scholar and PubMed with search words: augmented reality, virtual reality, spine surgery, and orthopaedic, out of which 89 related papers were selected for abstract review. Finally, full text of 67 papers were analyzed and reviewed.
The papers were divided into four groups: technological papers, applications in surgery, applications in spine education and training, and general application in orthopaedic. A team of two reviewers performed paper reviews and a thorough web search to ensure the most updated state of the art in each of four group is captured in the review.
In this review we discuss the current state of the art in AR and VR hardware, their preoperative applications and surgical applications in spine surgery. Finally, we discuss the future potentials of AR and VR and their integration with AI, robotic surgery, gaming, and wearables.
AR and VR are promising technologies that will soon become part of standard of care in spine surgery.
Health Literacy in Orthopaedics Lans, Amanda; Schwab, Joseph H
Journal of the American Academy of Orthopaedic Surgeons,
2023-Apr-15, Letnik:
31, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Health literacy is a dynamic, multifaceted skill set that relies on patients, healthcare providers, and the healthcare system. In addition, health literacy assessment provides an avenue for ...evaluating patient understanding and offers insights into their health management capabilities. Inadequate health literacy results in poor patient outcomes and compromised care by considerably hindering successful communication and comprehension of relevant health information between the patient and the provider. In this narrative review, we explore why limited health literacy poses serious implications for orthopaedic patient health and safety, expectations, treatment outcomes, and healthcare costs. Furthermore, we elaborate on the complexity of health literacy, provide an overview of key concepts, and offer recommendations for clinical practice and research investigations.
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.
Postoperative recovery after total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study is ...to develop machine learning algorithms for preoperative prediction of prolonged opioid prescriptions after THA.
A retrospective review of electronic health records was conducted at 2 academic medical centers and 3 community hospitals to identify adult patients who underwent THA for osteoarthritis between January 1, 2000 and August 1, 2018. Prolonged postoperative opioid prescriptions were defined as continuous opioid prescriptions after surgery to at least 90 days after surgery. Five machine learning algorithms were developed to predict this outcome and were assessed by discrimination, calibration, and decision curve analysis.
Overall, 5507 patients underwent THA, of which 345 (6.3%) had prolonged postoperative opioid prescriptions. The factors determined for prediction of prolonged postoperative opioid prescriptions were age, duration of opioid exposure, preoperative hemoglobin, and preoperative medications (antidepressants, benzodiazepines, nonsteroidal anti-inflammatory drugs, and beta-2-agonists). The elastic-net penalized logistic regression model achieved the best performance across discrimination (c-statistic = 0.77), calibration, and decision curve analysis. This model was incorporated into a digital application able to provide both predictions and explanations (available at https://sorg-apps.shinyapps.io/thaopioid/).
If externally validated in independent populations, the algorithms developed in this study could improve preoperative screening and support for THA patients at high risk for prolonged postoperative opioid prescriptions. Early identification and intervention in high-risk cases may mitigate the long-term adverse consequence of opioid dependence.
III.
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.
An infection of the spinal epidural space, spinal epidural abscess (SEA) is a potentially devastating entity that is rising in incidence. Its insidious presentation, variable progression, and ...potential for precipitous neurologic decline make diagnosis and management of SEA challenging. Prompt diagnosis is key because treatment delay can lead to paralysis or death. Owing to the nonspecific symptoms and signs of SEA, misdiagnosis is alarmingly common. Risk factor assessment to determine the need for definitive MRI reduces diagnostic delays compared with relying on clinical or laboratory findings alone. Although decompression has long been considered the benchmark for SEA, considerable risk associated with spinal surgery is noted in an older cohort with multiple comorbidities. Nonoperative management may represent an alternative in select cases. Failure of nonoperative management is a feared outcome associated with motor deterioration and poor clinical outcomes. Recent studies have identified independent predictors of failure and residual neurologic dysfunction, recurrence, and mortality. Importantly, these studies provide tools that generate probabilities of these outcomes. Future directions of investigation should include external validation of existing algorithms through multi-institutional collaboration, prospective trials, and incorporation of powerful predictive statistics such as machine learning methods.
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.
Giant cell tumor of bone Raskin, Kevin A; Schwab, Joseph H; Mankin, Henry J ...
Journal of the American Academy of Orthopaedic Surgeons
21, Številka:
2
Journal Article
Recenzirano
Giant cell tumor (GCT) of bone is one type of giant cell-rich lesion of bone. This benign mesenchymal tumor has characteristic multinuclear giant cells. Mononuclear stromal cells are the ...physiologically active and diagnostic cell type. Most GCTs are located in the epiphyseal regions of long bones. The axial skeleton-primarily the sacrum-is a secondary site of involvement. Most patients present with pain, swelling, joint effusion, and disability in the third and fourth decades of life. Imaging studies are important for tumor staging and radiographic grading. Typically, these clinically active but slow-growing tumors are confined to bone, with relatively well-defined radiographic borders. Monostotic disease is most common. Metastatic spread to the lungs is rare. Extended intralesional curettage with or without adjuvant therapy is the primary treatment choice. Local recurrence is seen in ≤ 20% of cases, and a second local intralesional procedure is typically sufficient in cases that are detected early. Medical therapies include diphosphonates and denosumab. Denosumab has been approved for use in osteoporosis as well as breast and prostate cancer metastatic to bone. Medical therapy and radiotherapy can alter the management of GCT of bone, especially in multifocal disease, local recurrences, and bulky central/axial disease.
PDF
