Risk Factors for Adjacent Vertebral Fractures Following Cement Vertebroplasty: The Clinical Significance of Multiple Preexisting Vertebral Compression Fractures.

Study Design: A retrospective cohort study.

Objective: The study retrospectively analyzed the factors associated with the development of adjacent vertebral fractures.

Summary Of Background Data: Adjacent vertebral fractures (AVF) may occur following cement vertebroplasty, and several risk factors have been reported with controversies.

Clinical, oncological, and prognostic differences of patients with subsequent skeletal-related events in bone metastases.

Aims: Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE.

Validation of Ten Osteoporosis Screening Tools in Rural Communities of Taiwan.

Patients with osteoporosis are at risk of fractures, which can lead to immobility and reduced quality of life. Early diagnosis and treatment are crucial for preventing fractures, but many patients are not diagnosed until after a fracture has occurred. This study aimed to evaluate the performance of 10 osteoporosis screening tools (OSTs) in rural communities of Taiwan.

Comparison of Classically and Machine Learning Generated Survival Prediction Models for Patients With Spinal Metastasis - A meta-Analysis of Two Recently Developed Algorithms.

Study Design: A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study.

Objective: (1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.

Risk Factors of Failed Conservative Treatment for Adjacent Vertebral Fractures Following Percutaneous Vertebroplasty.

Study Design: A retrospective, single-center, observational study.

Objective: This study investigated the risk factors associated with the failure of conservative treatment for adjacent vertebral fractures (AVFs).

Summary Of Background Data: Adjacent vertebral fractures following vertebroplasty for osteoporotic vertebral compression fractures are not uncommon.

Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?

Background: Bone metastasis in advanced cancer is challenging because of pain, functional issues, and reduced life expectancy. Treatment planning is complex, with consideration of factors such as location, symptoms, and prognosis. Prognostic models help guide treatment choices, with Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) showing promise in predicting survival for initial spinal metastases and extremity metastases treated with surgery or radiotherapy.

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long-bone metastases.

Background: Predictive analytics is gaining popularity as an aid to treatment planning for patients with bone metastases, whose expected survival should be considered. Decreased psoas muscle area (PMA), a morphometric indicator of suboptimal nutritional status, has been associated with mortality in various cancers, but never been integrated into current survival prediction algorithms (SPA) for patients with skeletal metastases. This study investigates whether decreased PMA predicts worse survival in patients with extremity metastases and whether incorporating PMA into three modern SPAs (PATHFx, SORG-NG, and SORG-MLA) improves their performance.

The Outcome of under 10 mm Single-Incision Surgery Using a Non-Specialized Volar Plate in Distal Radius Fractures: A Retrospective Comparative Study.

Background: The distal radius fracture is a common orthopedic injury. We aimed to share the surgical steps and investigate the outcomes of treating distal radius fractures with wounds ≤10 mm using a globally accessible locking plate.

Methods: We collected 46 patients who underwent surgery via a <10 mm wound, with a control group consisting of 40 patients who underwent conventional procedures.

Determining patients with spinal metastases suitable for surgical intervention: A cost-effective analysis.

Background: Both nonoperative and operative treatments for spinal metastasis are expensive interventions. Patients' expected 3-month survival is believed to be a key factor to determine the most suitable treatment. However, to the best of our knowledge, no previous study lends support to the hypothesis.

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.

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.

Comparison of eight modern preoperative scoring systems for survival prediction in patients with extremity metastasis.

Background: Survival is an important factor to consider when clinicians make treatment decisions for patients with skeletal metastasis. Several preoperative scoring systems (PSSs) have been developed to aid in survival prediction. Although we previously validated the Skeletal Oncology Research Group Machine-learning Algorithm (SORG-MLA) in Taiwanese patients of Han Chinese descent, the performance of other existing PSSs remains largely unknown outside their respective development cohorts.

Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm.

Background: The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA) was developed to predict the survival of patients with spinal metastasis. The algorithm was successfully tested in five international institutions using 1101 patients from different continents. The incorporation of 18 prognostic factors strengthens its predictive ability but limits its clinical utility because some prognostic factors might not be clinically available when a clinician wishes to make a prediction.

A Machine Learning Algorithm for Predicting 6-Week Survival in Spinal Metastasis: An External Validation Study Using 2,768 Taiwanese Patients.

Introduction: There are predictive algorithms for predicting 3-month and 1-year survival in patients with spinal metastasis. However, advance in surgical technique, immunotherapy, and advanced radiation therapy has enabled shortening of postoperative recovery, which returns dividends to the overall quality-adjusted life-year. As such, the Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was proposed to predict 6-week survival in patients with spinal metastasis, whereas its utility for patients treated with nonsurgical treatment was untested externally.

Indications for and Outcomes of Three Unilateral Biportal Endoscopic Approaches for the Decompression of Degenerative Lumbar Spinal Stenosis: A Systematic Review.

Objective: In this systematic review, we summarized the indications for and outcomes of three main unilateral biportal endoscopic (UBE) approaches for the decompression of degenerative lumbar spinal stenosis (DLSS).

Methods: A comprehensive search of the literature was performed using Ovid Embase, PubMed, Web of Science, and Ovid's Cochrane Library. The following information was collected: surgical data; patients' scores on the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and Macnab criteria; and surgical complications.

Optimization of Tokuhashi Scoring System to Improve Survival Prediction in Patients with Spinal Metastases.

Introduction: Predicting survival time for patients with spinal metastases is important in treatment choice. Generally speaking, six months is a landmark cutoff point. Revised Tokuhashi score (RTS), the most widely used scoring system, lost its accuracy in predicting 6-month survival, gradually.

Multilayer Electrospun-Aligned Fibroin/Gelatin Implant for Annulus Fibrosus Repair: An In Vitro and In Vivo Evaluation.

Annulus fibrosus (AF) damage is proven to prompt intervertebral disc (IVD) degeneration, and unrepaired AF lesions after surgical discectomy may boost herniation of the nucleus pulposus (NP) which may lead to further compression of neural structures. Moreover, vascular and neural ingrowth may occur within the defect which is known as a possible reason for discogenic pain. Due to a limited healing capacity, an effective strategy to repair and close the AF defect is necessary.

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

Background And Purpose: Predicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis.

Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy.

Background And Purpose: Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM).

Materials And Methods: From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected.

Simultaneous Hypercorrection of Lowest Instrumented Vertebral Tilt and Main Thoracic Curve is Associated With Progression of Residual Lumbar Curve in Adolescent Idiopathic Scoliosis.

Study Design: A retrospective cohort study.

Objective: To determine radiographic parameters, including the lowest instrumented vertebral (LIV) tilt, related to the postoperative magnitude and progression of residual lumbar curves (LCs) in adolescent idiopathic scoliosis patients who underwent posterior spinal fusion with LIV at or above L1.

Summary Of Background Data: Although several guidelines have been proposed for thoracic curve fusion, factors related to the postoperative magnitude and potential progression of unfused LCs remained undetermined.

Development and external validation of predictive algorithms for six-week mortality in spinal metastasis using 4,304 patients from five institutions.

Background Context: Historically, spine surgeons used expected postoperative survival of 3-months to help select candidates for operative intervention in spinal metastasis. However, this cutoff has been challenged by the development of minimally invasive techniques, novel biologics, and advanced radiotherapy. Recent studies have suggested that a life expectancy of 6 weeks may be enough to achieve significant improvements in postoperative health-related quality of life.