Pilot Study: Short Term Impact of Radiation Therapy on Bone Mineral Density and Bone Metabolism.

Despite the risk of complications, high dose radiation therapy is increasingly utilized in the management of selected bone malignancies. In this study, we investigate the impact of moderate to high dose radiation (over 50 Gy) on bone metabolism and structure. Between 2015 and 2018, patients with a primary malignant bone tumor of the sacrum that were either treated with high dose definitive radiation only or a combination of moderate to high dose radiation and surgery were prospectively enrolled at a single institution.

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.

Serum alkaline phosphatase is a prognostic marker in bone metastatic disease of the extremity.

Purpose: The purpose of this study was to determine the prognostic value of serum alkaline phosphatase for treatment decision making in metastatic bone disease.

Methods: 1090 patients who underwent surgery for extremity metastatic disease were retrospectively identified at two tertiary care centers. The association between alkaline phosphatase and mortality was assessed by bivariate and multivariate analyses.

Thirty-day Postoperative Complications After Surgery For Metastatic Long Bone Disease Are Associated With Higher Mortality at 1 Year.

Background: The benefits of surgical treatment of a metastasis of the extremities may be offset by drawbacks such as potential postoperative complications. For this group of patients, the primary goal of surgery is to improve quality of life in a palliative setting. A better comprehension of factors associated with complications and the impact of postoperative complications on mortality may prevent negative outcomes and help surgeons in surgical decision-making.

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.

Discharge Disposition After Anterior Cervical Discectomy and Fusion.

Objective: Age and comorbidity burden of patients going anterior cervical discectomy and fusion (ACDF) have increased significantly over the past 2 decades, resulting in increased expenditures. Non-home discharge after ACDF contributes to increased direct and indirect costs of postoperative care. The purpose of this study was to identify independent prognostic factors for discharge disposition in patients undergoing ACDF.

External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.

Background Context: Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were previously developed in a single institutional sample but remain to be externally validated.

Purpose: The purpose of this study was to externally validate these algorithms in an independent population from another institution.

Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation.

Background Context: Spine surgery has been identified as a risk factor for prolonged postoperative opioid use. Preoperative prediction of opioid use could improve risk stratification, shared decision-making, and patient counseling before surgery.

Purpose: The primary purpose of this study was to develop algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation.

Physical function and pain intensity in patients with metastatic bone disease.

Background: Patient reported outcome data in bone metastatic disease are scarce and it would be useful to have normative data and understand what patients are at risk for poor function and more pain.

Objectives: We aimed to assess what factors are independently associated with physical function and pain intensity in patients with bone metastasis.

Methods: We included data from 211 patients with bone metastasis who completed a survey (2014-2016) including the PROMIS Physical Function Cancer and PROMIS Pain Intensity questionnaires.

Does the SORG Algorithm Predict 5-year Survival in Patients with Chondrosarcoma? An External Validation.

Background: We developed a machine learning algorithm to predict the survival of patients with chondrosarcoma. The algorithm demonstrated excellent discrimination and calibration on internal validation in a derivation cohort based on data from the Surveillance, Epidemiology, and End Results (SEER) registry. However, the algorithm has not been validated in an independent external dataset.

Surgical outcomes of patients with diffuse-type tenosynovial giant-cell tumours: an international, retrospective, cohort study.

Background: Diffuse-type tenosynovial giant-cell tumour is a rare, locally aggressive, and difficult-to-treat soft tissue tumour. Clinical and surgical outcomes depend on multiple factors, including preoperative diagnostic assessment, the localisation and extent of disease, and possibly the choice of treatment modalities by orthopaedic surgeons. We did a retrospective cohort study to characterise global surgical treatment protocols, and assess surgical outcomes, complications, and functional results in patients with diffuse-type tenosynovial giant-cell tumours.

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.

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.

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation.

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.

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.

Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion.

Background Context: The severity of the opioid epidemic has increased scrutiny of opioid prescribing practices. Spine surgery is a high-risk episode for sustained postoperative opioid prescription.

Purpose: To develop machine learning algorithms for preoperative prediction of sustained opioid prescription after anterior cervical discectomy and fusion (ACDF).

Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.

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.

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders.

OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e.

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.

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.

Prognostic role of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with bone metastases.

Background: Skeletal metastases are a common problem in patients with cancer, and surgical decision making depends on multiple factors including life expectancy. Identification of new prognostic factors can improve survival estimation and guide healthcare providers in surgical decision making. In this study, we aim to determine the prognostic value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in patients with bone metastasis.

Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival.

Background: Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learning studies in the chordoma literature. The purpose of this study was to develop machine learning models for survival prediction and deploy them as open access web applications as a proof of concept for machine learning in rare nervous system lesions.