Temporal validation of the SORG 90-Day and 1-Year machine learning algorithms for survival of patients with spinal metastatic disease.

Purpose: The SORG-MLA was developed to predict 90-day and 1-year postoperative survival in patients with spinal metastatic disease who underwent surgery between 2000 and 2016. Due to the constant changes in treatment methods, it is essential to perform temporal validation with a recent patient population. Therefore, the purpose of this study was to validate the Skeletal Oncology Research Group machine learning algorithms (SORG-MLA) using a contemporary patient cohort.

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.

Survival in Patients With Spinal Metastatic Disease Treated Nonoperatively With Radiotherapy: Are the SORG-ML Algorithms Relevant?

Summary Of Background Data: The SORG-ML algorithms for survival in spinal metastatic disease were developed in patients who underwent surgery and were externally validated for patients managed operatively.

Objective: To externally validate the SORG-ML algorithms for survival in spinal metastatic disease in patients managed nonoperatively with radiation.

Study Design: Retrospective cohort.

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.

Clockwise torque results in higher reoperation rates in left-sided femur fractures.

Purpose: Effects of clockwise torque rotation onto proximal femoral fracture fixation have been subject of ongoing debate: fixated right-sided trochanteric fractures seem more rotationally stable than left-sided fractures in the biomechanical setting, but this theoretical advantage has not been demonstrated in the clinical setting to date. The purpose of this study was to identify a difference in early reoperation rate between patients undergoing surgery for left- versus right-sided proximal femur fractures using cephalomedullary nailing (CMN).

Materials And Methods: The American College of Surgeons National Surgical Quality Improvement Program was queried from 2016-2019 to identify patients aged 50 years and older undergoing CMN for a proximal femoral fracture.

Intercontinental validation of a clinical prediction model for predicting 90-day and 2-year mortality in an Israeli cohort of 2033 patients with a femoral neck fracture aged 65 or above.

Purpose: Mortality prediction in elderly femoral neck fracture patients is valuable in treatment decision-making. A previously developed and internally validated clinical prediction model shows promise in identifying patients at risk of 90-day and 2-year mortality. Validation in an independent cohort is required to assess the generalizability; especially in geographically distinct regions.

External validation of a predictive algorithm for in-hospital and 90-day mortality after spinal epidural abscess.

Background Context: Mortality in patients with spinal epidural abscess (SEA) remains high. Accurate prediction of patient-specific prognosis in SEA can improve patient counseling as well as guide management decisions. There are no externally validated studies predicting short-term mortality in patients with SEA.

Utilization of Machine Learning to Model Important Features of 30-day Readmissions following Surgery for Metastatic Spinal Column Tumors: The Influence of Frailty.

Study Design: Retrospective cohort study.

Objective: The aim of this study was to determine the relative importance and predicative power of the Hospital Frailty Risk Score (HFRS) on unplanned 30-day readmission after surgical intervention for metastatic spinal column tumors.

Methods: All adult patients undergoing surgery for metastatic spinal column tumor were identified in the Nationwide Readmission Database from the years 2016 to 2018.

Are Detailed, Patient-level Social Determinant of Health Factors Associated With Physical Function and Mental Health at Presentation Among New Patients With Orthopaedic Conditions?

Background: It is well documented that routinely collected patient sociodemographic characteristics (such as race and insurance type) and geography-based social determinants of health (SDoH) measures (for example, the Area Deprivation Index) are associated with health disparities, including symptom severity at presentation. However, the association of patient-level SDoH factors (such as housing status) on musculoskeletal health disparities is not as well documented. Such insight might help with the development of more-targeted interventions to help address health disparities in orthopaedic surgery.

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.

Sociodemographic Factors Are Associated with Patient-Reported Outcome Measure Completion in Orthopaedic Surgery: An Analysis of Completion Rates and Determinants Among New Patients.

Unlabelled: Patient-reported outcome measures (PROMs) and, specifically, the Patient-Reported Outcomes Measurement Information System (PROMIS), are increasingly utilized for clinical research, clinical care, and health-care policy. However, completion of these outcome measures can be inconsistent and challenging. We hypothesized that sociodemographic variables are associated with the completion of PROM questionnaires.

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.

Predictors of reoperation after surgery for spinal epidural abscess.

Background Context: Spinal epidural abscess is a rare but severe condition with high rates of postoperative adverse events.

Purpose: The objective of the study was to identify independent prognostic factors for reoperation using two datasets: an institutional and national database.

Study Design/setting: Retrospective Review.

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Purpose: Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Does the SORG Orthopaedic Research Group Hip Fracture Delirium Algorithm Perform Well on an Independent Intercontinental Cohort of Patients With Hip Fractures Who Are 60 Years or Older?

Background: Postoperative delirium in patients aged 60 years or older with hip fractures adversely affects clinical and functional outcomes. The economic cost of delirium is estimated to be as high as USD 25,000 per patient, with a total budgetary impact between USD 6.6 to USD 82.

What Is the Clinical Benefit of Common Orthopaedic Procedures as Assessed by the PROMIS Versus Other Validated Outcomes Tools?

Background: Patient-reported outcome measures (PROMs), including the Patient-reported Outcomes Measurement Information System (PROMIS), are increasingly used to measure healthcare value. The minimum clinically important difference (MCID) is a metric that helps clinicians determine whether a statistically detectable improvement in a PROM after surgical care is likely to be large enough to be important to a patient or to justify an intervention that carries risk and cost. There are two major categories of MCID calculation methods, anchor-based and distribution-based.

Development and internal validation of machine learning algorithms for predicting complications after primary total hip arthroplasty.

Introduction: Complications after total hip arthroplasty (THA) may result in readmission or reoperation and impose a significant cost on the healthcare system. Understanding which patients are at-risk for complications can potentially allow for targeted interventions to decrease complication rates through pursuing preoperative health optimization. The purpose of the current was to develop and internally validate machine learning (ML) algorithms capable of performing patient-specific predictions of all-cause complications within two years of primary THA.