Usability and performance expectancy govern spine surgeons’ use of a clinical decision support system for shared decision-making on the choice of treatment of common lumbar degenerative disorders

Study designQuantitative survey study is the study design.ObjectivesThe study aims to develop a model for the factors that drive or impede the use of an artificial intelligence clinical decision support system (CDSS) called PROPOSE, which supports shared decision-making on the choice of treatment of ordinary spinal disorders.MethodsA total of 62 spine surgeons were asked to complete a questionnaire regarding their behavioral intention to use the CDSS after being introduced to PROPOSE. The model behind the questionnaire was the Unified Theory of Acceptance and Use of Technology. Data were analyzed using partial least squares structural equation modeling.ResultsThe degree of ease of use associated with the new technology (effort expectancy/usability) and the degree to which an individual believes that using a new technology will help them attain gains in job performance (performance expectancy) were the most important factors. Social influence and trust in the CDSS were other factors in the path model. r2 for the model was 0.63, indicating that almost two-thirds of the variance in the model was explained. The only significant effect in the multigroup analyses of path differences between two subgroups was for PROPOSE use and social influence (p = 0.01).ConclusionShared decision-making is essential to meet patient expectations in spine surgery. A trustworthy CDSS with ease of use and satisfactory predictive ability promoted by the leadership will stand the best chance of acceptance and bridging the communication gap between the surgeon and the patient

Applied Machine Learning for Spine Surgeons:Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data

STUDY DESIGN: Retrospective/prospective study. OBJECTIVE: Models based on preoperative factors can predict patients’ outcome at 1-year follow-up. This study measures the performance of several machine learning (ML) models and compares the results with conventional methods. METHODS: Inclusion criteria were patients who had lumbar disc herniation (LDH) surgery, identified in the Danish national registry for spine surgery. Initial training of models included 16 independent variables, including demographics and presurgical patient-reported measures. Patients were grouped by reaching minimal clinically important difference or not for EuroQol, Oswestry Disability Index, Visual Analog Scale (VAS) Leg, and VAS Back and by their ability to return to work at 1 year follow-up. Data were randomly split into training, validation, and test sets by 50%/35%/15%. Deep learning, decision trees, random forest, boosted trees, and support vector machines model were trained, and for comparison, multivariate adaptive regression splines (MARS) and logistic regression models were used. Model fit was evaluated by inspecting area under the curve curves and performance during validation. RESULTS: Seven models were arrived at. Classification errors were within ±1% to 4% SD across validation folds. ML did not yield superior performance compared with conventional models. MARS and deep learning performed consistently well. Discrepancy was greatest among VAS Leg models. CONCLUSIONS: Five predictive ML and 2 conventional models were developed, predicting improvement for LDH patients at the 1-year follow-up. We demonstrate that it is possible to build an ensemble of models with little effort as a starting point for further model optimization and selection

Validating the predictive precision of the dialogue support tool on Danish patient cohorts

BACKGROUND: Despite advances in surgical techniques and diagnostics, some patients remain unsatisfied with the result following spine surgery. One way to improve patient satisfaction may be found in better alignment of expectations. Prognostic tools might prove useful in strengthening surgeon-patient communication prior to surgery. The purpose of this study is to assess the predictive capabilities of the Swedish based Dialogue Support (DS) tool for spine surgery on a Danish population. METHODS: The study included the diagnoses lumbar disc herniation, lumbar spinal stenosis, and lumbar degenerative disc disease. A total of 5.954 patients were retrieved from the Danish national spine registry (DaneSpine). For each group, 200 random cases with complete preoperative and 1 year follow-up data were selected. Two outcome measures were used: Global assessment of pain (GA pain) and satisfaction with outcome. Predictions were produced by manual entry in the DS application. Goodness of fit tests were used to compare the predicted distribution of proportions with successful outcomes (GA pain) to the actual distribution in the three samples. Binomial tests were performed to evaluate the predicted proportion of satisfied patients. Furthermore, ROC-curves, calibration plots, and metrics were calculated to assess the predictive performance. RESULTS: ROC curves showed comparable AUC values with the values reported by the developing authors of the DS from 0.62 to 0.73 (GA pain) and 0.64 to 0.70 (satisfaction with outcome). The calibration plots, however, revealed a low degree of concordance. For GA pain sensitivity varied from 92.4% to 99.3%, and specificity from 1.5% to 13.4%. For satisfaction, sensitivity varied from 97.1% to 99.2% and specificity from 0.0% to 2.9%. CONCLUSIONS: The predictive capabilities of the DS tool could not be generalized to the Danish sample cohorts. Further research on larger samples, provided full access to the underlying algorithms can be obtained, could produce a different result

The Association of MRI Findings and Long-Term Disability in Patients With Chronic Low Back Pain

STUDY DESIGN: Longitudinal cohort study with 13-year follow-up. OBJECTIVE: To assess whether long-term disability is associated with baseline degenerative magnetic resonance imaging (MRI) findings in patients with low back pain (LBP). METHODS: In 2004-2005, patients aged 18 to 60 years with chronic LBP were enrolled in a randomized controlled trial and lumbar MRI was performed. Patients completed the Roland-Morris Disability Questionnaire (RMDQ) and the LBP Rating Scale, at baseline and 13 years after the MRI. Multivariate regression analysis was performed with 13-year RMDQ as the dependent variable and baseline disc degeneration (DD, Pfirrmann grade), Modic changes (MC), facet joint degeneration (FJD, Fujiwara grade) smoking status, body mass index, and self-reported weekly physical activity at leisure as independent variables. RESULTS: Of 204 patients with baseline MRI, 170 (83%) were available for follow-up. Of these, 88 had Pfirrmann grade >III (52%), 67 had MC (39%) and 139 had Fujiwara grade >2 (82%) on at least 1 lumbar level. Only MC (β = −0.15, P = .031) and weekly physical activity at leisure (β = −0.51, P < .001) were significantly, negatively, associated with 13-year RMDQ-score (R(2) = 0.31). CONCLUSION: DD and FJD were not associated with long-term disability. Baseline MC and weekly physical activity at leisure were statistically significantly associated with less long-term disability

Multiple Myeloma Associated Bone Disease

The lytic bone disease is a hallmark of multiple myeloma, being present in about 80% of patients with newly diagnosed MM, and in more during the disease course. The myeloma associated bone disease (MBD) severely affects the morbidity and quality of life of the patients. MBD defines treatment demanding MM. In recent years, knowledge of the underlying pathophysiology has increased, and novel imaging technologies, medical and non-pharmaceutical treatments have improved. In this review, we highlight the major achievements in understanding, diagnosing and treating MBD. For diagnosing MBD, low-dose whole-body CT is now recommended over conventional skeletal survey, but also more advanced functional imaging modalities, such as diffusion-weighted MRI and PET/CT are increasingly important in the assessment and monitoring of MBD. Bisphosphonates have, for many years, played a key role in management of MBD, but denosumab is now an alternative to bisphosphonates, especially in patients with renal impairment. Radiotherapy is used for uncontrolled pain, for impeding fractures and in treatment of impeding or symptomatic spinal cord compression. Cement augmentation has been shown to reduce pain from vertebral compression fractures. Cautious exercise programs are safe and feasible and may have the potential to improve the status of patients with MM