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Background The use of so-called “red flags” may be beneficial in identifying patients with metastatic spinal disease. This study examined the utility and efficacy of these red flags in the referral chain of patients surgically treated for spinal metastases. Methods The referral chains from the onset of symptoms until surgical treatment for all patients receiving surgery for spinal metastases between March 2009 and December 2020 were reconstructed. The documentation of red flags, as defined by the Dutch National Guideline on Metastatic Spinal Disease, was assessed for each healthcare provider involved. Results A total of 389 patients were included in the study. On average, 33.3% of red flags were documented as present, 3.6% were documented as absent, and 63.1% were undocumented. A higher rate of red flags documented as present was associated with a longer time to diagnosis, but a shorter time to definitive treatment by a spine surgeon. Moreover, red flags were documented as present more often in patients who developed neurological symptoms at any point during the referral chain than those who remained neurologically intact. Conclusions The association of red flags with developing neurological deficits highlights their significance in clinical assessment. However, the presence of red flags was not found to decrease delays prior to referral to a spine surgeon, indicating that their relevance is currently not sufficiently recognized by healthcare providers. Raising awareness of symptoms indicative of spinal metastases may expedite timely (surgical) treatment and thus improve treatment outcome.

Purpose We assessed the accuracy of a new 3D2D registration algorithm to be used for navigated spine surgery and explored anatomical and radiologic parameters affecting the registration accuracy. Compared to existing 3D2D registration algorithms, the algorithm does not need bone-mounted or table-mounted instruments for registration. Neither does the intraoperative imaging device have to be tracked or calibrated. Methods The rigid registration algorithm required imaging data (a pre-existing CT scan (3D) and two angulated fluoroscopic images (2D)) to register positions of vertebrae in 3D and is based on non-invasive skin markers. The algorithm registered five adjacent vertebrae and was tested in the thoracic and lumbar spine from three human cadaveric specimens. The registration accuracy was calculated for each registered vertebra and measured with the target registration error (TRE) in millimeters. We used multivariable analysis to identify parameters independently affecting the algorithm’s accuracy such as the angulation between the two fluoroscopic images (between 40° and 90°), the detector-skin distance, the number of skin markers applied, and waist circumference. Results The algorithm registered 780 vertebrae with a median TRE of 0.51 mm [interquartile range 0.32–0.73 mm] and a maximum TRE of 2.06 mm. The TRE was most affected by the angulation between the two fluoroscopic images obtained (p p = 0.004) and in the specimen with the smallest waist circumference (p = 0.003). The algorithm registered all five adjacent vertebrae with similar accuracy. Conclusion We studied the accuracy of a new 3D2D registration algorithm based on non-invasive skin markers. The algorithm registered five adjacent vertebrae with similar accuracy in the thoracic and lumbar spine and showed a maximum target registration error of approximately 2 mm. To further evaluate its potential for navigated spine surgery, the algorithm may now be integrated into a complete navigation system.

Background: Limited health literacy has been associated with adverse health outcomes. Undergoing orthopedic surgery often requires patients to make complex decisions and adhere to complicated instructions, suggesting that health literacy skills might have a profound impact on orthopedic surgery outcomes. Purpose: We sought to review the literature for studies investigating the level of health literacy in patients undergoing orthopedic surgery and also to assess how those studies report factors affecting health equity. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane Library for all health literacy studies published in the orthopedic surgery literature up to February 8, 2022. Search terms included synonyms for health literacy and for all orthopedic surgery subspecialties. Two reviewers independently extracted study data in addition to indicators of equity reporting using the PROGRESS+ checklist (Place of Residence, Race/Ethnicity, Occupation, Gender/sex, Religion, Education, Social capital, Socioeconomic status, plus age, disability, and sexual orientation). Results: The search resulted in 616 studies; 9 studies remained after exclusion criteria were applied. Most studies were of arthroplasty (4/9; 44%) or trauma (3/9; 33%) patients. Validated health literacy assessments were used in 4 of the included studies, and only 3 studies reported the rate of limited health literacy in the patients studied, which ranged between 34% and 38.5%. At least one PROGRESS+ item was reported in 88% (8/9) of the studies. Conclusions: We found a paucity of appropriately designed studies that used validated measures of health literacy in the field of orthopedic surgery. The potential impact of health literacy on orthopedic patients and their outcomes has yet to be elucidated. Thoughtful, high-quality trials across diverse demographics and geographies are warranted.

Skeletal diseases and their surgical treatment induce severe pain. The innervation density of bone potentially explains the severe pain reported. Animal studies concluded that sensory myelinated A∂-fibers and unmyelinated C-fibers are mainly responsible for conducting bone pain, and that the innervation density of these nerve fibers was highest in periosteum. However, literature regarding sensory innervation of human bone is scarce. This observational study aimed to quantify sensory nerve fiber density in periosteum, cortical bone, and bone marrow of axial and appendicular human bones using immunohistochemistry and confocal microscopy. Multivariate Poisson regression analysis demonstrated that the total number of sensory and sympathetic nerve fibers was highest in periosteum, followed by bone marrow, and cortical bone for all bones studied. Bone from thoracic vertebral bodies contained most sensory nerve fibers, followed by the upper extremity, lower extremity, and parietal neurocranium. The number of nerve fibers declined with age and did not differ between male and female specimens. Sensory nerve fibers were organized as a branched network throughout the periosteum. The current results provide an explanation for the severe pain accompanying skeletal disease, fracture, or surgery. Further, the results could provide more insight into mechanisms that generate and maintain skeletal pain and might aid in developing new treatment strategies. PERSPECTIVE: This article presents the innervation of human bone and assesses the effect of age, gender, bone compartment and type of bone on innervation density. The presented data provide an explanation for the severity of bone pain arising from skeletal diseases and their surgical treatment.

Background and purpose — Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed. Material and methods — We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics. Results — Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635–26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73–0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis. Interpretation — ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.

BACKGROUND The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data. QUESTIONS/PURPOSES (1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions? METHODS All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study. RESULTS The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1-year survival, respectively. CONCLUSION SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps.io/extremitymetssurvival/. LEVEL OF EVIDENCE Level III, therapeutic study.

Purpose: Pain response after conventional external beam radiation therapy (cRT) in patients with painful bone metastases is observed in 60% to 70% of patients. The aim of the VERTICAL trial was to investigate whether stereotactic body radiation therapy (SBRT) improves pain response.Methods and Materials: This single-center, phase 2, randomized controlled trial was conducted within the PRESENT cohort, which consists of patients referred for radiation therapy of bone metastases to our tertiary center. Cohort participants with painful bone metastases who gave broad informed consent for randomization were randomly assigned to cRT or SBRT. Only patients in the intervention arm received information about the trial and were offered SBRT (1 x 18 Gy, 3 x 10 Gy, or 5 x 7 Gy), which they could accept or refuse. Patients who refused SBRT underwent standard cRT (1 x 8 Gy, 5 x 4 Gy, or 10 x 3 Gy). Patients in the control arm were not informed. Primary endpoint was pain response at 3 months after radiation therapy. Secondary outcomes were pain response at any point within 3 months, mean pain scores, and toxicity. Data were analyzed intention to treat (ITT) and per protocol (PP). This trial was registered with Clinicaltrials.gov, NCT02364115.Results: Between January 29, 2015, and March 20, 2019, 110 patients were randomized. ITT analysis included 44 patients in the cRT arm and 45 patients in the SBRT arm. In the intervention arm, 12 patients (27%) declined SBRT, and 7 patients (16%) were unable to complete the SBRT treatment. In ITT, 14 of 44 patients (32%; 95% confidence interval [CI], 18%-45%) in the control arm and 18 of 45 patients (40%; 95% CI, 26%-54%) in the SBRTarm reported a pain response at 3 months (P = .42). In PP, these proportions were 14 of 44 (32%; 95% CI, 18%-45%) and 12 of 23 patients (46%; 95% CI, 27%-66%), respectively (P = .55). In ITT, a pain response within 3 months was reported by 30 of 44 control patients (82%; 95% CI, 68%-90%) and 38 of 45 patients (84%; 95% CI, 71%-92%) in the SBR Tarm (P = .12). In PP, these proportions were 36 of 44 (82%; 95% CI, 68%-90%) and 26 of 27 patients (96%; 95% CI; 81%-100%), respectively (P = .12). No grade 3 or 4 toxicity was observed in either arm.Conclusions: SBRT did not significantly improve pain response in patients with painful bone metastases. One in 4 patients preferred to undergo cRT over SBRT, and 1 in 5 patients starting SBRT was unable to complete this treatment. Because of this selective dropout, which can be attributed to the character of the intervention, the trial was underpowered to detect the prespecified difference in pain response. (C) 2020 The Author(s). Published by Elsevier Inc.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Implementation of COVID-19 measures may have induced concerns about access and quality of health care for cancer patients with bone metastases, and it may have affected their quality of life. In this study, we evaluated the effect of the first COVID-19 lockdown on quality of life and emotional functioning of patients with stage IV cancer treated for painful bone metastases in the UMC Utrecht, the Netherlands. A COVID-19 specific questionnaire was sent to active participants in the Prospective Evaluation of interventional StudiEs on boNe meTastases (PRESENT) cohort, consisting of patients irradiated for metastatic bone disease. Patient reported outcomes (PROs) were compared with the last two PROs collected within the PRESENT cohort before the COVID-19 lockdown in the Netherlands on the 16th of March. For the 169 (53%) responders, median age at start of lockdown was 68 years (range 38–92) and 62% were male. Patients reported a statistically significant decrease in emotional functioning (83.6 to 79.2, P = 0.004) and in general quality of life score during the COVID-19 lockdown (72.4 to 68.7, P = 0.007). A steep increase in feeling isolated was reported (18% before and 67% during lockdown). This study has shown a strong increase in the experience of isolation and a decrease of emotional functioning and general quality of life during the COVID-19 lockdown in cancer patients with bone metastases. Due to the nature of the treatment of this patient population, efforts should be made to minimize these changes during future lockdowns.

Study Design: Retrospective cohort study. Objectives: Symptoms caused by spinal metastases are often difficult to distinguish from symptoms caused by non-malignant spinal disease, complicating timely diagnosis, referral and treatment. The ensuing delays may promote the risk of neurological deficits or severe mechanical instability and consequent emergency surgery, leading to poorer prognosis. Presumably, treatment delay may subsequently lead to more health-care consumption and therefore increased average costs of treatment. Methods: All patients surgically treated for spinal metastases were included in the current study. Based on the presence of alarming symptoms and urgency of the required intervention, patients were categorized as having received timely or delayed treatment. Pre-surgical, in-hospital, aftercare and total costs were analyzed and compared between the 2 groups. Results: In total, 299 patients were included, of which 205 underwent timely and 94 delayed treatment. There was no significant difference in pre-surigcal costs (€3.229,13 in the timely treated group vs. €2.528,70 in the delayed treatment group, p = 0.849). The in-hospital costs (€16.738,49 vs. €13.108,81, p < 0.001) and the aftercare costs (€13.950,37 vs. 3.981,93, p < 0.001) were significantly higher for delayed treatment vs. timely treatment, respectively. The total costs were €33.741,71 for delayed treatment and €20.318,52 for timely treatment (p < 0.001). Conclusions: The total costs for timely treated patients with spinal metastases are significantly lower compared with patients receiving delayed treatment. Investing in the optimization of referral patterns may therefore reduce the overall pretreatment delay and subsequently increase patient outcome, leading to better clinical outcomes at lower costs.

BACKGROUND: Machine learning (ML) is a subdomain of artificial intelligence that enables computers to abstract patterns from data without explicit programming. A myriad of impactful ML applications already exists in orthopaedics ranging from predicting infections after surgery to diagnostic imaging. However, no systematic reviews that we know of have compared, in particular, the performance of ML models with that of clinicians in musculoskeletal imaging to provide an up-to-date summary regarding the extent of applying ML to imaging diagnoses. By doing so, this review delves into where current ML developments stand in aiding orthopaedists in assessing musculoskeletal images. QUESTIONS/PURPOSES: This systematic review aimed (1) to compare performance of ML models versus clinicians in detecting, differentiating, or classifying orthopaedic abnormalities on imaging by (A) accuracy, sensitivity, and specificity, (B) input features (for example, plain radiographs, MRI scans, ultrasound), (C) clinician specialties, and (2) to compare the performance of clinician-aided versus unaided ML models. METHODS: A systematic review was performed in PubMed, Embase, and the Cochrane Library for studies published up to October 1, 2019, using synonyms for machine learning and all potential orthopaedic specialties. We included all studies that compared ML models head-to-head against clinicians in the binary detection of abnormalities in musculoskeletal images. After screening 6531 studies, we ultimately included 12 studies. We conducted quality assessment using the Methodological Index for Non-randomized Studies (MINORS) checklist. All 12 studies were of comparable quality, and they all clearly included six of the eight critical appraisal items (study aim, input feature, ground truth, ML versus human comparison, performance metric, and ML model description). This justified summarizing the findings in a quantitative form by calculating the median absolute improvement of the ML models compared with clinicians for the following metrics of performance: accuracy, sensitivity, and specificity. RESULTS: ML models provided, in aggregate, only very slight improvements in diagnostic accuracy and sensitivity compared with clinicians working alone and were on par in specificity (3% (interquartile range [IQR] -2.0% to 7.5%), 0.06% (IQR -0.03 to 0.14), and 0.00 (IQR -0.048 to 0.048), respectively). Inputs used by the ML models were plain radiographs (n = 8), MRI scans (n = 3), and ultrasound examinations (n = 1). Overall, ML models outperformed clinicians more when interpreting plain radiographs than when interpreting MRIs (17 of 34 and 3 of 16 performance comparisons, respectively). Orthopaedists and radiologists performed similarly to ML models, while ML models mostly outperformed other clinicians (outperformance in 7 of 19, 7 of 23, and 6 of 10 performance comparisons, respectively). Two studies evaluated the performance of clinicians aided and unaided by ML models; both demonstrated considerable improvements in ML-aided clinician performance by reporting a 47% decrease of misinterpretation rate (95% confidence interval [CI] 37 to 54; p < 0.001) and a mean increase in specificity of 0.048 (95% CI 0.029 to 0.068; p < 0.001) in detecting abnormalities on musculoskeletal images. CONCLUSIONS: At present, ML models have comparable performance to clinicians in assessing musculoskeletal images. ML models may enhance the performance of clinicians as a technical supplement rather than as a replacement for clinical intelligence. Future ML-related studies should emphasize how ML models can complement clinicians, instead of determining the overall superiority of one versus the other. This can be accomplished by improving transparent reporting, diminishing bias, determining the feasibility of implantation in the clinical setting, and appropriately tempering conclusions. LEVEL OF EVIDENCE: Level III, diagnostic study.

BACKGROUND Patients with bone metastases often are unable to complete quality of life (QoL) questionnaires, and cohabitants (such as spouses, domestic partners, offspring older than 18 years, or other people who live with the patient) could be a reliable alternative. However, the extent of reliability in this complicated patient population remains undefined, and the influence of the cohabitant's condition on their assessment of the patient's QoL is unknown. QUESTIONS/PURPOSES (1) Do QoL scores, measured by the 5-level EuroQol-5D (EQ-5D-5L) version and the Patient-reported Outcomes Measurement Information System (PROMIS) version 1.0 in three domains (anxiety, pain interference, and depression), reported by patients differ markedly from scores as assessed by their cohabitants? (2) Do cohabitants' PROMIS-Depression scores correlate with differences in measured QoL results? METHODS This cross-sectional study included patients and cohabitants older than 18 years of age. Patients included those with presence of histologically confirmed bone metastases (including lymphoma and multiple myeloma), and cohabitants must have been present at the clinic visit. Patients were eligible for inclusion in the study regardless of comorbidities, prognosis, prior surgery, or current treatment. Between June 1, 2016 and March 1, 2017 and between October 1, 2017 and February 26, 2018, all 96 eligible patients were approached, of whom 49% (47) met the selection criteria and were willing to participate. The included 47 patient-cohabitant pairs independently completed the EQ-5D-5L and the eight-item PROMIS for three domains (anxiety, pain, and depression) with respect to the patients' symptoms. The cohabitants also completed the four-item PROMIS-Depression survey with respect to their own symptoms. RESULTS There were no clinically important differences between the scores of patients and their cohabitants for all questionnaires, and the agreement between patient and cohabitant scores was moderate to strong (Spearman correlation coefficients ranging from 0.52 to 0.72 on the four questionnaires; all p values < 0.05). However, despite the good agreement in QoL scores, an increased cohabitant's depression score was correlated with an overestimation of the patient's symptom burden for the anxiety and depression domains (weak Spearman correlation coefficient of 0.33 [95% confidence interval 0.08 to 0.58]; p = 0.01 and moderate Spearman correlation coefficient of 0.52 [95% CI 0.29 to 0.74]; p < 0.01, respectively). CONCLUSION The present findings support that cohabitants might be reliable raters of the QoL of patients with bone metastases. However, if a patient's cohabitant has depression, the cohabitant may overestimate a patient's symptoms in emotional domains such as anxiety and depression, warranting further research that includes cohabitants with and without depression to elucidate the effect of depression on the level of agreement. For now, clinicians may want to reconsider using the cohabitant's judgement if depression is suspected. CLINICAL RELEVANCE These findings suggest that a cohabitant's impressions of a patient's quality of life are, in most instances, accurate; this is potentially helpful in situations where the patient cannot weigh in. Future studies should employ longitudinal designs to see how or whether our findings change over time and with disease progression, and how specific interventions-like different chemotherapeutic regimens or surgery-may factor in.

Purpose A major challenge in metastatic spinal disease is timely identification of patients. Left untreated, spinal metastases may lead to gross mechanical instability and/or neurological deficits, often requiring extensive invasive surgical treatment. The aim of this cohort study was to assess the correlation between delayed treatment of patients with spinal metastases and functional performance, quality of life and survival. Methods All patients surgically treated for metastatic spinal disease at a tertiary care facility were included for analysis. Patients who underwent elective surgery were considered as timely treated, whereas patients requiring emergency surgery were considered to be treated in a delayed fashion. EQ-5D scores, KPS scores and mortality rates were compared between the two groups. Results A total of 317 patients (215 timely treated, 102 delayed) had survivorship data available and 202 patients (147 timely treated, 55 delayed) had clinical data available. Multivariate analyses showed delayed treatment was associated with lower EQ-5D and KPS scores and higher mortality rates, independent of confounders such as baseline EQ-5D/KPS scores, neurological status, tumor prognosis and patient age. Conclusions The results from the present study show delayed treatment of patients with symptomatic spinal metastases has both direct and indirect adverse consequences for functional performance status, quality of life and survival. Optimization of referral pattern may accelerate the time to surgical treatment, potentially leading to better quality of life and survival.

Diffuse idiopathic skeletal hyperostosis (DISH) is characterized by growing ossifications of spinal entheses and tendons, which may cause trachea and esophagus compression when located anteriorly in the cervical spine.Our previous systematic review on the epidemiological and clinical knowledge of dysphagia and airway obstruction caused by cervical DISH was updated, with a focus on (surgical) treatment and outcomes.A systematic review of the literature was performed.Publications in Medline and EMBASE from July 2010 to June 2021 were searched. Two investigators performed data extraction and study specific quality assessment.A total of 138 articles (112 case reports and 26 case series) were included, describing 419 patients with dysphagia and/or airway obstruction. The mean age of the patient group was 67.3 years (range: 35-91 years), and 85.4% was male. An evident increase of published cases was observed within the last decade. Surgical treatment was chosen for 66% of patients with the anterolateral approach most commonly used. The total complication rate after surgery was 22.1%, with 12.7% occurring within 1 month after intervention. Improvement of dysphagia was observed in 95.5% of operated patients. After a mean follow-up of 3.7 years (range: 0.4-9.0 years), dysphagia recurred in 12 surgically treated patients (4%), of which five patients had osteophyte regrowth.The number of published cases of dysphagia in patients with DISH has doubled in the last decade compared to our previous review. Yet, randomized studies or guidelines on the treatment or prevention on recurrence are lacking. Surgical treatment is effective and has low (major) complication rates. Common trends established across the cases in our study may help improve our understanding and management of dysphagia and airway obstruction in cervical DISH.

Machine learning (ML) models are emerging at a rapid pace in orthopaedic imaging due to their ability to facilitate timely diagnostic and treatment decision making. However, despite a considerable increase in model development and ML-related publications, there has been little evaluation regarding the quality of these studies. In order to successfully move forward with the implementation of ML models for diagnostic imaging in orthopaedics, it is imperative that we ensure models are held at a high standard and provide applicable, reliable and accurate results. Multiple reporting guidelines have been developed to help authors and reviewers of ML models, such as the Checklist for AI in Medical Imaging (CLAIM) and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Previous investigations of prognostic orthopaedic ML models have reported concerns with regard to the rate of transparent reporting. Therefore, an assessment of whether ML models for diagnostic imaging in orthopaedics adequately and clearly report essential facets of their model development is warranted.To evaluate (1) the completeness of the CLAIM checklist and (2) the risk of bias according to the QUADAS-2 tool for ML-based orthopaedic diagnostic imaging models. This study sought to identify ML details that researchers commonly fail to report and to provide recommendations to improve reporting standards for diagnostic imaging ML models.A systematic review was performed to identify ML-based diagnostic imaging models in orthopaedic surgery. Articles published within the last 5 years were included. Two reviewers independently extracted data using the CLAIM checklist and QUADAS-2 tool, and discrepancies were resolved by discussion with at least two additional reviewers.After screening 7507 articles, 91 met the study criteria. The mean completeness of CLAIM items was 63 % (SD ± 28 %). Among the worst reported CLAIM items were item 28 (metrics of model performance), item 13 (the handling of missing data) and item 9 (data preprocessing steps), with only 2 % (2/91), 8 % (7/91) and 13 % (12/91) of studies correctly reporting these items, respectively. The QUADAS-2 tool revealed that the patient selection domain was at the highest risk of bias: 18 % (16/91) of studies were at high risk of bias and 32 % (29/91) had an unknown risk of bias.This review demonstrates that the reporting of relevant information, such as handling missing data and data preprocessing steps, by diagnostic ML studies for orthopaedic imaging studies is limited. Additionally, a substantial number of works were at high risk of bias. Future studies describing ML-based models for diagnostic imaging should adhere to acknowledged methodological standards to maximize the quality and applicability of their models.

Study Design: Literature review. Objective: To provide an overview of the recent advances in spinal oncology, emphasizing the key role of the surgeon in the treatment of patients with spinal metastatic tumors. Methods: Literature review. Results: Therapeutic advances led to longer survival times among cancer patients, placing significant emphasis on durable local control, optimization of quality of life, and daily function for patients with spinal metastatic tumors. Recent integration of modern diagnostic tools, precision oncologic treatment, and widespread use of new technologies has transformed the treatment of spinal metastases. Currently, multidisciplinary spinal oncology teams include spinal surgeons, radiation and medical oncologists, pain and rehabilitation specialists, and interventional radiologists. Consistent use of common language facilitates communication, definition of treatment indications and outcomes, alongside comparative clinical research. The main parameters used to characterize patients with spinal metastases include functional status and health-related quality of life, the spinal instability neoplastic score, the epidural spinal cord compression scale, tumor histology, and genomic profile. Conclusions: Stereotactic body radiotherapy revolutionized spinal oncology through delivery of durable local tumor control regardless of tumor histology. Currently, the major surgical indications include mechanical instability and high-grade spinal cord compression, when applicable, with surgery providing notable improvement in the quality of life and functional status for appropriately selected patients. Surgical trends include less invasive surgery with emphasis on durable local control and spinal stabilization.

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. Methods: We used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance score. Results: We included 28,600 patients with a median age of 67 (interquartile range 58–74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic = 0.751), calibration (slope = 0.933; intercept = 0.037), and overall performance (Brier score = 0.131). Conclusions: A machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower costs. Graphical abstract: These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.].

AIM: Surgery for spinal metastases can improve symptoms, but sometimes complications can negate the benefits. Operations may have different indications, complexities and risks, and the choice for an individual is a tailor-made personalised decision. Previous prognostic scoring systems are becoming out of date and inaccurate. We designed a risk calculator to estimate survival after surgery, to inform clinicians and patients when making management decisions. METHODS: A prospective cohort study was performed, including 1430 patients with spinal metastases who underwent surgery. Of them, 1264 patients from 20 centres were used for model development using a Cox frailty model. Calibration slope, D-statistic and C-index were used for model validation based on 166 patients. Follow-up was to death or minimum of 2 years after surgery. Pre-operative indices (examination findings, pain, Karnofsky physical functioning score, and radiology) were assessed. RESULTS: An algorithm to predict survival was constructed including the tumour type, ambulatory status, analgesic use, American Society of Anesthesiologists score, number of spinal metastases, previous radiotherapy or chemotherapy, presence of visceral metastases, cervical or thoracic spine involvement, as predictors. An Internet-based risk calculator was developed based on this algorithm, with similar or improved accuracy compared to other validated prognostic scoring systems (C-index, 0.68; 95% confidence interval, 0.63--0.73, and calibration slope, 1.00; 95% confidence interval, 0.68--1.32). CONCLUSION: A large, prospective, surgical series of patients with symptomatic spinal metastases was used to create a validated risk calculator that can help clinicians to inform patients about the most appropriate treatment plan. The calculator is available at www.spinemet.com. ispartof: EUROPEAN JOURNAL OF CANCER vol:107 pages:28-36 ispartof: location:England status: published

Objectives Earlier retrospective studies have suggested a relation between DISH and cardiovascular disease, including myocardial infarction. The present study assessed the association between DISH and incidence of cardiovascular events and mortality in patients with high cardiovascular risk. Methods In this prospective cohort study, we included 4624 patients (mean age 58.4 years, 69.6% male) from the Second Manifestations of ARTerial disease cohort. The main end point was major cardiovascular events (MACE: stroke, myocardial infarction and vascular death). Secondary endpoints included all-cause mortality and separate vascular events. Cause-specific proportional hazard models were used to evaluate the risk of DISH on all outcomes, and subdistribution hazard models were used to evaluate the effect of DISH on the cumulative incidence. All models were adjusted for age, sex, body mass index, blood pressure, diabetes, non-HDL cholesterol, packyears, renal function and C-reactive protein. Results DISH was present in 435 (9.4%) patients. After a median follow-up of 8.7 (IQR 5.0–12.0) years, 864 patients had died and 728 patients developed a MACE event. DISH was associated with an increased cumulative incidence of ischaemic stroke. After adjustment in cause-specific modelling, DISH remained significantly associated with ischaemic stroke (HR 1.55; 95% CI: 1.01, 2.38), but not with MACE (HR 0.99; 95% CI: 0.79, 1.24), myocardial infarction (HR 0.88; 95% CI: 0.59, 1.31), vascular death (HR 0.94; 95% CI: 0.68, 1.27) or all-cause mortality (HR 0.94; 95% CI: 0.77, 1.16). Conclusion The presence of DISH is independently associated with an increased incidence and risk for ischaemic stroke, but not with MACE, myocardial infarction, vascular death or all-cause mortality.

The recent societal debate on opioid use in treating postoperative pain has sparked the development of long-acting, opioid-free analgesic alternatives, often using the amino-amide local anesthetic bupivacaine as active pharmaceutical ingredient. A potential application is musculoskeletal surgeries, as these interventions rank amongst the most painful overall. Current literature showed that bupivacaine induced dose-dependent myo-, chondro-, and neurotoxicity, as well as delayed osteogenesis and disturbed wound healing

Background Realistic pre-treatment expectations are important and have been associated with post-treatment health related quality of life (HRQOL). Patient expectations are greatly influenced by physicians, as they are the primary resource for information. This study aimed to explore the communication practices of physicians regarding treatment outcomes for patients with spinal metastases, and physician experiences with patients’ pre-treatment expectations. Methods An international qualitative study using semi-structured interviews with physicians routinely involved in treating metastatic spine disease (spine surgeons, radiation and medical oncologists, and rehabilitation specialists) was conducted. Physicians were interviewed about the content and extent of information they provide to patients with spinal metastases regarding treatment options, risks and treatment outcomes. Interviews were transcribed verbatim and analyzed using a thematic coding network. Results After 22 interviews data saturation occurred. The majority of the physicians indicated that they currently do not establish patients’ pre-treatment expectations, despite acknowledging the importance of these expectations. Spine surgeons often believe that patient expectations are disproportionate. Physicians expressed they manage expectations by detailing the most common risks and providing a broad but nonspecific overview of treatment outcomes. While the palliative intent seems clear to the physicians, their perception is that the implications of a palliative treatment remains elusive to most patients. Conclusion This study highlights the current gap in patient-physician communication regarding expectations of treatment outcomes of patients with spinal metastases. These results warrant further research to improve communication practices and determine the effect of patient expectations on patient reported outcomes in this population.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.

The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. However, processing this rich resource of data for clinical and research purposes, depends on labor-intensive and potentially error-prone manual review. The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.Bone scintigraphy reports of patients undergoing surgery for bone metastases were labeled each by three independent reviewers using a binary classification (single metastasis versus two or more metastases) to establish a ground truth. A stratified 80:20 split was used to develop and test an extreme-gradient boosting supervised machine learning NLP algorithm.A total of 704 free-text bone scintigraphy reports from 704 patients were included in this study and 617 (88%) had multiple bone metastases. In the independent test set (NLP has the potential to automate clinical data extraction from free text radiology notes in orthopedics, thereby optimizing the speed, accuracy, and consistency of clinical chart review. Pending external validation, the NLP algorithm developed in this study may be implemented as a means to aid researchers in tackling large amounts of data.

Predicting oncologic outcomes is essential for optimizing the treatment for patients with cancer. This review examines the feasibility of using Computed Tomography (CT) images of fat density as a prognostic factor in patients with cancer.A systematic literature search was performed in PubMed, Embase and Cochrane up to March 2020. All studies that mentioned using subcutaneous or visceral adipose tissue (SAT and VAT, respectively) CT characteristics as a prognostic factor for patients with cancer were included. The primary endpoints were any disease-related outcomes in patients with cancer.After screening 1043 studies, ten studies reporting a total of 23 - ten for SAT and thirteen for VAT - comparisons on survival, tumor recurrence and postsurgical infection were included. All ten studies included different types of malignancy: six localized, two metastatic disease, and two both. Five different anatomic landmarks were used to uniformly measure fat density on CT: lumbar (L)4 (This review may support the feasibility of using SAT or VAT on CT as a prognostic tool for patients with cancer in predicting adverse outcomes such as survival and tumor recurrence. Future research should standardize radiologic protocol in prospective homogeneous series of patients on each cancer diagnosis group in order to establish accurate parameters to help physicians use CT scan defined characteristics in clinical practice.

Diffuse idiopathic skeletal hyperostosis (DISH) is a systemic bone-forming condition characterized by the presence of at least three bony bridges at the anterolateral spine. The aim of this review was to address the present state of pathophysiological knowledge, the clinical relevance, and diagnosis of DISH. The pathogenesis of DISH is currently unknown. The presence of DISH has been associated with older age, male sex, obesity, hypertension, atherosclerosis, and diabetes mellitus. Because the new bone forms mainly at entheseal sites, local fibroblasts, chondrocytes, collagen fibers, and calcified matrix are probably influenced by genetic, vascular, metabolic, and mechanical factors. Diagnosing the presence of DISH is of clinical importance, because the risk of a spinal fracture increases and associations with the metabolic syndrome, coronary and aortic disease, and respiratory effects are strong. Unravelling the pathogenesis of DISH can impact the field of regenerative medicine and bone tissue regeneration.

Diffuse idiopathic skeletal hyperostosis (DISH) is a condition characterised by calcification and ossification of ligaments and entheses. The condition usually affects the axial skeleton, in particular, at the thoracic segment, though also other portions of the spine are often involved. DISH often involves also peripheral tendinous and/or entheseal sites either alone, or in association with the involvement of peripheral joints. At times, new bone formation involves the bone itself, but sometimes it involves joints not usually affected by osteoarthritis (OA) which result in bony enlargement of the epiphysis, joints space narrowing and a reduced range of motion. Because of the entheseal involvement, DISH can be mistaken for seronegative spondyloarthropathies or for a "simple" OA. Furthermore, other implications for the recognition of DISH include spinal fractures, difficult intubation and upper endoscopies, decreased response rates in DISH with concomitant spondyloarthritides, and increased likelihood to be affected by metabolic syndrome and cardiovascular diseases. This Atlas is intended to show the imaging finding in DISH in patients diagnosed with the condition by the Resnick classification criteria.

BACKGROUND: The benefits of surgery for symptomatic spinal metastases have been demonstrated, largely based on series of patients undergoing debulking and instrumentation operations. However, as cancer treatments improve and overall survival lengths increase, the incidence of recurrent spinal cord compression after debulking may increase. The aim of the current paper is to document the postoperative evolution of neurological function, pain, and quality of life following debulking and instrumentation in the Global Spine Tumor Study Group (GSTSG) database. METHODS: The GSTSG database is a prospective multicenter data repository of consecutive patients that underwent surgery for a symptomatic spinal metastasis. For the present analysis, patients were selected from the database that underwent decompressive debulking surgery with instrumentation. Preoperative tumor type, Tomita and Tokuhashi scores, EQ-5D, Frankel, Karnofsky, and postoperative complications, survival, EQ-5D, Frankel, Karnofsky, and pain numeric rating scores (NRS) at 3, 6, 12, and 24 months were analyzed. RESULTS: A total of 914 patients underwent decompressive debulking surgery with instrumentation and had documented follow-up until death or until 2 years post surgery. Median preoperative Karnofsky performance index was 70. A total of 656 patients (71.8%) had visceral metastases and 490 (53.6%) had extraspinal bone metastases. Tomita scores were evenly distributed above (49.1%) and below or equal to 5 (50.9%), and Tokuhashi scores almost evenly distributed below or equal to 8 (46.3%) and above 8 (53.7%). Overall, 12-month survival after surgery was 56.3%. The surgery resulted in EQ-5D health status improvement and NRS pain reduction that was maintained throughout follow-up. Frankel scores improved at first follow-up in 25.0% of patients, but by 12 months neurological deterioration was observed in 18.8%. CONCLUSION: We found that palliative debulking and instrumentation surgeries were performed throughout all Tomita and Tokuhashi categories. These surgeries reduced pain scores and improved quality of life up to 2 years after surgery. After initial improvement, a proportion of patients experienced neurological deterioration by 1 year, but the majority of patients remained stable. ispartof: ACTA NEUROCHIRURGICA vol:162 issue:4 pages:943-950 ispartof: location:Austria status: published

Background Little is known about treatment expectations of patients with spinal metastases undergoing radiotherapy and/or surgery. Assuming that patients with spinal metastases share characteristics with patients who had spinal surgery for non-cancer related conditions and with advanced cancer patients, we performed a systematic review to summarize the literature on patient expectations regarding treatment outcomes of spinal surgery and advanced cancer care. Methods A comprehensive search was performed in MEDLINE, EMBASE and PsycINFO for studies between 2000 and sep-2019. Studies including adult patients (> 18 years), undergoing spinal surgery or receiving advanced cancer care, investigating patients’ pre-treatment expectations regarding treatment outcomes were included. Two independent reviewers screened titles, abstracts and full-texts, extracted data and assessed methodological quality. Results The search identified 7343 articles, of which 92 were selected for full-text review. For this review, 31 articles were included. Patients undergoing spinal surgery had overly optimistic expectations regarding pain and symptom relief, they underestimated the probability of functional disability, and overestimated the probability of (complete) recovery and return to work. Studies highlighted that patients feel not adequately prepared for surgery in terms of post-treatment expectations. Similarly, advanced cancer patients receiving palliative treatment often had overly optimistic expectations regarding their survival probability and cure rates. Conclusions Patients tend to have overly optimistic expectations regarding pain and symptom relief, recovery and prognosis following spinal surgery or advanced cancer care. Pretreatment consultation about the expected pain and symptom relief, recovery and prognosis may improve understanding of prognosis, and promote and manage expectations, which, in turn, may lead to better perceived outcomes. Trial registration PROSPERO registration number: CRD42020145151.

Study Design. A prospective multicenter cohort study.Objective. To assess the clinical accuracy of six commonly cited prognostic scoring systems for patients with spinal metastases.Summary of Background Data. There are presently several available methods for the estimation of prognosis in metastatic spinal disease, but none are universally accepted by surgeons for clinical use. These scoring systems have not been rigorously tested and validated in large datasets to see if they are reliable enough to inform day-to-day patient management decisions. We tested these scoring systems in a large cohort of patients. A total of 1469 patients were recruited into a secure internet database, and prospectively collected data were analyzed to assess the accuracy of published prognostic scoring systems.Methods. We assessed six prognostic scoring systems, described by the first authors Tomita, Tokuhashi, Bauer, van der Linden, Rades, and Bollen. Kaplan-Meier survival estimates were created for different patient subgroups as described in the original publications. Harrell's C-statistic was calculated for the survival estimates, to assess the concordance between estimated and actual survival.Results. All the prognostic scoring systems tested were able to categorize patients into separate prognostic groups with different overall survivals. However none of the scores were able to achieve "good concordance" as assessed by Harrell's C-statistic. The score of Bollen and colleagues was found to be the most accurate, with a Harrell's C-statistic of 0.66.Conclusion. No prognostic scoring system was found to have a good predictive value. The scores of Bollen and Tomita were the most effective with Harrell's C-statistic of 0.66 and 0.65, respectively. Prognostic scoring systems are calculated using data from previous years, and are subject to inaccuracies as treatments advance in the interim. We suggest that other methods of assessing prognosis should be explored, such as prognostic risk calculation.