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Limited health literacy has previously been associated with increased healthcare utilization, worse general health status and self-reported health, and increased mortality. Identifying and accommodating patients with limited health literacy may offer an avenue towards mitigating adverse health outcomes and reduce unnecessary healthcare expenditure. Due to the challenges associated with implementation of lengthy health literacy assessments, the Brief Health Literacy Screening Instrument was developed. However, to our knowledge, there are no reports on the accuracy of this screening questionnaire, with or without the inclusion of sociodemographic characteristics, when predicting limited health literacy in orthopaedic spine patients.To evaluate the reliability and predictive accuracy of self-reported health literacy screening questions with and without the inclusion of sociodemographic variables in orthopaedic spine patients.Cross-sectional PATIENT SAMPLE: Patients seen at a tertiary urban academic hospital-based multi-surgeon spine center OUTCOME MEASURES: Brief Health Literacy Screening Instrument (BRIEF), and the Newest Vital Sign (NVS) health literacy assessment tool.Between December 2021 and February 2022, consecutive English-speaking patients over the age of 18 presenting as new patients to an urban, hospital-based outpatient spine clinic were approached for participation. A sociodemographic survey, the Brief Health Literacy Screening Instrument (BRIEF), and the Newest Vital Sign (NVS) Health Literacy Assessment Tool were administered verbally. Simple and multivariable logistic regression were utilized to assess the accuracy of each BRIEF question individually, and collectively, at predicting limited health literacy as defined by the NVS. Further regression analysis included sociodemographic variables (age, body mass index, race, ethnicity, highest educational degree, employment status, marital status, annual household income, insurance status, and self-reported health.A total of 262 patients [mean age (years), 57 ± 17] were included in this study. One hundred thirty-four (51%) were male, 223 (85%) were White, and 151 (58%) were married. Patient BRIEF scores were as follows: 23 (9%) limited, 43 (16%) marginal, and 196 (75%) adequate. NVS scores identified 87 (33%) patients with possible limited health literacy. BRIEF items collectively demonstrated fair accuracy in the prediction of limited health literacy (AUROC, 0.76; 95% CI, 0.70-0.82). Individually, the fourth BRIEF item ("How confident are you in filling out medical forms by yourself?") was the best predictor of limited health literacy (AUROC, 0.67; 95% CI, 0.60-0.73). The predictive accuracy of the BRIEF items, both individually and collectively, increased with the inclusion of sociodemographic variables within the logistic regression. Specific characteristics independently associated with limited health literacy were self-identified Black race, retired or disabled employment status, single or divorced marital status, high school education or below, and self-reported "poor" health.Limited health literacy has implications for patient outcomes and healthcare costs. Our results show that the BRIEF questionnaire is a low-cost screening tool that demonstrates fair predictability in determining limited health literacy within a population of spine patients. Self-reported health literacy assessments may be more feasible in daily practice and easier to implement into clinical workflow.

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.

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).From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs.A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8.Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.

Cross-sectional survey study.To determine if health literacy level is associated with patient-reported outcomes and self-reported health status among patients presenting to an academic outpatient spine center.Patient reports are critical to assessing symptom severity and treatment success in orthopaedic spine patients. Patient-reported outcome measures (PROMs) are important instruments commonly used for this purpose. However, the influence of patient health literacy on PROMs has not yet been given much consideration in spine literature.Consecutive English-speaking patients over the age of 18 years and new to our clinic verbally completed the Newest Vital Sign health literacy assessment tool and a sociodemographic survey, including self-reported health status. Additionally, seven Patient-Reported Outcomes Measurement Information System scores were extracted from patient records. Regression modeling was performed with PROMs considered as dependent variables, health literacy level as the primary predictor, and all other factors (age, sex, race, ethnicity, native English speaker, highest educational degree, grade-level reading, marital status, employment status, annual household income and type of insurance) as covariates.Among the 318 included patients, 33% had limited health literacy. Adjusted regression analysis demonstrated that patients with limited health literacy had worse PROM scores across all seven domains (Physical Function: P=0.028; Depression: P=0.035; Global Health - Physical: P=0.001; Global Health - Mental: P=0.007; Pain Interference: P=0.036; Pain Intensity: P=0.002; Anxiety: P=0.047). Additionally, patients with limited health literacy reported worse self-reported health status (P=0.001).Spine patients with limited health literacy have worse baseline PROM scores confounders and report worse general health. Further investigations are necessary to elucidate if limited health literacy is a marker or the root cause of these disparities. Findings from this study urge the consideration of patient health literacy when interpreting PROMs as well as the implications for patient assessment and discussion of treatment options.

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.

Preoperative prediction of prolonged postoperative opioid prescription helps identify patients for increased surveillance after surgery. The SORG machine learning model has been developed and successfully tested using 5,413 patients from the United States (US) to predict the risk of prolonged opioid prescription after surgery for lumbar disc herniation. However, external validation is an often-overlooked element in the process of incorporating prediction models in current clinical practice. This cannot be stressed enough in prediction models where medicolegal and cultural differences may play a major role.The authors aimed to investigate the generalizability of the US citizens prediction model SORG to a Taiwanese patient cohort.Retrospective study at a large academic medical center in Taiwan.Of 1,316 patients who were 20 years or older undergoing initial operative management for lumbar disc herniation between 2010 and 2018.The primary outcome of interest was prolonged opioid prescription defined as continuing opioid prescription to at least 90 to 180 days after the first surgery for lumbar disc herniation at our institution.Baseline characteristics were compared between the external validation cohort and the original developmental cohorts. Discrimination (area under the receiver operating characteristic curve and the area under the precision-recall curve), calibration, overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithm in the validation cohort. This study had no funding source or conflict of interests.Overall, 1,316 patients were identified with sustained postoperative opioid prescription in 41 (3.1%) patients. The validation cohort differed from the development cohort on several variables including 93% of Taiwanese patients receiving NSAIDS preoperatively compared with 22% of US citizens patients, while 30% of Taiwanese patients received opioids versus 25% in the US. Despite these differences, the SORG prediction model retained good discrimination (area under the receiver operating characteristic curve of 0.76 and the area under the precision-recall curve of 0.33) and good overall performance (Brier score of 0.028 compared with null model Brier score of 0.030) while somewhat overestimating the chance of prolonged opioid use (calibration slope of 1.07 and calibration intercept of -0.87). Decision-curve analysis showed the SORG model was suitable for clinical use.Despite differences at baseline and a very strict opioid policy, the SORG algorithm for prolonged opioid use after surgery for lumbar disc herniation has good discriminative abilities and good overall performance in a Han Chinese patient group in Taiwan. This freely available digital application can be used to identify high-risk patients and tailor prevention policies for these patients that may mitigate the long-term adverse consequence of opioid dependence: https://sorg-apps.shinyapps.io/lumbardiscopioid/.

BACKGROUND The outcome differences following surgery for an impending versus a completed pathological fracture have not been clearly defined. The purpose of the present study was to assess differences in outcomes following the surgical treatment of impending versus completed pathological fractures in patients with long-bone metastases in terms of (1) 90-day and 1-year survival and (2) intraoperative blood loss, perioperative blood transfusion, anesthesia time, duration of hospitalization, 30-day postoperative systemic complications, and reoperations. METHODS We retrospectively performed a matched cohort study utilizing a database of 1,064 patients who had undergone operative treatment for 462 impending and 602 completed metastatic long-bone fractures. After matching on 22 variables, including primary tumor, visceral metastases, and surgical treatment, 270 impending pathological fractures were matched to 270 completed pathological fractures. The primary outcome was assessed with the Cox proportional hazard model. The secondary outcomes were assessed with the McNemar test and the Wilcoxon signed-rank test. RESULTS The 90-day survival rate did not differ between the groups (HR, 1.13 [95% CI, 0.81 to 1.56]; p = 0.48), but the 1-year survival rate was worse for completed pathological fractures (46% versus 38%) (HR, 1.28 [95% CI, 1.02 to 1.61]; p = 0.03). With regard to secondary outcomes, completed pathological fractures were associated with higher intraoperative estimated blood loss (p = 0.03), a higher rate of perioperative blood transfusions (p = 0.01), longer anesthesia time (p = 0.04), and more reoperations (OR, 2.50 [95% CI, 1.92 to 7.86]; p = 0.03); no differences were found in terms of the rate of 30-day postoperative complications or the duration of hospitalization. CONCLUSIONS Patients undergoing surgery for impending pathological fractures had lower 1-year mortality rates and better secondary outcomes as compared with patients undergoing surgery for completed pathological fractures when accounting for 22 covariates through propensity matching. Patients with an impending pathological fracture appear to benefit from prophylactic stabilization as stabilizing a completed pathological fracture seems to be associated with increased mortality, blood loss, rate of blood transfusions, duration of surgery, and reoperation risk. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

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: The clinical relevance of patient-reported outcomes score changes is often unclear. Especially in patients undergoing surgery due to lower extremity metastases–where surgery is performed in the palliative setting and the goal is to optimize functional mobility, relieve pain and improve overall quality of life. This study assessed the minimal clinically important difference (MCID) of Patient-Reported Outcomes Measurement Information System (PROMIS) Pain Interference, Cancer-specific Physical Function, and Global (Physical and Mental Health) in patients treated surgically for impending or completed pathologic fractures. Methods: Patients undergoing surgery for osseous metastasis of the lower extremity because of an impending or completed pathologic fracture were consecutively enrolled in this tertiary center study. Patients completed the three PROMIS questionnaires preoperatively (n = 56) and at postoperative follow-up (n = 33) assessment one to three months later. Of the 23 patients that did not complete the postoperative survey, 5 patients died within 1–3 months and 18 patients were alive at 3-months but did not respond or show up at their postoperative consult. Thirty-one patients (94%) of the 33 included patients reported at least minimal improvement and two patients (6.1%) no change 1–3 months after the surgery based on an anchor-based approach. Results: The PROMIS MCIDs (95% confidence interval) for Pain Interference was 7.5 (3.4–12), Physical Function 4.1 (0.6–7.6), Global Physical Health 4.2 (2.0–6.6), and Global Mental Health 0.8 (−4.5–2.9). Conclusion: This prospective study successfully defined a MCID for PROMIS Pain Interference of 7.5 (3.4–12), PROMIS Physical Function of 4.1 (0.6–7.6), and Global Physical Health of 4.2 (2.0–6.6) in patients with (impending) pathological fractures due to osseous metastases in the lower extremity; no MCID could be established for PROMIS Global Mental Health. Defining a narrower MCID value for each subpopulation requires a large, prospective, multicenter study. Nevertheless, the provided MCID values allow guidance to clinicians to evaluate the impact of surgical treatment on a patient’s QoL. Level of evidence: Level II Diagnostic study.

Purpose: Painful bone metastases hamper quality of life (QoL). The aim of this prespecified secondary analysis of the PRESENT trial was to compare change in global QoL, physical functioning, emotional functioning, functional interference, and psychosocial aspects after conventional radiation therapy (cRT) versus stereotactic body RT (SBRT).Methods and Materials: A total of 110 patients were enrolled in the phase 2 randomized controlled VERTICAL trial (NCT02364115) following the " trials within cohorts" design and randomized 1:1 to cRT or SBRT. Patient-reported global QoL, physical functioning, emotional functioning, functional interference, and psychosocial aspects were assessed by the European Organization for Research and Treatment of Cancer QoL Questionnaire (QLQ) Core 15 Palliative Care and QLQ Bone Metastases 22 modules. Changes in QoL domains over time were compared between patients treated with cRT and SBRT using intention-to-treat (ITT) and per-protocol (PP) linear mixed model analysis adjusting for baseline scores. Proportions of patients in the cRT versus SBRT arm reporting a clinically relevant change in QoL within 3 months were compared using a x2 test.Results: QoL scores had improved over time and were comparable between groups for all domains in both the ITT and PP analyses, except for functional interference and psychological aspects in the ITT. Functional interference scores had improved more after 12 weeks in the cRT arm than in the SBRT arm (25.5 vs 14.1 points, respectively; effect size [ES] = 0.49, P =.04). Psychosocial aspects scores had improved more after 8 weeks in the cRT arm than in the SBRT arm (12.2 vs 7.3; ES = 0.56, P =.04). No clinically relevant differences between groups at 12 weeks in terms of global QoL, physical functioning, emotional functioning, functional interference, and psychosocial aspects were observed.Conclusions: Palliative RT improves QoL. Both SBRT and cRT have a comparable effect on patient-reported QoL outcomes in patients with painful bone metastases. Functional interference and psychological aspects scores improved more in patients treated with cRT versus patients offered SBRT. (C) 2022 Elsevier Inc. All rights reserved.

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.

STUDY DESIGN: This was a systematic review and meta-analysis. OBJECTIVE: A systematic review and meta-analysis was conducted to assess the quality of life (QoL) after open surgery for spinal metastases, and how surgery affects physical, social/family, emotional, and functional well-being. SUMMARY OF BACKGROUND DATA: It remains questionable to what extent open surgery improves QoL for metastatic spinal disease, it would be interesting to quantify the magnitude and duration of QoL benefits-if any-after surgery for spinal metastases. MATERIALS AND METHODS: Included were studies measuring QoL before and after nonpercutaneous, open surgery for spinal metastases for various indications including pain, spinal cord compression, instability, or tumor control. A random-effect model assessed standardized mean differences (SMDs) of summary QoL scores between baseline and 1, 3, 6, or 9-12 months after surgery. RESULTS: The review yielded 10 studies for data extraction. The pooled QoL summary score improved from baseline to 1 month (SMD=1.09, P

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.

Preoperative embolization (PE) reduces intraoperative blood loss during surgery for spinal metastases of hypervascular primary tumors such as thyroid and renal cell tumors. However, most spinal metastases originate from primary breast, prostate, and lung tumors and it remains unclear whether these and other spinal metastases benefit from PE.To assess the (1) efficacy of PE on the amount of intraoperative blood loss and safety in patients with spinal metastases originating from non-hypervascular primary tumors, and (2) secondary outcomes including perioperative allogeneic blood transfusion, anesthesia time, hospitalization, postoperative complication within 30 days, reoperation, 90-day mortality, and 1-year mortality.Retrospective propensity-score matched, case-control study at 2 academic tertiary medical centers.Patients 18 years of age or older undergoing surgery for spinal metastases originating from primary non-thyroid, non-renal cell, and non-hepatocellular tumors between January 1, 2002 and December 31, 2016 were included.The primary outcomes were estimated amount of intraoperative blood loss and complications attributable to PE, such as neurologic injury, wound infection, thrombosis, or dissection. The secondary outcomes included perioperative allogeneic blood transfusion, anesthesia time, hospitalization, postoperative complication within 30 days, reoperation, 90-day mortality, and 1-year mortality.In total, 495 patients were identified, of which 54 (11%) underwent PE. After propensity score matching on 21 variables, including primary tumor, number of spinal levels, and surgical treatment, 53 non-PE patients were matched to 53 PE patients. Matching was adequate measured by comparing the matched variables, testing the standardized mean differences (0.25), and inspecting Kernel density plots. The degree of embolization was noted to be complete, until stasis, or successful in 43 (80%) patients.Intraoperative blood loss did not differ between both groups with a median blood loss in liters of 0.6 (IQR, 0.4-1.2) for non-PE patients and 0.9 (IQR, 0.6-1.2) for PE patients (p=.32). No complications occurred during embolization or the time between embolization and surgery. No differences were found in terms of the secondary outcomes.Our data suggest that, although no complications occurred and the embolization procedure can be considered safe, patients with non-hypervascular spinal metastases might not benefit from PE. A larger, prospective study could confirm or refute these study findings and aid in elucidating a subset of spinal metastases that might benefit from PE.

BACKGROUND The prediction of survival is valuable to optimize treatment of metastatic long-bone disease. The Skeletal Oncology Research Group (SORG) machine-learning (ML) algorithm has been previously developed and internally validated. The purpose of this study was to determine if the SORG ML algorithm accurately predicts 90-day and 1-year survival in an external metastatic long-bone disease patient cohort. METHODS A retrospective review of 264 patients who underwent surgery for long-bone metastases between 2003 and 2019 was performed. Variables used in the stochastic gradient boosting SORG algorithm were age, sex, primary tumor type, visceral/brain metastases, systemic therapy, and 10 preoperative laboratory values. Model performance was calculated by discrimination, calibration, and overall performance. RESULTS The SORG ML algorithms retained good discriminative ability (area under the cure [AUC]: 0.83; 95% confidence interval [CI]: 0.76-0.88 for 90-day mortality and AUC: 0.84; 95% CI: 0.79-0.88 for 1-year mortality), calibration, overall performance, and decision curve analysis. CONCLUSION The previously developed ML algorithms demonstrated good performance in the current study, thereby providing external validation. The models were incorporated into an accessible application (https://sorg-apps.shinyapps.io/extremitymetssurvival/) that may be freely utilized by clinicians in helping predict survival for individual patients and assist in informative decision-making discussion before operative management of long bone metastatic lesions.

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