Validation of Ten Osteoporosis Screening Tools in Rural Communities of Taiwan.

PURPOSE: Patients with osteoporosis are at risk of fractures, which can lead to immobility and reduced quality of life. Early diagnosis and treatment are crucial for preventing fractures, but many patients are not diagnosed until after a fracture has occurred. This study aimed to evaluate the performance of 10 osteoporosis screening tools (OSTs) in rural communities of Taiwan. In this prospective study, a total of 567 senior citizens from rural communities underwent bone mineral density (BMD) measurement using dual-energy X-ray absorptiometry (DXA) and ten OSTs were administered. Discrimination analysis was performed using the area under the receiver operating characteristic curve (AUROC). Primary outcomes included area under curve (AUC) value, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The DXA examination revealed that 63.0% of females and 22.4% of males had osteoporosis. Among females, Osteoporosis Index of Risk (OSIRIS) and Osteoporosis Self-Assessment Tool for Asians (OSTA) presented the best AUC value with 0.71 (0.66-0.76) and 0.70 (0.66-0.75), respectively. Among males, BWC had the best AUC value of 0.77 (0.67-0.86), followed by OSTA, Simple Calculated Osteoporosis Risk Estimation (SCORE), and OSIRIS. OSTA and OSIRIS showed acceptable performance in both genders. The specificity of Fracture Risk Assessment Tool (FRAX-H), SCORE, National Osteoporosis Foundation Score, OSIRIS, Osteoporosis Risk Assessment Instrument, Age, Bulk, One or Never Estrogen (ABONE), and Body weight criteria increased in both genders after applying the optimum cut-off. Considering it high AUC and simplicity of use, OSTA appeared to be the recommended tool for seniors of both genders among the ten OSTs. This study provides a viable reference for future development of OSTs in Taiwan. Further adjustment according to epidemiological data and risk factors is recommended while applying OSTs to different cohorts.

Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?

BACKGROUND: Bone metastasis in advanced cancer is challenging because of pain, functional issues, and reduced life expectancy. Treatment planning is complex, with consideration of factors such as location, symptoms, and prognosis. Prognostic models help guide treatment choices, with Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) showing promise in predicting survival for initial spinal metastases and extremity metastases treated with surgery or radiotherapy. Improved therapies extend patient lifespans, increasing the risk of subsequent skeletal-related events (SREs). Patients experiencing subsequent SREs often suffer from disease progression, indicating a deteriorating condition. For these patients, a thorough evaluation, including accurate survival prediction, is essential to determine the most appropriate treatment and avoid aggressive surgical treatment for patients with a poor survival likelihood. Patients experiencing subsequent SREs often suffer from disease progression, indicating a deteriorating condition. However, some variables in the SORG prediction model, such as tumor histology, visceral metastasis, and previous systemic therapies, might remain consistent between initial and subsequent SREs. Given the prognostic difference between patients with and without a subsequent SRE, the efficacy of established prognostic models-originally designed for individuals with an initial SRE-in addressing a subsequent SRE remains uncertain. Therefore, it is crucial to verify the model's utility for subsequent SREs. QUESTION/PURPOSE: We aimed to evaluate the reliability of the SORG-MLAs for survival prediction in patients undergoing surgery or radiotherapy for a subsequent SRE for whom both the initial and subsequent SREs occurred in the spine or extremities. METHODS: We retrospectively included 738 patients who were 20 years or older who received surgery or radiotherapy for initial and subsequent SREs at a tertiary referral center and local hospital in Taiwan between 2010 and 2019. We excluded 74 patients whose initial SRE was in the spine and in whom the subsequent SRE occurred in the extremities and 37 patients whose initial SRE was in the extremities and the subsequent SRE was in the spine. The rationale was that different SORG-MLAs were exclusively designed for patients who had an initial spine metastasis and those who had an initial extremity metastasis, irrespective of whether they experienced metastatic events in other areas (for example, a patient experiencing an extremity SRE before his or her spinal SRE would also be regarded as a candidate for an initial spinal SRE). Because these patients were already validated in previous studies, we excluded them in case we overestimated our result. Five patients with malignant primary bone tumors and 38 patients in whom the metastasis's origin could not be identified were excluded, leaving 584 patients for analysis. The 584 included patients were categorized into two subgroups based on the location of initial and subsequent SREs: the spine group (68% [399]) and extremity group (32% [185]). No patients were lost to follow-up. Patient data at the time they presented with a subsequent SRE were collected, and survival predictions at this timepoint were calculated using the SORG-MLAs. Multiple imputation with the Missforest technique was conducted five times to impute the missing proportions of each predictor. The effectiveness of SORG-MLAs was gauged through several statistical measures, including discrimination (measured by the area under the receiver operating characteristic curve [AUC]), calibration, overall performance (Brier score), and decision curve analysis. Discrimination refers to the model's ability to differentiate between those with the event and those without the event. An AUC ranges from 0.5 to 1.0, with 0.5 indicating the worst discrimination and 1.0 indicating perfect discrimination. An AUC of 0.7 is considered clinically acceptable discrimination. Calibration is the comparison between the frequency of observed events and the predicted probabilities. In an ideal calibration, the observed and predicted survival rates should be congruent. The logarithm of observed-to-expected survival ratio [log(O:E)] offers insight into the model's overall calibration by considering the total number of observed (O) and expected (E) events. The Brier score measures the mean squared difference between the predicted probability of possible outcomes for each individual and the observed outcomes, ranging from 0 to 1, with 0 indicating perfect overall performance and 1 indicating the worst performance. Moreover, the prevalence of the outcome should be considered, so a null-model Brier score was also calculated by assigning a probability equal to the prevalence of the outcome (in this case, the actual survival rate) to each patient. The benefit of the prediction model is determined by comparing its Brier score with that of the null model. If a prediction model's Brier score is lower than the null model's Brier score, the prediction model is deemed as having good performance. A decision curve analysis was performed for models to evaluate the "net benefit," which weighs the true positive rate over the false positive rate against the "threshold probabilities," the ratio of risk over benefit after an intervention was derived based on a comprehensive clinical evaluation and a well-discussed shared-decision process. A good predictive model should yield a higher net benefit than default strategies (treating all patients and treating no patients) across a range of threshold probabilities. RESULTS: For the spine group, the algorithms displayed acceptable AUC results (median AUCs of 0.69 to 0.72) for 42-day, 90-day, and 1-year survival predictions after treatment for a subsequent SRE. In contrast, the extremity group showed median AUCs ranging from 0.65 to 0.73 for the corresponding survival periods. All Brier scores were lower than those of their null model, indicating the SORG-MLAs' good overall performances for both cohorts. The SORG-MLAs yielded a net benefit for both cohorts; however, they overestimated 1-year survival probabilities in patients with a subsequent SRE in the spine, with a median log(O:E) of -0.60 (95% confidence interval -0.77 to -0.42). CONCLUSION: The SORG-MLAs maintain satisfactory discriminatory capacity and offer considerable net benefits through decision curve analysis, indicating their continued viability as prediction tools in this clinical context. However, the algorithms overestimate 1-year survival rates for patients with a subsequent SRE of the spine, warranting consideration of specific patient groups. Clinicians and surgeons should exercise caution when using the SORG-MLAs for survival prediction in these patients and remain aware of potential mispredictions when tailoring treatment plans, with a preference for less invasive treatments. Ultimately, this study emphasizes the importance of enhancing prognostic algorithms and developing innovative tools for patients with subsequent SREs as the life expectancy in patients with bone metastases continues to improve and healthcare providers will encounter these patients more often in daily practice. LEVEL OF EVIDENCE: Level III, prognostic study.

Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm.

BACKGROUND: The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA) was developed to predict the survival of patients with spinal metastasis. The algorithm was successfully tested in five international institutions using 1101 patients from different continents. The incorporation of 18 prognostic factors strengthens its predictive ability but limits its clinical utility because some prognostic factors might not be clinically available when a clinician wishes to make a prediction. QUESTIONS/PURPOSES: We performed this study to (1) evaluate the SORG-MLA's performance with data and (2) develop an internet-based application to impute the missing data. METHODS: A total of 2768 patients were included in this study. The data of 617 patients who were treated surgically were intentionally erased, and the data of the other 2151 patients who were treated with radiotherapy and medical treatment were used to impute the artificially missing data. Compared with those who were treated nonsurgically, patients undergoing surgery were younger (median 59 years [IQR 51 to 67 years] versus median 62 years [IQR 53 to 71 years]) and had a higher proportion of patients with at least three spinal metastatic levels (77% [474 of 617] versus 72% [1547 of 2151]), more neurologic deficit (normal American Spinal Injury Association [E] 68% [301 of 443] versus 79% [1227 of 1561]), higher BMI (23 kg/m2 [IQR 20 to 25 kg/m2] versus 22 kg/m2 [IQR 20 to 25 kg/m2]), higher platelet count (240 × 103/µL [IQR 173 to 327 × 103/µL] versus 227 × 103/µL [IQR 165 to 302 × 103/µL], higher lymphocyte count (15 × 103/µL [IQR 9 to 21× 103/µL] versus 14 × 103/µL [IQR 8 to 21 × 103/µL]), lower serum creatinine level (0.7 mg/dL [IQR 0.6 to 0.9 mg/dL] versus 0.8 mg/dL [IQR 0.6 to 1.0 mg/dL]), less previous systemic therapy (19% [115 of 617] versus 24% [526 of 2151]), fewer Charlson comorbidities other than cancer (28% [170 of 617] versus 36% [770 of 2151]), and longer median survival. The two patient groups did not differ in other regards. These findings aligned with our institutional philosophy of selecting patients for surgical intervention based on their level of favorable prognostic factors such as BMI or lymphocyte counts and lower levels of unfavorable prognostic factors such as white blood cell counts or serum creatinine level, as well as the degree of spinal instability and severity of neurologic deficits. This approach aims to identify patients with better survival outcomes and prioritize their surgical intervention accordingly. Seven factors (serum albumin and alkaline phosphatase levels, international normalized ratio, lymphocyte and neutrophil counts, and the presence of visceral or brain metastases) were considered possible missing items based on five previous validation studies and clinical experience. Artificially missing data were imputed using the missForest imputation technique, which was previously applied and successfully tested to fit the SORG-MLA in validation studies. Discrimination, calibration, overall performance, and decision curve analysis were applied to evaluate the SORG-MLA's performance. The discrimination ability was measured with an area under the receiver operating characteristic curve. It ranges from 0.5 to 1.0, with 0.5 indicating the worst discrimination and 1.0 indicating perfect discrimination. An area under the curve of 0.7 is considered clinically acceptable discrimination. Calibration refers to the agreement between the predicted outcomes and actual outcomes. An ideal calibration model will yield predicted survival rates that are congruent with the observed survival rates. The Brier score measures the squared difference between the actual outcome and predicted probability, which captures calibration and discrimination ability simultaneously. A Brier score of 0 indicates perfect prediction, whereas a Brier score of 1 indicates the poorest prediction. A decision curve analysis was performed for the 6-week, 90-day, and 1-year prediction models to evaluate their net benefit across different threshold probabilities. Using the results from our analysis, we developed an internet-based application that facilitates real-time data imputation for clinical decision-making at the point of care. This tool allows healthcare professionals to efficiently and effectively address missing data, ensuring that patient care remains optimal at all times. RESULTS: Generally, the SORG-MLA demonstrated good discriminatory ability, with areas under the curve greater than 0.7 in most cases, and good overall performance, with up to 25% improvement in Brier scores in the presence of one to three missing items. The only exceptions were albumin level and lymphocyte count, because the SORG-MLA's performance was reduced when these two items were missing, indicating that the SORG-MLA might be unreliable without these values. The model tended to underestimate the patient survival rate. As the number of missing items increased, the model's discriminatory ability was progressively impaired, and a marked underestimation of patient survival rates was observed. Specifically, when three items were missing, the number of actual survivors was up to 1.3 times greater than the number of expected survivors, while only 10% discrepancy was observed when only one item was missing. When either two or three items were omitted, the decision curves exhibited substantial overlap, indicating a lack of consistent disparities in performance. This finding suggests that the SORG-MLA consistently generates accurate predictions, regardless of the two or three items that are omitted. We developed an internet application (https://sorg-spine-mets-missing-data-imputation.azurewebsites.net/) that allows the use of SORG-MLA with up to three missing items. CONCLUSION: The SORG-MLA generally performed well in the presence of one to three missing items, except for serum albumin level and lymphocyte count (which are essential for adequate predictions, even using our modified version of the SORG-MLA). We recommend that future studies should develop prediction models that allow for their use when there are missing data, or provide a means to impute those missing data, because some data are not available at the time a clinical decision must be made. CLINICAL RELEVANCE: The results suggested the algorithm could be helpful when a radiologic evaluation owing to a lengthy waiting period cannot be performed in time, especially in situations when an early operation could be beneficial. It could help orthopaedic surgeons to decide whether to intervene palliatively or extensively, even when the surgical indication is clear.

The Skeletal Oncology Research Group Machine Learning Algorithm (SORG-MLA) for predicting prolonged postoperative opioid prescription after total knee arthroplasty: an international validation study using 3,495 patients from a Taiwanese cohort.

BACKGROUND: Preoperative prediction of prolonged postoperative opioid use (PPOU) after total knee arthroplasty (TKA) could identify high-risk patients for increased surveillance. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) has been tested internally while lacking external support to assess its generalizability. The aims of this study were to externally validate this algorithm in an Asian cohort and to identify other potential independent factors for PPOU. METHODS: In a tertiary center in Taiwan, 3,495 patients receiving TKA from 2010-2018 were included. Baseline characteristics were compared between the external validation cohort and the original developmental cohorts. Discrimination (area under receiver operating characteristic curve [AUROC] and precision-recall curve [AUPRC]), calibration, overall performance (Brier score), and decision curve analysis (DCA) were applied to assess the model performance. A multivariable logistic regression was used to evaluate other potential prognostic factors. RESULTS: There were notable differences in baseline characteristics between the validation and the development cohort. Despite these variations, the SORG-MLA ( https://sorg-apps.shinyapps.io/tjaopioid/ ) remained its good discriminatory ability (AUROC, 0.75; AUPRC, 0.34) and good overall performance (Brier score, 0.029; null model Brier score, 0.032). The algorithm could bring clinical benefit in DCA while somewhat overestimating the probability of prolonged opioid use. Preoperative acetaminophen use was an independent factor to predict PPOU (odds ratio, 2.05). CONCLUSIONS: The SORG-MLA retained its discriminatory ability and good overall performance despite the different pharmaceutical regulations. The algorithm could be used to identify high-risk patients and tailor personalized prevention policy.

External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort.

BACKGROUND/PURPOSE: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment plans. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was developed to predict the risk of prolonged opioid use in opioid-naive patients after lumbar spine surgery. However, its utility in a distinct country remains unknown. METHODS: A Taiwanese cohort containing 2795 patients who were 20 years or older undergoing primary surgery for lumbar decompression from 2010 to 2018 were used to validate the SORG-MLA. Discrimination (area under receiver operating characteristic curve [AUROC] and area under precision-recall curve [AUPRC]), calibration, overall performance (Brier score), and decision curve analysis were applied. RESULTS: Among 2795 patients, the prolonged opioid prescription rate was 5.2%. The validation cohort were older, more inpatient disposition, and more common pharmaceutical history of NSAIDs. Despite the differences, the SORG-MLA provided a good discriminative ability (AUROC of 0.71 and AURPC of 0.36), a good overall performance (Brier score of 0.044 compared to that of 0.039 in the developmental cohort). However, the probability of prolonged opioid prescription tended to be overestimated (calibration intercept of -0.07 and calibration slope of 1.45). Decision curve analysis suggested greater clinical net benefit in a wide range of clinical scenarios. CONCLUSION: The SORG-MLA retained good discriminative abilities and overall performances in a geologically and medicolegally different region. It was suitable for predicting patients in risk of prolonged postoperative opioid use in Taiwan.

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment.

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.

Can a Bayesian belief network for survival prediction in patients with extremity metastases (PATHFx) be externally validated in an Asian cohort of 356 surgically treated patients?

BACKGROUND AND PURPOSE: Predicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis. PATIENTS AND METHODS: We included 356 patients who underwent surgery for skeletal extremity metastasis in a tertiary center in Taiwan between 2014 and 2019 to validate PATHFx's survival predictions at 6 different time points. Model performance was assessed by concordance index (c-index), calibration analysis, decision curve analysis (DCA), Brier score, and model consistency (MC). RESULTS: The c-indexes for the 1-, 3-, 6-, 12-, 18-, and 24-month survival estimations were 0.71, 0.66, 0.65, 0.69, 0.68, and 0.67, respectively. The calibration analysis demonstrated positive calibration intercepts for survival predictions at all 6 timepoints, indicating PATHFx tended to underestimate the actual survival. The Brier scores for the 6 models were all less than their respective null model's. DCA demonstrated that only the 6-, 12-, 18-, and 24-month predictions appeared useful for clinical decision-making across a wide range of threshold probabilities. The MC was < 0.9 when the 6- and 12-month models were compared with the 12-month and 18-month models, respectively. INTERPRETATION: In this Asian cohort, PATHFx's performance was not as encouraging as those of prior validation studies. Clinicians should be cognizant of the potential decline in validity of any tools designed using data outside their particular patient population. Developers of survival prediction tools such as PATHFx might refine their algorithms using data from diverse, contemporary patients that is more reflective of the world's population.

Impact of cervical sagittal parameters and spinal cord morphology in cervical spondylotic myelopathy status post spinous process-splitting laminoplasty.

PURPOSE: No standard strategy exists for managing cervical spondylotic myelopathy (CSM). The efficacy of spinous process-splitting laminoplasty, its impact on cervical alignment change and the incidence of postoperative neck pain remain unclear. We analyzed the parameters of cervical alignment and cord morphology in CSM. METHODS: The radiographic parameters investigated were pre- and postoperative C2-C7 lordosis (CL), C2-C7 sagittal vertical axis (CSVA), T1 slope (TS), TS minus CL (TS - CL) and cervical spinal cord morphology. Myelopathy severity was measured using two different functional scores. Statistical analysis was performed to determine significant differences between preoperative and follow-up radiological findings and change in functional scores. RESULTS: This retrospective study comprised 85 CSM patients from a single institute, with a minimum follow-up of 24 months. Overall, 63.5% (n = 54) of patients had improvement in their postoperative cervical lordotic alignment; 36.5% (n = 31) developed progressive aggravation of the cervical kyphotic alignment. Pearson correlation analysis showed that CSVA, TS and T1-CL were independent predictors of CL curve change. Based on the receiver operating characteristic curve, the cutoff value for CSVA was 2.89 cm with a postoperative visual analog scale (VAS) > 4. The cutoff value of the TS - CL was 20 degrees with a postoperative VAS > 4. CSVA, TS and TS - CL had a significant association with variation in CL. CSVA and TS - CL had a significant association with postoperative neck pain. CONCLUSIONS: CSVA, T1 slope and T1-CL are good predictors of postoperative degenerative kyphotic change and neck pain. Careful consideration of their preoperative cutoff values can improve postoperative outcomes. LEVEL OF EVIDENCE: IV. These slides can be retrieved under Electronic Supplementary Material.

Primary intradural extramedullary spinal mesenchymal chondrosarcoma: case report and literature review.

BACKGROUND: Mesenchymal chondrosarcoma (MCS) is a rare malignant variant of chondrosarcoma with a high tendency of recurrence and metastasis. Intradural extramedullary spinal MCS is exceedingly rare and usually found in pediatric patients. Herein, we present an elderly patient with primary intradural extramedullary spinal MCS. Relevant literatures are reviewed to disclose characteristics of intradural extramedullary spinal MCS. CASE PRESENTATION: A 64-year-old female presented with urinary difficulty and tightness of upper back preceding progressive weakness of right lower extremity. Magnetic resonance imaging revealed an intradural extramedullary tumor at the level of 3rd thoracic vertebra. This patient underwent total tumor resection and then received adjuvant radiotherapy. Histopathological examination showed that the tumor composed of spindle and round cells with high nucleocytoplasmic ratio accompanied by scattered eosinophilic chondroid matrix. Along with immunohistochemical findings and the existence of HEY1-NCOA2 fusion transcript, the diagnosis of MCS was confirmed. Neurologic deficit recovered nearly completely after surgery. No evidence of local recurrence or distant metastasis was found 5 years after treatments. Including the current case, a total of 18 cases have been reported in the literature with only one case with local recurrence and one case of mortality. The current case was the eldest patient diagnosed with primary intraspinal MCS in the literature. CONCLUSIONS: MCS rarely appears in the intradural space of the spine. In contrast to classic MCS, treatment outcome of primary intradural extramedullary spinal MCS is usually excellent as total tumor resection is commonly achievable. Adjuvant radiotherapy may reduce local recurrence and chemotherapy may be associated with fewer recurrences especially for unresectable tumors.

Risk Factors of Failed Conservative Treatment for Adjacent Vertebral Fractures Following Percutaneous Vertebroplasty.

STUDY DESIGN: A retrospective, single-center, observational study. OBJECTIVE: This study investigated the risk factors associated with the failure of conservative treatment for adjacent vertebral fractures (AVFs). SUMMARY OF BACKGROUND DATA: Adjacent vertebral fractures following vertebroplasty for osteoporotic vertebral compression fractures are not uncommon. Presently, there is a lack of consensus regarding the management of adjacent vertebral fractures. METHODS: We included patients who developed adjacent vertebral fractures within two years post single-level vertebroplasty between January 2013 and December 2020. All patients initially underwent six weeks of conservative treatment, including pain medications, bracing, and physical therapy. Surgical intervention was offered to those with intractable back pain due to AVFs. Baseline demographics, AVF characteristics, and radiological measurements were systematically collected, and sequential univariable and multivariable logistic regression analyses were conducted to explore the risk factors. RESULTS: Of the 114 patients with a mean age of 78.6 years, two-thirds (76 patients) tolerated conservative treatment well, while 38 required surgical interventions for adjacent vertebral fractures. Both groups demonstrated similar baseline demographics and radiological parameters regarding AVFs (P>0.05). The multivariable logistic regression analyses revealed that the development of AVFs later than six months post-vertebroplasty and their caudal location to the index vertebroplasty were the independent risk factors of unsuccessful conservative treatment, with odds ratios of 3.57 (95% confidence interval [CI]: 1.14-11.1, P=0.029) and 2.50 (95% CI: 1.09-5.88, P=0.032), respectively. CONCLUSION: Adjacent vertebral fractures following percutaneous vertebroplasty generally have favorable outcomes under conservative treatment. However, the timing and the relative anatomical location of adjacent vertebral fractures are associated with treatment efficacy. Adjacent vertebral fractures occurring later than six months following the initial vertebroplasty or situated in the caudal location to the index vertebroplasty may exhibit reduced responsiveness to conservative treatment. These patients might benefit from a more aggressive therapeutic approach. LEVEL OF EVIDENCE: 3.

Risk Factors for Adjacent Vertebral Fractures Following Cement Vertebroplasty: The Clinical Significance of Multiple Preexisting Vertebral Compression Fractures.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The study retrospectively analyzed the factors associated with the development of adjacent vertebral fractures. SUMMARY OF BACKGROUND DATA: Adjacent vertebral fractures (AVF) may occur following cement vertebroplasty, and several risk factors have been reported with controversies. METHODS: A total of 123 patients, with a mean age of 79.2 years, who underwent single-level vertebroplasty were included in the investigation. We systematically collected data encompassing baseline demographics, osteoporosis parameters, surgical details, radiologic measurements, and Hounsfield unit (HU) values in the lumbar spine. Subsequently, univariable, followed by multivariable logistic regression analyses, were employed to identify the risk factors of AVFs. RESULTS: Thirty of 123 patients had AVFs within 6 months following vertebroplasty. The AVF group exhibited a higher percentage of multiple preexisting vertebral compression fractures (P=0.006), a greater volume of injected cement (P=0.032), and a more pronounced reduction in local kyphosis (P=0.007). Multivariable logistic regression analysis revealed multiple preexisting vertebral compression fractures and a reduction in local kyphosis exceeding 8 degrees were independent risk factors for AVFs (P=0.008 and 0.003, respectively), with odds ratios of 3.78 (95% confidence interval: 1.41-10.12) and 4.16 (95% CI: 1.65-10.50), respectively. Subgroup analysis showed that patients with multiple preexisting vertebral compression fractures (VCFs) had significantly lower bone mineral density Z-score, T-score, and HU values compared with those without preexisting VCFs (P<0.05). Conversely, there were no significant differences in T-score or HU values between patients with no VCFs and those with a single VCF. CONCLUSION: This study demonstrated that both bone strength and local alignment are key factors associated with adjacent vertebral fractures. Specifically, having multiple preexisting vertebral compression fractures and a reduction in local kyphosis exceeding 8 degrees are independent risk factors. The presence of more than one previous vertebral compression fracture serves as a significant clinical indicator of advanced bone density reduction in patients with osteoporosis, offering a quick and straightforward method for identifying high-risk patients. Patients exhibiting these risk factors should be monitored more closely for favorable clinical outcomes. LEVEL OF EVIDENCE: Level III-retrospective nonexperimental study.

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long-bone metastases.

BACKGROUND: Predictive analytics is gaining popularity as an aid to treatment planning for patients with bone metastases, whose expected survival should be considered. Decreased psoas muscle area (PMA), a morphometric indicator of suboptimal nutritional status, has been associated with mortality in various cancers, but never been integrated into current survival prediction algorithms (SPA) for patients with skeletal metastases. This study investigates whether decreased PMA predicts worse survival in patients with extremity metastases and whether incorporating PMA into three modern SPAs (PATHFx, SORG-NG, and SORG-MLA) improves their performance. METHODS: One hundred eighty-five patients surgically treated for long-bone metastases between 2014 and 2019 were divided into three PMA tertiles (small, medium, and large) based on their psoas size on CT. Kaplan-Meier, multivariable regression, and Cox proportional hazards analyses were employed to compare survival between tertiles and examine factors associated with mortality. Logistic regression analysis was used to assess whether incorporating adjusted PMA values enhanced the three SPAs' discriminatory abilities. The clinical utility of incorporating PMA into these SPAs was evaluated by decision curve analysis (DCA). RESULTS: Patients with small PMA had worse 90-day and 1-year survival after surgery (log-rank test p < 0.001). Patients in the large PMA group had a higher chance of surviving 90 days (odds ratio, OR, 3.72, p = 0.02) and 1 year than those in the small PMA group (OR 3.28, p = 0.004). All three SPAs had increased AUC after incorporation of adjusted PMA. DCA indicated increased net benefits at threshold probabilities >0.5 after the addition of adjusted PMA to these SPAs. CONCLUSIONS: Decreased PMA on CT is associated with worse survival in surgically treated patients with extremity metastases, even after controlling for three contemporary SPAs. Physicians should consider the additional prognostic value of PMA on survival in patients undergoing consideration for operative management due to extremity metastases.

Clinical, oncological, and prognostic differences of patients with subsequent skeletal-related events in bone metastases.

Aims: Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments. Methods: This retrospective study included 3,814 adult patients who received local treatment - surgery and/or radiotherapy - for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher's exact test, and Kaplan-Meier curve. Results: Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians' experiences when treating the initial SRE are not similar when treating a subsequent SRE. Conclusion: This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival prediction models for better management of subsequent SREs.

Comparison of Classically and Machine Learning Generated Survival Prediction Models for Patients With Spinal Metastasis - A meta-Analysis of Two Recently Developed Algorithms.

STUDY DESIGN: A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study. OBJECTIVE: (1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input. METHODS: After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC). RESULTS: The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (P < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA. CONCLUSION: These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.

Indications for and Outcomes of Three Unilateral Biportal Endoscopic Approaches for the Decompression of Degenerative Lumbar Spinal Stenosis: A Systematic Review.

OBJECTIVE: In this systematic review, we summarized the indications for and outcomes of three main unilateral biportal endoscopic (UBE) approaches for the decompression of degenerative lumbar spinal stenosis (DLSS). METHODS: A comprehensive search of the literature was performed using Ovid Embase, PubMed, Web of Science, and Ovid's Cochrane Library. The following information was collected: surgical data; patients' scores on the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and Macnab criteria; and surgical complications. RESULTS: In total, 23 articles comprising 7 retrospective comparative studies, 2 prospective comparative studies, 12 retrospectives case series, and 2 randomized controlled trials were selected for quantitative analysis. The interlaminar approach for central and bilateral lateral recess stenoses, contralateral approach for isolated lateral recess stenosis, and paraspinal approach for foraminal stenosis were used in 16, 2, and 4 studies, respectively. In one study, both interlaminar and contralateral approaches were used. L4-5 was the most common level decompressed using the interlaminar and contralateral approaches, whereas L5-S1 was the most common level decompressed using the paraspinal approach. All three approaches provided favorable clinical outcomes at the final follow-up, with considerable improvements in patients' VAS scores for leg pain (63.6-73.5%) and ODI scores (67.2-71%). The overall complication rate was <6%. CONCLUSIONS: The three approaches of UBE surgery are effective and safe for the decompression of various types of DLSS. In the future, long-term prospective studies and randomized control trials are warranted to explore this new technique further and to compare it with conventional surgical techniques.

Distal Junctional Kyphosis after Posterior Spinal Fusion in Lenke 1 and 2 Adolescent Idiopathic Scoliosis-Exploring Detailed Features of the Sagittal Stable Vertebra Concept.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: To investigate the factors contributing to the development of postoperative distal junctional kyphosis (DJK) in adolescent idiopathic scoliosis (AIS) patients who underwent posterior spinal fusion (PSF) with lowest instrumented vertebrae (LIV) at or above L1. METHODS: Patients with Lenke type 1 or 2 curves who underwent PSF with LIV at or above L1 with a minimum follow-up of 2 years were evaluated. The primary outcome measure was the occurrence of postoperative DJK. Radiographic parameters of sagittal alignment and inclusion/exclusion of sagittal stable vertebra (SSV) in PSF were analyzed to determine their associations with the occurrence of postoperative DJK. RESULTS: Overall, 122 patients (mean age: 15.1 ± 3.2 years) were included. The overall incidence of postoperative DJK was 6.6%. DJK was observed in 19.0% (8/42) of patients whose SSV was not included in PSF and not in patients with SSV included in PSF (n = 80). In the SSV-excluded group, univariate analysis found two significant risk factors for DJK: postoperative thoracic kyphosis (TK, T5-12) and postoperative thoracolumbar kyphosis (TLK, T11-L2). The ROC curve revealed that postoperative TK ≥ 25° and TLK ≥ 10° best predicted the occurrence of postoperative DJK in the SSV-excluded group. The incidence was significantly higher in cases with postoperative TK ≥ 25° or TLK ≥ 10° (7/13 = 53.8%) than in those with postoperative TK < 25° and TLK < 10° (1/29 = 3.4%). CONCLUSIONS: The current study revealed that postoperative TK ≥ 25° or postoperative TLK ≥ 10° with SSV excluded from PSF were related to DJK after PSF for Lenke type 1 and type 2 AIS. When the SSV is intended to be spared from PSF to save more motion segments, TK and TLK should be carefully evaluated and attained in a lesser magnitude (TK < 25°, TLK < 10°) after surgery.

The Outcome of under 10 mm Single-Incision Surgery Using a Non-Specialized Volar Plate in Distal Radius Fractures: A Retrospective Comparative Study.

BACKGROUND: The distal radius fracture is a common orthopedic injury. We aimed to share the surgical steps and investigate the outcomes of treating distal radius fractures with wounds ≤10 mm using a globally accessible locking plate. METHODS: We collected 46 patients who underwent surgery via a <10 mm wound, with a control group consisting of 40 patients who underwent conventional procedures. Both groups were treated using the same volar plate. We compared the radiographic reduction quality, including volar tilt angle, radial inclination angle, and ulna variance. Additionally, clinical outcomes, such as pain assessed using VAS, Q-Dash score, and PRWE, were evaluated. Patient satisfaction with the wound was also analyzed. The follow-up time for the clinical outcomes was 24.2 ± 13.47 months. RESULTS: There were no differences in the quality of reduction in parameters such as the volar tilt angle (p = 0.762), radial inclination angle (p = 0.986), and ulna variance (p = 0.166). Both groups exhibited comparable results in pain VAS (p = 0.684), Q-Dash score (p = 0.08), and PRWE (p = 0.134). The ≤10 mm incision group displayed an increase in satisfaction with the wound (p < 0.001). CONCLUSIONS: Treating distal radius fractures with a <10 mm wound using a non-specialized locking plate is a feasible approach. It does not compromise the quality of fracture reduction or functional scores and improves wound satisfaction.

A Machine Learning Algorithm for Predicting 6-Week Survival in Spinal Metastasis: An External Validation Study Using 2,768 Taiwanese Patients.

INTRODUCTION: There are predictive algorithms for predicting 3-month and 1-year survival in patients with spinal metastasis. However, advance in surgical technique, immunotherapy, and advanced radiation therapy has enabled shortening of postoperative recovery, which returns dividends to the overall quality-adjusted life-year. As such, the Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was proposed to predict 6-week survival in patients with spinal metastasis, whereas its utility for patients treated with nonsurgical treatment was untested externally. This study aims to validate the survival prediction of the 6-week SORG-MLA for patients with spinal metastasis and provide the measurement of model consistency (MC). METHODS: Discrimination using area under the receiver operating characteristic curve, calibration, Brier score, and decision curve analysis were conducted to assess the model's performance in the Taiwanese-based cohort. MC was also applied to detect the proportion of paradoxical predictions among 6-week, 3-month, and 1-year survival predictions. The long-term prognosis should not be better than the shorter-term prognosis in that of an individual. RESULTS: The 6-week survival rate was 84.2%. The SORG-MLA retained good discrimination with an area under the receiver operating characteristic curve of 0.78 (95% confidence interval, 0.75 to 0.80) and good prediction accuracy with a Brier score of 0.11 (null model Brier score 0.13). There is an underestimation of the 6-week survival rate when the predicted survival rate is less than 50%. Decision curve analysis showed that the model was suitable for use over all threshold probabilities. MC showed suboptimal consistency between 6-week and 90-day survival prediction (78%). CONCLUSIONS: The results of this study supported the utility of the algorithm. The online tool ( https://sorg-apps.shinyapps.io/spinemetssurvival/ ) can be used by both clinicians and patients in informative decision-making discussion before management of spinal metastasis.