Transcriptional Profiling Supports the Notochordal Origin of Chordoma and Its Dependence on a TGFB1-TBXT Network.

Chordoma is a rare malignant tumor demonstrating notochordal differentiation. It is dependent on brachyury (TBXT), a hallmark notochordal gene and transcription factor, and shares histologic features and the same anatomic location as the notochord. This study involved a molecular comparison of chordoma and notochord to identify dysregulated cellular pathways. The lack of a molecular reference from appropriate control tissue limits our understanding of chordoma and its relationship to notochord. Therefore, an unbiased comparison of chordoma, human notochord, and an atlas of normal and cancerous tissue was conducted using gene expression profiling to clarify the chordoma/notochord relationship and potentially identify novel drug targets. The study found striking consistency in gene expression profiles between chordoma and notochord, supporting the hypothesis that chordoma develops from notochordal remnants. A 12-gene diagnostic chordoma signature was identified and the TBXT/transforming growth factor beta (TGF-ß)/SOX6/SOX9 pathway was hyperactivated in the tumor, suggesting that pathways associated with chondrogenesis were a central driver of chordoma development. Experimental validation in chordoma cells confirmed these findings and emphasized the dependence of chordoma proliferation and survival on TGF-ß. The computational and experimental evidence provided the first molecular connection between notochord and chordoma and identified core members of a chordoma regulatory pathway involving TBXT. This pathway provides new therapeutic targets for this unique malignant neoplasm and highlights TGF-ß as a prime druggable candidate.

Factors associated with skeletal-related events in patients with bone metastatic melanoma: A retrospective study of 481 patients.

OBJECTIVES: Metastatic bone disease is estimated to develop in up to 17% of patients with melanoma, compromising skeleton integrity resulting in skeletal-related events (SREs), which impair quality of life and reduce survival. The objective of the study was to investigate (1) the proportion of melanoma patients developing SREs following diagnosis of bone metastasis and (2) the predictors for SREs in this patient cohort. METHODS: Four hundred and eighty-one patients with bone metastatic melanoma from two tertiary centers in the United States from 2008 to 2018 were included. The primary outcome was 90-day and 1-year occurrence of a SRE, including pathological fractures of bones, cord compression, hypercalcemia, radiotherapy, and surgery. Fine-Gray regression analysis was performed for overall SREs and pathological fracture, with death as a competing risk. RESULTS: By 1-year, 52% (258/481) of patients experienced SREs, and 28% (137/481) had a pathological fracture. At 90-day, lytic lesions, bone pain, elevated calcium and absolute lymphocyte, and decreased albumin and hemoglobin were associated with higher SRE risk. The same factors, except for decreased hemoglobin, were shown to predict development of SREs at 1-year. CONCLUSION: The high incidence of SREs and pathological fractures warrants vigilance using the identified factors in this study and preventative measures during clinical oncological care.

Are Commonly Used Geographically Based Social Determinant of Health Indices in Orthopaedic Surgery Research Correlated With Each Other and With PROMIS Global-10 Physical and Mental Health Scores?

BACKGROUND: Geographically based social determinants of health (SDoH) measures are useful in research and policy aimed at addressing health disparities. In the United States, the Area Deprivation Index (ADI), Neighborhood Stress Score (NSS), and Social Vulnerability Index (SVI) are frequently used, but often without a clear reason as to why one is chosen over another. There is limited evidence about how strongly correlated these geographically based SDoH measures are with one another. Further, there is a paucity of research examining their relationship with patient-reported outcome measures (PROMs) in orthopaedic patients. Such insights are important in order to determine whether comparisons of policies and care programs using different geographically based SDoH indices to address health disparities in orthopaedic surgery are appropriate. QUESTIONS/PURPOSES: Among new patients seeking care at an orthopaedic surgery clinic, (1) what is the correlation of the NSS, ADI, and SVI with one another? (2) What is the correlation of Patient-Reported Outcomes Measurement Information System (PROMIS) Global-10 physical and mental health scores and the NSS, ADI, and SVI? (3) Which geographically based SDoH index or indices are associated with presenting PROMIS Global-10 physical and mental health scores when accounting for common patient-level sociodemographic factors? METHODS: New adult orthopaedic patient encounters at clinic sites affiliated with a tertiary referral academic medical center between 2016 and 2021 were identified, and the ADI, NSS, and SVI were determined. Patients also completed the PROMIS Global-10 questionnaire as part of routine care. Overall, a total of 75,335 new patient visits were noted. Of these, 62% (46,966 of 75,335) of new patient visits were excluded because of missing PROMIS Global-10 physical and mental health scores. An additional 2.2% of patients (1685 of 75,335) were excluded because they were missing at least one SDoH index at the time of their visit (for example, if a patient only had a Post Office box listed, the SDoH index could not be determined). This left 35% of the eligible new patient visits (26,684 of 75,335) in our final sample. Though only 35% of possible new patient visits were included, the diversity of these individuals across numerous characteristics and the wide range of sociodemographic status-as measured by the SDoH indices-among included patients supports the generalizability of our sample. The mean age of patients in our sample was 55 ± 18 years and a slight majority were women (54% [14,366 of 26,684]). Among the sample, 16% (4381of 26,684) of patients were of non-White race. The mean PROMIS Global-10 physical and mental health scores were 43.4 ± 9.4 and 49.7 ± 10.1, respectively. Spearman correlation coefficients were calculated among the three SDoH indices and between each SDoH index and PROMIS Global-10 physical and mental health scores. In addition, regression analysis was used to assess the association of each SDoH index with presenting functional and mental health, accounting for key patient characteristics. The strength of the association between each SDoH index and PROMIS Global-10 physical and mental health scores was determined using partial r-squared values. Significance was set at p < 0.05. RESULTS: There was a poor correlation between the ADI and the NSS (ρ = 0.34; p < 0.001). There were good correlations between the ADI and SVI (ρ = 0.43; p < 0.001) and between the NSS and SVI (ρ = 0.59; p < 0.001). There was a poor correlation between the PROMIS Global-10 physical health and NSS (ρ = -0.14; p < 0.001), ADI (ρ = -0.24; p < 0.001), and SVI (ρ = -0.17; p < 0.001). There was a poor correlation between PROMIS Global-10 mental health and NSS (ρ = -0.13; p < 0.001), ADI (ρ = -0.22; p < 0.001), and SVI (ρ = -0.17; p < 0.001). When accounting for key sociodemographic factors, the ADI demonstrated the largest association with presenting physical health (regression coefficient: -0.13 [95% CI -0.14 to -0.12]; p < 0.001) and mental health (regression coefficient: -0.13 [95% CI -0.14 to -0.12]; p < 0.001), as confirmed by the partial r-squared values for each SDoH index (physical health: ADI 0.04 versus SVI 0.02 versus NSS 0.01; mental health: ADI 0.04 versus SVI 0.02 versus NSS 0.01). This finding means that as social deprivation increases, physical and mental health scores decrease, representing poorer health. For further context, an increase in ADI score by approximately 36 and 39 suggests a clinically meaningful (determined using distribution-based minimum clinically important difference estimates of one-half SD of each PROMIS score) worsening of physical and mental health, respectively. CONCLUSION: Orthopaedic surgeons, policy makers, and other stakeholders looking to address SDoH factors to help alleviate disparities in musculoskeletal care should try to avoid interchanging the ADI, SVI, and NSS. Because the ADI has the largest association between any of the geographically based SDoH indices and presenting physical and mental health, it may allow for easier clinical and policy application. CLINICAL RELEVANCE: We suggest using the ADI as the geographically based SDoH index in orthopaedic surgery in the United States. Further, we caution against comparing findings in one study that use one geographically based SDoH index to another study's findings that incorporates another geographically based SDoH index. Although the general findings may be the same, the strength of association and clinical relevance could differ and have policy ramifications that are not otherwise appreciated; however, the degree to which this may be true is an area for future inquiry.

What Is the Influence of Femoral Version on Size, Tear Location, and Tear Pattern of the Acetabular Labrum in Patients With FAI?

BACKGROUND: Femoral version deformities have recently been identified as a major contributor to femoroacetabular impingement (FAI). An in-depth understanding of the specific labral damage patterns caused by femoral version deformities may help to understand the underlying pathomorphologies in symptomatic patients and select the appropriate surgical treatment. QUESTIONS/PURPOSES: We asked: (1) Is there a correlation between femoral version and the mean cross-sectional area of the acetabular labrum? (2) Is there a difference in the location of lesions of the acetabular labrum between hips with increased femoral version and hips with decreased femoral version? (3) Is there a difference in the pattern of lesions of the acetabular labrum between hips with increased femoral version and hips with decreased femoral version? METHODS: This was a retrospective, comparative study. Between November 2009 and September 2016, we evaluated 640 hips with FAI. We considered patients with complete diagnostic imaging including magnetic resonance arthrography (MRA) of the affected hip with radial slices of the proximal femur and axial imaging of the distal femoral condyles (allowing for calculation of femoral version) as eligible. Based on that, 97% (620 of 640 hips) were eligible; a further 77% (491 of 640 hips) were excluded because they had either normal femoral version (384 hips), incomplete imaging (20 hips), a lateral center-edge angle < 22° (43 hips) or > 39° (16 hips), age > 50 years (8 hips), or a history of pediatric hip disease (20 hips), leaving 20% (129 of 640 hips) of patients with a mean age of 27 ± 9 years for analysis, and 61% (79 of 129 hips) were female. Patients were assigned to either the increased (> 30°) or decreased (< 5°) femoral version group. The labral cross-sectional area was measured on radial MR images in all patients. The location-dependent labral cross-sectional area, presence of labral tears, and labral tear patterns were assessed using the acetabular clockface system and compared among groups. RESULTS: In hips with increased femoral version, the labrum was normal in size (21 ± 6 mm2 [95% confidence interval 20 to 23 mm2]), whereas hips with decreased femoral version showed labral hypotrophy (14 ± 4 mm2 [95% CI 13 to 15 mm2]; p < 0.01). In hips with increased femoral version, labral tears were located more anteriorly (median 1:30 versus 12:00; p < 0.01). Hips with increased femoral version exhibited damage of the anterior labrum with more intrasubstance tears anterosuperiorly (17% [222 of 1322] versus 9% [93 of 1084]; p < 0.01) and partial tears anteroinferiorly (22% [36 of 165] versus 6% [8 of 126]; p < 0.01). Hips with decreased femoral version showed superior labral damage consisting primarily of partial labral tears. CONCLUSION: In the evaluation of patients with FAI, the term "labral tear" is not accurate enough to describe labral pathology. Based on high-quality radial MR images, surgeons should always evaluate the combination of labral tear location and labral tear pattern, because these may provide insight into associated femoral version abnormalities, which can inform appropriate surgical treatment. Future studies should examine symptomatic patients with normal femoral version, as well as an asymptomatic control group, to describe the effect of femoral version on labral morphology across the entire spectrum of pathomorphologies. LEVEL OF EVIDENCE: Level III, prognostic study.

Quality of life in chordoma survivors: results from the Chordoma Foundation Survivorship Survey.

OBJECTIVE: Chordomas are rare malignant bone tumors whose location in the skull base or spine, invasive surgical treatment, and accompanying adjuvant radiotherapy may all lead patients to experience poor quality of life (QOL). Limited research has been conducted on specific demographic and clinical factors associated with decreased QOL in chordoma survivors. Thus, the aim of the present study was to investigate several potential variables and their impact on specific QOL domains in these patients as well the frequencies of specific QOL challenges within these domains. METHODS: The Chordoma Foundation (CF) Survivorship Survey was electronically distributed to chordoma survivors subscribed to the CF Chordoma Connections forum. Survey questions assessed QOL in three domains: physical, emotional/cognitive, and social. The degree of impairment was assessed by grouping the participants into high- and low-challenge groups designated by having ≥ 5 or < 5 symptoms or challenges within a given QOL domain. Bivariate analysis of demographic and clinical characteristics between these groups was conducted using Fisher's exact test and the Mann-Whitney U-test. RESULTS: A total of 665 chordoma survivors at least partially completed the survey. On bivariate analysis, female sex was significantly associated with increased odds of significant emotional (p = 0.001) and social (p = 0.019) QOL burden. Younger survivors (age < 65 years) were significantly more likely to experience significant physical (p < 0.0001), emotional (p < 0.0001), and social (p < 0.0001) QOL burden. Skull base chordoma survivors had significantly higher emotional/cognitive QOL burden than spinal chordoma survivors (p = 0.022), while the converse was true for social QOL challenges (p = 0.0048). Survivors currently in treatment were significantly more likely to experience significant physical QOL challenges compared with survivors who completed their treatment > 10 years ago (p = 0.0074). Fear of cancer recurrence (FCR) was the most commonly reported emotional/cognitive QOL challenge (49.6%). Only 41% of the participants reported having their needs met for their physical QOL challenges as well as 25% for emotional/cognitive and 18% for social. CONCLUSIONS: The authors' findings suggest that younger survivors, female survivors, and survivors currently undergoing treatment for chordoma are at high risk for adverse QOL outcomes. Additionally, although nearly half of the participants reported a FCR, very few reported having adequate emotional/cognitive care. These findings may be useful in identifying specific groups of chordoma survivors vulnerable to QOL challenges and bring to light the need to expand care to meet the QOL needs for these patients.

Drop in survivorship 13 years after AMIC procedures in aligned knees: A long-term follow-up.

PURPOSE: Autologous matrix-induced chondrogenesis (AMIC) showed promising short-term results comparable to microfracture. This study aims to assess the 19-year outcomes of AMIC, addressing the lack of long-term data. METHODS: Retrospective cohort of 34 knees treated with AMIC underwent a 19-year follow-up. The primary outcome was AMIC survival, considering total knee arthroplasty as a failure event. Survival analysis for factors that were associated with longer survival of the AMIC was also performed. Clinical and radiological outcome scores were analysed for the AMIC group. RESULTS: Twenty-three knees were available for follow-up analysis. Of these, 14 (61%) underwent revision surgery for total knee arthroplasty (TKA). The mean time was 13.3 ± 2.5 years (range: 9-17 years). Secondary outcomes showed that increased age at surgery (hazard ratio [HR]: 1.05; p = 0.021) and larger defect size (HR: 1.95; p = 0.018) were risk factors for failure. Concomitant proximal tibial osteotomy (HR: 0.22; p = 0.019) was associated with longer survival. The remaining nine knees (39%) were analysed as a single group. The mean clinical score at follow-up of 18.6 ± 0.9 SD years was 79.5 ± 19.7 SD for the Lysholm score, 1.8 ± 1.5 SD for the visual analog scale score, 74.2 ± 22.4 SD for the KOOS score and a median of 3 (range: 3-4) for the Tegner activity scale. CONCLUSIONS: The mean survival time of 13.3 years indicates the durability of AMIC in properly aligned knees. Nonetheless, despite a 61% conversion to TKA, the knees that persisted until the 19-year follow-up remained stable, underscoring the procedure's longevity and consistent clinical outcomes. LEVEL OF EVIDENCE: Level IV.

Modified Kapandji technique in pediatric displaced distal radius fractures: results in 195 patients.

PURPOSE: The modified Kapandji technique has been proposed for fracture reduction in pediatric displaced distal radius fractures (DDRFs), but evidence is sparse. The purpose of this study was to evaluate our outcomes and complications, critically and systematically, when performing the modified Kapandji technique in pediatric DDRFs. Using this technique since 2011, we asked: (1) What is the quality of fracture reduction using this technique? (2) How stable is fracture alignment with this technique? (3) What are the postoperative complications and complication rates? METHODS: Retrospective observational study of 195 pediatric patients treated with the modified Kapandji technique. Quality of fracture reduction, fixation type (intrafocal, combined, or extrafocal), and coronal/sagittal angulation were recorded at surgery and healing. Perioperative complications were graded. Patients were stratified by fracture (metaphyseal or Salter-Harris) and fixation type, as well as age (≤ 6 years; 6 to 10 years; > 10 years). RESULTS: Fracture reduction was 'good' to 'anatomical' in 85% of patients. 'Anatomical' fracture reduction was less frequent in metaphyseal fractures (21% vs. 51%; p < .001). Mean angulation change was higher in metaphyseal fractures in both the sagittal (p = .011) and coronal (p = .021) planes. Metaphyseal fractures showed a higher mean change in sagittal angulation during fracture healing for the 'intrafocal' group. We observed a 15% overall complication rate with 1% being modified Sink Grade 3. CONCLUSION: The modified Kapandji technique for pediatric DDRFs is a safe and effective treatment option. Metaphyseal fractures that do not involve the physis should be treated with extrafocal or combined wire fixation. Complications that require additional surgical treatment are rare. LEVEL OF EVIDENCE: Level of evidence IV.

Prognostic Significance of Percentage and Size of Dedifferentiation in Dedifferentiated Chondrosarcoma.

Dedifferentiated chondrosarcoma is rare, aggressive, and microscopically bimorphic. How pathologic features such as the amounts of dedifferentiation affect prognosis remains unclear. We evaluated the percentages and sizes of dedifferentiation in a consecutive institutional series of dedifferentiated chondrosarcomas from 1999 to 2021. The statistical analysis included cox proportional hazard models and log-rank tests. Of the 67 patients (26 women, 41 men; age, 39 to >89 [median 61] years; 2 with Ollier disease), 58 presented de novo; 9 were identified with conventional chondrosarcomas 0.6-13.2 years (median, 5.5 years) prior. Pathologic fracture and distant metastases were noted in 27 and 7 patients at presentation. The tumors involved the femur (n = 27), pelvis (n = 22), humerus (n = 7), tibia (n = 4), scapula/ribs (n = 4), spine (n = 2), and clivus (n = 1). In the 56 resections, the tumors ranged in size from 3.5 to 46.0 cm (median, 11.5 cm) and contained 1%-99.5% (median, 70%) dedifferentiated components that ranged in size from 0.6 to 24.0 cm (median, 7.3 cm). No correlation was noted between total size and percentage of dedifferentiation. The dedifferentiated components were typically fibrosarcomatous or osteosarcomatous, whereas the associated cartilaginous components were predominantly grade 1-2, rarely enchondromas or grade 3. The entire cohort's median overall survival and progression-free survival were 11.8 and 5.4 months, respectively. In the resected cohort, although the total size was not prognostic, the percentage of dedifferentiation ≥20% and size of dedifferentiation >3.0 cm each predicted worse overall survival (9.9 vs 72.5 months; HR, 3.76; 95% CI, 1.27-11.14; P = .02; 8.7 vs 58.9 months; HR, 3.03; 95% CI, 1.21-7.57; P = .02, respectively) and progression-free survival (5.3 vs 62.1 months; HR, 3.05; 95% CI, 1.13-8.28; P = .03; 5.3 vs 56.6 months; HR, 2.50; 95% CI, 1.06-5.88; P = .04, respectively). In conclusion, both the percentages and sizes of dedifferentiation were better prognostic predictors than total tumor sizes in dedifferentiated chondrosarcomas, highlighting the utility of their pathologic evaluations.

Pilot Study: Short Term Impact of Radiation Therapy on Bone Mineral Density and Bone Metabolism.

Despite the risk of complications, high dose radiation therapy is increasingly utilized in the management of selected bone malignancies. In this study, we investigate the impact of moderate to high dose radiation (over 50 Gy) on bone metabolism and structure. Between 2015 and 2018, patients with a primary malignant bone tumor of the sacrum that were either treated with high dose definitive radiation only or a combination of moderate to high dose radiation and surgery were prospectively enrolled at a single institution. Quantitative CTs were performed before and after radiation to determine changes in volumetric bone mineral density (BMD) of the irradiated and non-irradiated spine. Bone histomorphometry was performed on biopsies of the irradiated sacrum and the non-irradiated iliac crest of surgical patients using a quadruple tetracycline labeling protocol. In total, 9 patients were enrolled. Two patients received radiation only (median dose 78.3 Gy) and 7 patients received a combination of preoperative radiation (median dose 50.4 Gy), followed by surgery. Volumetric BMD of the non-irradiated lumbar spine did not change significantly after radiation, while the BMD of the irradiated sacrum did (pre-radiation median: 108.0 mg/cm3 (IQR 91.8-167.1); post-radiation median: 75.3 mg/cm3 (IQR 57.1-110.2); p = 0.010). The cancellous bone of the non-irradiated iliac crest had a stable bone formation rate, while the irradiated sacrum showed a significant decrease in bone formation rate [pre-radiation median: 0.005 mm3/mm2/year (IQR 0.003-0.009), post-radiation median: 0.001 mm3/mm2/year (IQR 0.001-0.001); p = 0.043]. Similar effects were seen in the cancellous and endocortical envelopes. This pilot study shows a decrease of volumetric BMD and bone formation rate after high-dose radiation therapy. Further studies with larger cohorts and other endpoints are needed to get more insight into the effect of radiation on bone. Level of evidence: IV.

Are degenerative findings detected on traction MR arthrography of the hip associated with failure of arthroscopic femoroacetabular impingement surgery?

OBJECTIVES: To identify preoperative degenerative features on traction MR arthrography associated with failure after arthroscopic femoroacetabular impingement (FAI) surgery. METHODS: Retrospective study including 102 patients (107 hips) undergoing traction magnetic resonance arthrography (MRA) of the hip at 1.5 T and subsequent hip arthroscopic FAI surgery performed (01/2016 to 02/2020) with complete follow-up. Clinical outcomes were assessed using the International Hip Outcome Tool (iHOT-12) score. Clinical endpoint for failure was defined as an iHOT-12 of < 60 points or conversion to total hip arthroplasty. MR images were assessed by two radiologists for presence of 9 degenerative lesions including osseous, chondrolabral/ligamentum teres lesions. Uni- and multivariate Cox regression analysis was performed to assess the association between MRI findings and failure of FAI surgery. RESULTS: Of the 107 hips, 27 hips (25%) met at least one endpoint at a mean 3.7 ± 0.9 years follow-up. Osteophytic changes of femur or acetabulum (hazard ratio [HR] 2.5-5.0), acetabular cysts (HR 3.4) and extensive cartilage (HR 5.1) and labral damage (HR 5.5) > 2 h on the clockface were univariate risk factors (all p < 0.05) for failure. Three risk factors for failure were identified in multivariate analysis: Acetabular cartilage damage > 2 h on the clockface (HR 3.2, p = 0.01), central femoral osteophyte (HR 3.1, p = 0.02), and femoral cartilage damage with ligamentum teres damage (HR 3.0, p = 0.04). CONCLUSION: Joint damage detected by preoperative traction MRA is associated with failure 4 years following arthroscopic FAI surgery and yields promise in preoperative risk stratification. CLINICAL RELEVANCE STATEMENT: Evaluation of negative predictors on preoperative traction MR arthrography holds the potential to improve risk stratification based on the already present joint degeneration ahead of FAI surgery. KEY POINTS: • Osteophytes, acetabular cysts, and extensive chondrolabral damage are risk factors for failure of FAI surgery. • Extensive acetabular cartilage damage, central femoral osteophytes, and combined femoral cartilage and ligamentum teres damage represent independent negative predictors. • Survival rates following hip arthroscopy progressively decrease with increasing prevalence of these three degenerative findings.

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

BACKGROUND: It is well documented that routinely collected patient sociodemographic characteristics (such as race and insurance type) and geography-based social determinants of health (SDoH) measures (for example, the Area Deprivation Index) are associated with health disparities, including symptom severity at presentation. However, the association of patient-level SDoH factors (such as housing status) on musculoskeletal health disparities is not as well documented. Such insight might help with the development of more-targeted interventions to help address health disparities in orthopaedic surgery. QUESTIONS/PURPOSES: (1) What percentage of patients presenting for new patient visits in an orthopaedic surgery clinic who were unemployed but seeking work reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, reported trouble paying for medications, and/or had no current housing? (2) Accounting for traditional sociodemographic factors and patient-level SDoH measures, what factors are associated with poorer patient-reported outcome physical health scores at presentation? (3) Accounting for traditional sociodemographic factor patient-level SDoH measures, what factors are associated with poorer patient-reported outcome mental health scores at presentation? METHODS: New patient encounters at one Level 1 trauma center clinic visit from March 2018 to December 2020 were identified. Included patients had to meet two criteria: they had completed the Patient-Reported Outcome Measure Information System (PROMIS) Global-10 at their new orthopaedic surgery clinic encounter as part of routine clinical care, and they had visited their primary care physician and completed a series of specific SDoH questions. The SDoH questionnaire was developed in our institution to improve data that drive interventions to address health disparities as part of our accountable care organization work. Over the study period, the SDoH questionnaire was only distributed at primary care provider visits. The SDoH questions focused on transportation, housing, employment, and ability to pay for medications. Because we do not have a way to determine how many patients had both primary care provider office visits and new orthopaedic surgery clinic visits over the study period, we were unable to determine how many patients could have been included; however, 9057 patients were evaluated in this cross-sectional study. The mean age was 61 ± 15 years, and most patients self-reported being of White race (83% [7561 of 9057]). Approximately half the patient sample had commercial insurance (46% [4167 of 9057]). To get a better sense of how this study cohort compared with the overall patient population seen at the participating center during the time in question, we reviewed all new patient clinic encounters (n = 135,223). The demographic information between the full patient sample and our study subgroup appeared similar. Using our study cohort, two multivariable linear regression models were created to determine which traditional metrics (for example, self-reported race or insurance type) and patient-specific SDoH factors (for example, lack of reliable transportation) were associated with worse physical and mental health symptoms (that is, lower PROMIS scores) at new patient encounters. The variance inflation factor was used to assess for multicollinearity. For all analyses, p values < 0.05 designated statistical significance. The concept of minimum clinically important difference (MCID) was used to assess clinical importance. Regression coefficients represent the projected change in PROMIS physical or mental health symptom scores (that is, the dependent variable in our regression analyses) accounting for the other included variables. Thus, a regression coefficient for a given variable at or above a known MCID value suggests a clinical difference between those patients with and without the presence of that given characteristic. In this manuscript, regression coefficients at or above 4.2 (or at and below -4.2) for PROMIS Global Physical Health and at or above 5.1 (or at and below -5.1) for PROMIS Global Mental Health were considered clinically relevant. RESULTS: Among the included patients, 8% (685 of 9057) were unemployed but seeking work, 4% (399 of 9057) reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, 4% (328 of 9057) reported trouble paying for medications, and 2% (181 of 9057) had no current housing. Lack of reliable transportation to attend doctor visits or pick up medications (ß = -4.52 [95% CI -5.45 to -3.59]; p < 0.001), trouble paying for medications (ß = -4.55 [95% CI -5.55 to -3.54]; p < 0.001), Medicaid insurance (ß = -5.81 [95% CI -6.41 to -5.20]; p < 0.001), and workers compensation insurance (ß = -5.99 [95% CI -7.65 to -4.34]; p < 0.001) were associated with clinically worse function at presentation. Trouble paying for medications (ß = -6.01 [95% CI -7.10 to -4.92]; p < 0.001), Medicaid insurance (ß = -5.35 [95% CI -6.00 to -4.69]; p < 0.001), and workers compensation (ß = -6.07 [95% CI -7.86 to -4.28]; p < 0.001) were associated with clinically worse mental health at presentation. CONCLUSION: Although transportation issues and financial hardship were found to be associated with worse presenting physical function and mental health, Medicaid and workers compensation insurance remained associated with worse presenting physical function and mental health as well even after controlling for these more detailed, patient-level SDoH factors. Because of that, interventions to decrease health disparities should focus on not only sociodemographic variables (for example, insurance type) but also tangible patient-specific SDoH characteristics. For example, this may include giving patients taxi vouchers or ride-sharing credits to attend clinic visits for patients demonstrating such a need, initiating financial assistance programs for necessary medications, and/or identifying and connecting certain patient groups with social support services early on in the care cycle. LEVEL OF EVIDENCE: Level III, prognostic study.

Does the SORG Machine-learning Algorithm for Extremity Metastases Generalize to a Contemporary Cohort of Patients? Temporal Validation From 2016 to 2020.

BACKGROUND: The ability to predict survival accurately in patients with osseous metastatic disease of the extremities is vital for patient counseling and guiding surgical intervention. We, the Skeletal Oncology Research Group (SORG), previously developed a machine-learning algorithm (MLA) based on data from 1999 to 2016 to predict 90-day and 1-year survival of surgically treated patients with extremity bone metastasis. As treatment regimens for oncology patients continue to evolve, this SORG MLA-driven probability calculator requires temporal reassessment of its accuracy. QUESTION/PURPOSE: Does the SORG-MLA accurately predict 90-day and 1-year survival in patients who receive surgical treatment for a metastatic long-bone lesion in a more recent cohort of patients treated between 2016 and 2020? METHODS: Between 2017 and 2021, we identified 674 patients 18 years and older through the ICD codes for secondary malignant neoplasm of bone and bone marrow and CPT codes for completed pathologic fractures or prophylactic treatment of an impending fracture. We excluded 40% (268 of 674) of patients, including 18% (118) who did not receive surgery; 11% (72) who had metastases in places other than the long bones of the extremities; 3% (23) who received treatment other than intramedullary nailing, endoprosthetic reconstruction, or dynamic hip screw; 3% (23) who underwent revision surgery, 3% (17) in whom there was no tumor, and 2% (15) who were lost to follow-up within 1 year. Temporal validation was performed using data on 406 patients treated surgically for bony metastatic disease of the extremities from 2016 to 2020 at the same two institutions where the MLA was developed. Variables used to predict survival in the SORG algorithm included perioperative laboratory values, tumor characteristics, and general demographics. To assess the models' discrimination, we computed the c-statistic, commonly referred to as the area under the receiver operating characteristic (AUC) curve for binary classification. This value ranged from 0.5 (representing chance-level performance) to 1.0 (indicating excellent discrimination) Generally, an AUC of 0.75 is considered high enough for use in clinical practice. To evaluate the agreement between predicted and observed outcomes, a calibration plot was used, and the calibration slope and intercept were calculated. Perfect calibration would result in a slope of 1 and intercept of 0. For overall performance, the Brier score and null-model Brier score were determined. The Brier score can range from 0 (representing perfect prediction) to 1 (indicating the poorest prediction). Proper interpretation of the Brier score necessitates a comparison with the null-model Brier score, which represents the score for an algorithm that predicts a probability equal to the population prevalence of the outcome for each patient. Finally, a decision curve analysis was conducted to compare the potential net benefit of the algorithm with other decision-support methods, such as treating all or none of the patients. Overall, 90-day and 1-year mortality were lower in the temporal validation cohort than in the development cohort (90 day: 23% versus 28%; p < 0.001, and 1 year: 51% versus 59%; p<0.001). RESULTS: Overall survival of the patients in the validation cohort improved from 28% mortality at the 90-day timepoint in the cohort on which the model was trained to 23%, and 59% mortality at the 1-year timepoint to 51%. The AUC was 0.78 (95% CI 0.72 to 0.82) for 90-day survival and 0.75 (95% CI 0.70 to 0.79) for 1-year survival, indicating the model could distinguish the two outcomes reasonably. For the 90-day model, the calibration slope was 0.71 (95% CI 0.53 to 0.89), and the intercept was -0.66 (95% CI -0.94 to -0.39), suggesting the predicted risks were overly extreme, and that in general, the risk of the observed outcome was overestimated. For the 1-year model, the calibration slope was 0.73 (95% CI 0.56 to 0.91) and the intercept was -0.67 (95% CI -0.90 to -0.43). With respect to overall performance, the model's Brier scores for the 90-day and 1-year models were 0.16 and 0.22. These scores were higher than the Brier scores of internal validation of the development study (0.13 and 0.14) models, indicating the models' performance has declined over time. CONCLUSION: The SORG MLA to predict survival after surgical treatment of extremity metastatic disease showed decreased performance on temporal validation. Moreover, in patients undergoing innovative immunotherapy, the possibility of mortality risk was overestimated in varying severity. Clinicians should be aware of this overestimation and discount the prediction of the SORG MLA according to their own experience with this patient population. Generally, these results show that temporal reassessment of these MLA-driven probability calculators is of paramount importance because the predictive performance may decline over time as treatment regimens evolve. The SORG-MLA is available as a freely accessible internet application at https://sorg-apps.shinyapps.io/extremitymetssurvival/ .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.

An Improved Average Atomic Number Calculation for Estimating Backscatter and Continuum Production in Compounds.

It is often assumed that electron backscatter and continuum (bremsstrahlung) productions emitted from electron-solid interactions during X-ray microanalysis in compounds can be extrapolated from pure element observations by means of the assumption of average atomic number, or Z-bar (Z¯). For pure elements the average Z is equal to the atomic number, but this direct approach fails for compounds. The use of simple atomic fractions yields completely spurious results, and while the commonly used mass fraction Z averaging produces fairly reasonable results, we know from physical considerations that the mass of the neutron plays only a negligible role in such interactions below ∼1 MeV. Therefore, including the mass or atomic weight in such calculations can only introduce further errors in these models. We present an expression utilizing atomic fractions of the atomic numbers of the elements in the compound (Z fraction), with an exponent to account for the variation in nuclear screening as a function of the element Z value.

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

INTRODUCTION: Complications after total hip arthroplasty (THA) may result in readmission or reoperation and impose a significant cost on the healthcare system. Understanding which patients are at-risk for complications can potentially allow for targeted interventions to decrease complication rates through pursuing preoperative health optimization. The purpose of the current was to develop and internally validate machine learning (ML) algorithms capable of performing patient-specific predictions of all-cause complications within two years of primary THA. METHODS: This was a retrospective case-control study of clinical registry data from 616 primary THA patients from one large academic and two community hospitals. The primary outcome was all-cause complications at a minimum of 2-years after primary THA. Recursive feature elimination was applied to identify preoperative variables with the greatest predictive value. Five ML algorithms were developed on the training set using tenfold cross-validation and internally validated on the independent testing set of patients. Algorithms were assessed by discrimination, calibration, Brier score, and decision curve analysis to quantify performance. RESULTS: The observed complication rate was 16.6%. The stochastic gradient boosting model achieved the best performance with an AUC = 0.88, calibration intercept = 0.1, calibration slope = 1.22, and Brier score = 0.09. The most important factors for predicting complications were age, drug allergies, prior hip surgery, smoking, and opioid use. Individual patient-level explanations were provided for the algorithm predictions and incorporated into an open access digital application: https://sorg-apps.shinyapps.io/tha_complication/ CONCLUSIONS: The stochastic boosting gradient algorithm demonstrated good discriminatory capacity for identifying patients at high-risk of experiencing a postoperative complication and proof-of-concept for creating office-based applications from ML that can perform real-time prediction. However, this clinical utility of the current algorithm is unknown and definitions of complications broad. Further investigation on larger data sets and rigorous external validation is necessary prior to the assessment of clinical utility with respect to risk-stratification of patients undergoing primary THA. LEVEL OF EVIDENCE: III, therapeutic study.

Development of a machine learning algorithm to identify surgical candidates for hip and knee arthroplasty without in-person evaluation.

INTRODUCTION: Arthroplasty care delivery is facing a growing supply-demand mismatch. To meet future demand for joint arthroplasty, systems will need to identify potential surgical candidates prior to evaluation by orthopaedic surgeons. MATERIALS AND METHODS: Retrospective review was conducted at two academic medical centers and three community hospitals from March 1 to July 31, 2020 to identify new patient telemedicine encounters (without prior in-person evaluation) for consideration of hip or knee arthroplasty. The primary outcome was surgical indication for joint replacement. Five machine learning algorithms were developed to predict likelihood of surgical indication and assessed by discrimination, calibration, overall performance, and decision curve analysis. RESULTS: Overall, 158 patients underwent new patient telemedicine evaluation for consideration of THA, TKA, or UKA and 65.2% (n = 103) were indicated for operative intervention prior to in-person evaluation. The median age was 65 (interquartile range 59-70) and 60.8% were women. Variables found to be associated with operative intervention were radiographic degree of arthritis, prior trial of intra-articular injection, trial of physical therapy, opioid use, and tobacco use. In the independent testing set (n = 46) not used for algorithm development, the stochastic gradient boosting algorithm achieved the best performance with AUC 0.83, calibration intercept 0.13, calibration slope 1.03, Brier score 0.15 relative to a null model Brier score of 0.23, and higher net benefit than the default alternatives on decision curve analysis. CONCLUSION: We developed a machine learning algorithm to identify potential surgical candidates for joint arthroplasty in the setting of osteoarthritis without an in-person evaluation or physical examination. If externally validated, this algorithm could be deployed by various stakeholders, including patients, providers, and health systems, to direct appropriate next steps in patients with osteoarthritis and improve efficiency in identifying surgical candidates. LEVEL OF EVIDENCE: III.

Assessing the Safety and Utility of Wound VAC Temporization of the Sarcoma or Benign Aggressive Tumor Bed Until Final Margins Are Achieved.

BACKGROUND: Local recurrence of microinvasive sarcoma or benign aggressive pathologies can be limb- and life-threatening. Although frozen pathology is reliable, tumor microinvasion can be subtle or missed, having an impact on surgical margins and postoperative radiation planning. The authors' service has begun to temporize the tumor bed after primary tumor excision with a wound vacuum-assisted closure (VAC) pending formal margin analysis, with coverage performed in the setting of final negative margins. METHODS: This retrospective analysis included all patients managed at a tertiary referral cancer center with VAC temporization after soft tissue sarcoma or benign aggressive tumor excision from 1 January 2000 to 1 January 2019 and at least 2 years of oncologic follow-up evaluation. The primary outcome was local recurrence. The secondary outcomes were distant recurrence, unplanned return to the operating room for wound/infectious indications, thromboembolic events, and tumor-related deaths. RESULTS: For 62 patients, VAC temporization was performed. The mean age of the patients was 62.2 ± 22.3 years (median 66.5 years; 95% confidence interval [CI] 61.7-72.5 years), and the mean age-adjusted Charlson Comorbidity Index was 5.3 ± 1.9. The most common tumor histology was myxofibrosarcoma (51.6%, 32/62). The mean volume was 124.8 ± 324.1 cm3, and 35.5% (22/62) of the cases were subfascial. Local recurrences occurred for 8.1% (5/62) of the patients. Three of these five patients had planned positive margins, and 17.7% (11/62) of the patients had an unplanned return to the operating room. No demographic or tumor factors were associated with unplanned surgery. CONCLUSIONS: The findings showed that VAC-temporized management of microinvasive sarcoma and benign aggressive pathologies yields favorable local recurrence and unplanned operating room rates suggestive of oncologic and technical safety. These findings will need validation in a future randomized controlled trial.

Albumin and Survival in Extremity Metastatic Bone Disease: An Analysis of Two Independent Datasets.

INTRODUCTION: Numerous prognostication models have been developed to estimate survival in patients with extremity metastatic bone disease, but few include albumin despite albumin's role in malnutrition and inflammation. The purpose of this study was to examine two independent datasets to determine the value for albumin in prognosticating survival in this population. MATERIALS AND METHODS: Extremity metastatic bone disease patients undergoing surgical management were identified from two independent populations. Population 1: Retrospective chart review at two tertiary care centers. Population 2: A large, national, North American multicenter surgical registry with 30-day follow-up. Bivariate and multivariate analyses were used to examine albumin's value for prognostication at 1-, 3-, and 12-month after surgery. RESULTS: In Population 1, 1,090 patients were identified with 1-, 3-, and 12-month mortality rates of 95 (8.8%), 305 (28.9%), and 639 (62.0%), respectively. In Population 2, 1,675 patients were identified with one-month postoperative mortality rates of 148 (8.8%). In both populations, hypoalbuminemia was an independent prognostic factor for mortality at 30 days. In the institutional set, hypoalbuminemia was additionally associated with 3- and 12-month mortality. CONCLUSIONS: Hypoalbuminemia is a marker for mortality in extremity metastatic bone disease. Further consideration of this marker could improve existing prognostication models in this population. LEVEL OF EVIDENCE: III.

The preoperative machine learning algorithm for extremity metastatic disease can predict 90-day and 1-year survival: An external validation study.

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.