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.

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

BACKGROUND: Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. QUESTIONS/PURPOSES: (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? METHODS: Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. RESULTS: Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. CONCLUSION: Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. LEVEL OF EVIDENCE: Level III, diagnostic 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.

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 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.

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.

Body composition predictors of mortality in patients undergoing surgery for long bone metastases.

BACKGROUND AND OBJECTIVES: Body composition measurements using computed tomography (CT) may serve as imaging biomarkers of survival in patients with and without cancer. This study assesses whether body composition measurements obtained on abdominal CTs are independently associated with 90-day and 1-year mortality in patients with long-bone metastases undergoing surgery. METHODS: This single institutional retrospective study included 212 patients who had undergone surgery for long-bone metastases and had a CT of the abdomen within 90 days before surgery. Quantification of cross-sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue, and paraspinous and abdominal muscles were performed at L4. Multivariate Cox proportional-hazards analyses were performed. RESULTS: Sarcopenia was independently associated with 90-day mortality (hazard ratio [HR] = 1.87; 95% confidence interval [CI] = 1.11-3.16; p = 0.019) and 1-year mortality (HR = 1.50; 95% CI = 1.02-2.19; p = 0.038) in multivariate analysis while controlling for clinical variables such as primary tumors, comorbidities, and chemotherapy. Abdominal fat CSAs and muscle attenuation were not associated with mortality. CONCLUSIONS: The presence of sarcopenia assessed by CT is predictive of 90-day and 1-year mortality in patients undergoing surgery for long-bone metastases. This body composition measurement can be used as novel imaging biomarker supplementing existing prognostic tools to optimize patient selection for surgery and improve shared decision making.

Sclerostin immunohistochemical staining in surgically treated giant cell tumor of bone.

BACKGROUND: Giant cell tumor of bone (GCTB) is a destructive lesion with a high potential for recurrence. RANK-ligand targeted therapy has provided promising, yet mixed results. Sclerostin (SOST) inhibition results in a net anabolic response and is currently used in the treatment of osteoporosis. The application to GCTB is unknown. OBJECTIVES: We sought to determine if GCTB stained for SOST on immunohistochemistry and correlate its expression with predictor variables. METHODS: All patients at a single institution undergoing surgery for GCTB between 1993 and 2008 with a minimum of 6 months follow-up were included. Primary outcomes included the presence of SOST staining, secondary outcomes included the correlation of patient and tumor-specific predictor variables. RESULTS: SOST antibody staining of any cell type was present in 47 of 48 cases (97.9%). Positivity of the stromal cells was present in 39 of 48 cases (81.3%) and was associated with radiographic aggressiveness (p = 0.023), symptomatic presentation (p = 0.032), prior surgery (p = 0.005), and patient age (p = 0.034). Positivity of giant cells was present in 41 of 48 cases (85.4%) and was not significant with predictive factors. CONCLUSIONS: Sclerostin staining in GCTB is a novel finding and warrants further research to define the role of sclerostin as a prognostic factor and therapeutic target.

What Is the Clinical Benefit of Common Orthopaedic Procedures as Assessed by the PROMIS Versus Other Validated Outcomes Tools?

BACKGROUND: Patient-reported outcome measures (PROMs), including the Patient-reported Outcomes Measurement Information System (PROMIS), are increasingly used to measure healthcare value. The minimum clinically important difference (MCID) is a metric that helps clinicians determine whether a statistically detectable improvement in a PROM after surgical care is likely to be large enough to be important to a patient or to justify an intervention that carries risk and cost. There are two major categories of MCID calculation methods, anchor-based and distribution-based. This variability, coupled with heterogeneous surgical cohorts used for existing MCID values, limits their application to clinical care. QUESTIONS/PURPOSES: In our study, we sought (1) to determine MCID thresholds and attainment percentages for PROMIS after common orthopaedic procedures using distribution-based methods, (2) to use anchor-based MCID values from published studies as a comparison, and (3) to compare MCID attainment percentages using PROMIS scores to other validated outcomes tools such as the Hip Disability and Osteoarthritis Outcome Score (HOOS) and Knee Disability and Osteoarthritis Outcome Score (KOOS). METHODS: This was a retrospective study at two academic medical centers and three community hospitals. The inclusion criteria for this study were patients who were age 18 years or older and who underwent elective THA for osteoarthritis, TKA for osteoarthritis, one-level posterior lumbar fusion for lumbar spinal stenosis or spondylolisthesis, anatomic total shoulder arthroplasty or reverse total shoulder arthroplasty for glenohumeral arthritis or rotator cuff arthropathy, arthroscopic anterior cruciate ligament reconstruction, arthroscopic partial meniscectomy, or arthroscopic rotator cuff repair. This yielded 14,003 patients. Patients undergoing revision operations or surgery for nondegenerative pathologies and patients without preoperative PROMs assessments were excluded, leaving 9925 patients who completed preoperative PROMIS assessments and 9478 who completed other preoperative validated outcomes tools (HOOS, KOOS, numerical rating scale for leg pain, numerical rating scale for back pain, and QuickDASH). Approximately 66% (6529 of 9925) of patients had postoperative PROMIS scores (Physical Function, Mental Health, Pain Intensity, Pain Interference, and Upper Extremity) and were included for analysis. PROMIS scores are population normalized with a mean score of 50 ± 10, with most scores falling between 30 to 70. Approximately 74% (7007 of 9478) of patients had postoperative historical assessment scores and were included for analysis. The proportion who reached the MCID was calculated for each procedure cohort at 6 months of follow-up using distribution-based MCID methods, which included a fraction of the SD (1/2 or 1/3 SD) and minimum detectable change (MDC) using statistical significance (such as the MDC 90 from p < 0.1). Previously published anchor-based MCID thresholds from similar procedure cohorts and analogous PROMs were used to calculate the proportion reaching MCID. RESULTS: Within a given distribution-based method, MCID thresholds for PROMIS assessments were similar across multiple procedures. The MCID threshold ranged between 3.4 and 4.5 points across all procedures using the 1/2 SD method. Except for meniscectomy (3.5 points), the anchor-based PROMIS MCID thresholds (range 4.5 to 8.1 points) were higher than the SD distribution-based MCID values (2.3 to 4.5 points). The difference in MCID thresholds based on the calculation method led to a similar trend in MCID attainment. Using THA as an example, MCID attainment using PROMIS was achieved by 76% of patients using an anchor-based threshold of 7.9 points. However, 82% of THA patients attained MCID using the MDC 95 method (6.1 points), and 88% reached MCID using the 1/2 SD method (3.9 points). Using the HOOS metric (scaled from 0 to 100), 86% of THA patients reached the anchor-based MCID threshold (17.5 points). However, 91% of THA patients attained the MCID using the MDC 90 method (12.5 points), and 93% reached MCID using the 1/2 SD method (8.4 points). In general, the proportion of patients reaching MCID was lower for PROMIS than for other validated outcomes tools; for example, with the 1/2 SD method, 72% of patients who underwent arthroscopic partial meniscectomy reached the MCID on PROMIS Physical Function compared with 86% on KOOS. CONCLUSION: MCID calculations can provide clinical correlation for PROM scores interpretation. The PROMIS form is increasingly used because of its generalizability across diagnoses. However, we found lower proportions of MCID attainment using PROMIS scores compared with historical PROMs. By using historical proportions of attainment on common orthopaedic procedures and a spectrum of MCID calculation techniques, the PROMIS MCID benchmarks are realizable for common orthopaedic procedures. For clinical practices that routinely collect PROMIS scores in the clinical setting, these results can be used by individual surgeons to evaluate personal practice trends and by healthcare systems to quantify whether clinical care initiatives result in meaningful differences. Furthermore, these MCID thresholds can be used by researchers conducting retrospective outcomes research with PROMIS. LEVEL OF EVIDENCE: Level III, therapeutic study.

Does the SORG Orthopaedic Research Group Hip Fracture Delirium Algorithm Perform Well on an Independent Intercontinental Cohort of Patients With Hip Fractures Who Are 60 Years or Older?

BACKGROUND: Postoperative delirium in patients aged 60 years or older with hip fractures adversely affects clinical and functional outcomes. The economic cost of delirium is estimated to be as high as USD 25,000 per patient, with a total budgetary impact between USD 6.6 to USD 82.4 billion annually in the United States alone. Forty percent of delirium episodes are preventable, and accurate risk stratification can decrease the incidence and improve clinical outcomes in patients. A previously developed clinical prediction model (the SORG Orthopaedic Research Group hip fracture delirium machine-learning algorithm) is highly accurate on internal validation (in 28,207 patients with hip fractures aged 60 years or older in a US cohort) in identifying at-risk patients, and it can facilitate the best use of preventive interventions; however, it has not been tested in an independent population. For an algorithm to be useful in real life, it must be valid externally, meaning that it must perform well in a patient cohort different from the cohort used to "train" it. With many promising machine-learning prediction models and many promising delirium models, only few have also been externally validated, and even fewer are international validation studies. QUESTION/PURPOSE: Does the SORG hip fracture delirium algorithm, initially trained on a database from the United States, perform well on external validation in patients aged 60 years or older in Australia and New Zealand? METHODS: We previously developed a model in 2021 for assessing risk of delirium in hip fracture patients using records of 28,207 patients obtained from the American College of Surgeons National Surgical Quality Improvement Program. Variables included in the original model included age, American Society of Anesthesiologists (ASA) class, functional status (independent or partially or totally dependent for any activities of daily living), preoperative dementia, preoperative delirium, and preoperative need for a mobility aid. To assess whether this model could be applied elsewhere, we used records from an international hip fracture registry. Between June 2017 and December 2018, 6672 patients older than 60 years of age in Australia and New Zealand were treated surgically for a femoral neck, intertrochanteric hip, or subtrochanteric hip fracture and entered into the Australian & New Zealand Hip Fracture Registry. Patients were excluded if they had a pathological hip fracture or septic shock. Of all patients, 6% (402 of 6672) did not meet the inclusion criteria, leaving 94% (6270 of 6672) of patients available for inclusion in this retrospective analysis. Seventy-one percent (4249 of 5986) of patients were aged 80 years or older, after accounting for 5% (284 of 6270) of missing values; 68% (4292 of 6266) were female, after accounting for 0.06% (4 of 6270) of missing values, and 83% (4690 of 5661) of patients were classified as ASA III/IV, after accounting for 10% (609 of 6270) of missing values. Missing data were imputed using the missForest methodology. In total, 39% (2467 of 6270) of patients developed postoperative delirium. The performance of the SORG hip fracture delirium algorithm on the validation cohort was assessed by discrimination, calibration, Brier score, and a decision curve analysis. Discrimination, known as the area under the receiver operating characteristic curves (c-statistic), measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities, a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. RESULTS: The SORG hip fracture algorithm, when applied to an external patient cohort, distinguished between patients at low risk and patients at moderate to high risk of developing postoperative delirium. The SORG hip fracture algorithm performed with a c-statistic of 0.74 (95% confidence interval 0.73 to 0.76). The calibration plot showed high accuracy in the lower predicted probabilities (intercept -0.28, slope 0.52) and a Brier score of 0.22 (the null model Brier score was 0.24). The decision curve analysis showed that the model can be beneficial compared with no model or compared with characterizing all patients as at risk for developing delirium. CONCLUSION: Algorithms developed with machine learning are a potential tool for refining treatment of at-risk patients. If high-risk patients can be reliably identified, resources can be appropriately directed toward their care. Although the current iteration of SORG should not be relied on for patient care, it suggests potential utility in assessing risk. Further assessment in different populations, made easier by international collaborations and standardization of registries, would be useful in the development of universally valid prediction models. The model can be freely accessed at: https://sorg-apps.shinyapps.io/hipfxdelirium/ . LEVEL OF EVIDENCE: Level III, therapeutic study.