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

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.

Purpose This study aimed to investigate spine surgeons’ ability to estimate survival in patients with spinal metastases and whether survival estimates influence treatment recommendations. Methods 60 Spine surgeons were asked a survival estimate and treatment recommendation in 12 cases. Intraclass correlation coefficients and descriptive statistics were used to evaluate variability, accuracy and association of survival estimates with treatment recommendation. Results There was substantial variability in survival estimates amongst the spine surgeons. Survival was generally overestimated, and longer estimated survival seemed to lead to more invasive procedures. Conclusions Prognostic models to estimate survival may aid surgeons treating patients with spinal metastases.

➤ As the Patient-Reported Outcomes Measurement Information System (PROMIS) is increasingly utilized in orthopaedic research and clinical practice, there is not a consensus regarding the minimum clinically important difference (MCID) calculation. ➤ The varied MCID calculation methods can lead to a range of possible values, which limits the translatability of research efforts. ➤ The completion rate and follow-up period also influence MCID values and should be reported alongside study results.

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

BACKGROUND Intraoperative vascular injury (VI) may be an unavoidable complication of anterior lumbar spine surgery; however, vascular injury has implications for quality and safety reporting as this intraoperative complication may result in serious bleeding, thrombosis, and postoperative stricture. PURPOSE The purpose of this study was to (1) develop machine learning algorithms for preoperative prediction of VI and (2) develop natural language processing (NLP) algorithms for automated surveillance of intraoperative VI from free-text operative notes. PATIENT SAMPLE Adult patients, 18 years or age or older, undergoing anterior lumbar spine surgery at two academic and three community medical centers were included in this analysis. OUTCOME MEASURES The primary outcome was unintended VI during anterior lumbar spine surgery. METHODS Manual review of free-text operative notes was used to identify patients who had unintended VI. The available population was split into training and testing cohorts. Five machine learning algorithms were developed for preoperative prediction of VI. An NLP algorithm was trained for automated detection of intraoperative VI from free-text operative notes. Performance of the NLP algorithm was compared to current procedural terminology and international classification of diseases codes. RESULTS In all, 1035 patients underwent anterior lumbar spine surgery and the rate of intraoperative VI was 7.2% (n=75). Variables used for preoperative prediction of VI were age, male sex, body mass index, diabetes, L4-L5 exposure, and surgery for infection (discitis, osteomyelitis). The best performing machine learning algorithm achieved c-statistic of 0.73 for preoperative prediction of VI ( https://sorg-apps.shinyapps.io/lumbar_vascular_injury/ ). For automated detection of intraoperative VI from free-text notes, the NLP algorithm achieved c-statistic of 0.92. The NLP algorithm identified 18 of the 21 patients (sensitivity 0.86) who had a VI whereas current procedural terminologyand international classification of diseases codes identified 6 of the 21 (sensitivity 0.29) patients. At this threshold, the NLP algorithm had a specificity of 0.93, negative predictive value of 0.99, positive predictive value of 0.51, and F1-score of 0.64. CONCLUSION Relying on administrative procedural and diagnosis codes may underestimate the rate of unintended intraoperative VI in anterior lumbar spine surgery. External and prospective validation of the algorithms presented here may improve quality and safety reporting.

BACKGROUND CONTEXT Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling. PURPOSE To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis. STUDY DESIGN/SETTING Retrospective, cohort study PATIENT SAMPLE Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis. RESULTS Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients. CONCLUSIONS With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease.

Background Context Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and one-year mortality of patients with metastatic cancer to the spine has been multiply validated, with a high degree of accuracy in both internal and external validation studies. However, prior external validations were conducted using patient groups located on the east coast of the United States, representing a generally homogeneous population. The aim of this study was to externally validate the SORG algorithms with a Taiwanese population. Study Design/Setting Retrospective study at a single tertiary care center in Taiwan Patient Sample Four hundred and twenty-seven patients who underwent surgery for metastatic spine disease from November 1, 2010 to December 31, 2018 Outcome Measures 90-day and one-year mortality Methods The baseline characteristics of our validation cohort were compared with those of the previously published developmental and external validation cohorts. Discrimination (c-statistic and receiver operating curve), calibration (calibration plot, intercept, and slope), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in this cohort. Results Ninety-day and one-year mortality rates were 110 of 427 (26%) and 256 of 427 (60%), respectively. The external validation cohort and the developmental cohort differed in body mass index (BMI), preoperative performance status, American Spinal Injury Association impairment scale, primary tumor histology and in several laboratory measurements. The SORG ML algorithm for 90-day and 1-year mortality demonstrated a high level of discriminative ability (c-statistics of 0.73 [95% confidence interval [CI], 0.67–0.78] and 0.74 [95% CI, 0.69−0.79]), overall performance, and had a positive net benefit throughout the range of threshold probabilities in decision curve analysis. The algorithm for 1-year mortality had a calibration intercept of 0.08, representing a good calibration. However, the 90-day mortality algorithm underestimated mortality for the lowest predicted probabilities, with an overall intercept of 0.81. Conclusions The SORG algorithms for predicting 90-day and 1-year mortality in patients with spinal metastatic disease generally performed well on international external validation in a predominately Taiwanese population. However, 90-day mortality was underestimated in this group. Whether this inconsistency was due to different primary tumor characteristics, body mass index, selection bias or other factors remains unclear, and may be better understood with further validative works that utilize international and/or diverse populations.

Connecting pathways are essential for cultural and economic exchange. Commonly, historians investigate the role of routes for cultural development, whereas the environmental impacts of historical routes attract less attention. Here, we present a high-resolution reconstruction of the impact of the major trade route via Marchionis in the southern Baltic lowlands on landscape evolution since more than 800 years. We combine precisely dated annually laminated sediments from Lake Czechowskie alongside via Marchionis and pollen data at 5-year resolution together with historical data. The transformation from a quasi-natural to a cultural landscape occurred in three phases (1) an early phase until the mid-fourteenth century with slowly increasing human impact. (2) an intensification of environmental disturbance until (3) the mid-nineteenth century when via Marchionis became a modern traffic route with strong environmental impacts. Superimposed on the long-term development were repeated interruptions by short-term downturns related to societal crisis and political decisions.

BACKGROUND CONTEXT: The field of artificial intelligence (AI) is rapidly advancing, especially with recent improvements in deep learning (DL) techniques. Augmented (AR) and virtual reality (VR) are finding their place in healthcare, and spine surgery is no exception. The unique capabilities and advantages of AR and VR devices include their low cost, flexible integration with other technologies, user-friendly features and their application in navigation systems, which makes them beneficial across different aspects of spine surgery. Despite the use of AR for pedicle screw placement, targeted cervical foraminotomy, bone biopsy, osteotomy planning, and percutaneous intervention, the current applications of AR and VR in spine surgery remain limited. PURPOSE: The primary goal of this study was to provide the spine surgeons and clinical researchers with the general information about the current applications, future potentials, and accessibility of AR and VR systems in spine surgery. STUDY DESIGN/SETTING: We reviewed titles of more than 250 journal papers from google scholar and PubMed with search words: augmented reality, virtual reality, spine surgery, and orthopaedic, out of which 89 related papers were selected for abstract review. Finally, full text of 67 papers were analyzed and reviewed. METHODS: The papers were divided into four groups: technological papers, applications in surgery, applications in spine education and training, and general application in orthopaedic. A team of two reviewers performed paper reviews and a thorough web search to ensure the most updated state of the art in each of four group is captured in the review. RESULTS: In this review we discuss the current state of the art in AR and VR hardware, their preoperative applications and surgical applications in spine surgery. Finally, we discuss the future potentials of AR and VR and their integration with AI, robotic surgery, gaming, and wearables. CONCLUSIONS: AR and VR are promising technologies that will soon become part of standard of care in spine surgery. (C) 2021 Published by Elsevier Inc.

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

OBJECTIVE The effectiveness of starting systemic therapies after surgery for spinal metastases from renal cell carcinoma (RCC) has not been evaluated in randomized controlled trials. Agents that target tyrosine kinases, mammalian target of rapamycin signaling, and immune checkpoints are now commonly used. Variables like sarcopenia, nutritional status, and frailty may impact recovery from spine surgery and are considered when evaluating a patient’s candidacy for such treatments. A better understanding of the significance of these variables may help improve patient selection for available treatment options after surgery. The authors used comparative effectiveness methods to study the treatment effect of postoperative systemic therapies (PSTs) on survival. METHODS Univariable and multivariable Cox regression analyses were performed to determine factors associated with overall survival (OS) in a retrospective cohort of adult patients who underwent spine surgery for metastatic RCC between 2010 and 2019. Propensity score–matched (PSM) analysis and inverse probability weighting (IPW) were performed to determine the treatment effect of PST on OS. To address confounding and minimize bias in estimations, PSM and IPW were adjusted for covariates, including age, sex, frailty, sarcopenia, nutrition, visceral metastases, International Metastatic RCC Database Consortium (IMDC) risk score, and performance status. RESULTS In total, 88 patients (73.9% male; median age 62 years, range 29–84 years) were identified; 49 patients (55.7%) had an intermediate IMDC risk, and 29 (33.0%) had a poor IMDC risk. The median follow-up was 17 months (range 1–104 months) during which 57 patients (64.7%) died. Poor IMDC risk (HR 3.2 [95% CI 1.08–9.3]), baseline performance status (Eastern Cooperative Oncology Group score 3 or 4; HR 2.7 [95% CI 1.5–4.7]), and nutrition (prognostic nutritional index [PNI] first tertile, PNI < 40.74; HR 2.69 [95% CI 1.42–5.1]) were associated with worse OS. Sarcopenia and frailty were not significantly associated with poor survival. PST was associated with prolonged OS, demonstrated by similar effects from multivariable Cox analysis (HR 0.55 [95% CI 0.30–1.00]), PSM (HR 0.53 [95% CI 0.29–0.93]), IPW (HR 0.47 [95% CI 0.24–0.94]), and comparable confidence intervals. The median survival for those receiving PST was 28 (95% CI 19–43) months versus 12 (95% CI 4–37) months for those who only had surgery (log-rank p = 0.027). CONCLUSIONS This comparative analysis demonstrated that PST is associated with improved survival in specific cohorts with metastatic spinal RCC after adjusting for frailty, sarcopenia, and malnutrition. The marked differences in survival should be taken into consideration when planning for surgery.

Spinal metastases are an increasing societal health burden secondary to improvements in systemic therapy. Estimates indicate that 100,000 or more people have symptomatic spine metastases requiring management. While open surgery and external beam radiotherapy have been the pillars of treatment, there is growing interest in more minimally invasive technologies (eg separation surgery) and non-operative interventions (eg percutaneous cementoplasty, stereotactic radiosurgery). The great expansion of these alternatives to open surgery and the prevalence of adjuvant therapeutic options means that treatment decision making is now complex and reliant upon multidisciplinary collaboration. To help facilitate construction of care plans that meet patient goals and expectations, clinical decision aids and prognostic scores have been developed. These have been shown to have superior predictive value relative to more classic prediction models and may become an increasingly important aspect of the clinical practice of spinal oncology. Here we overview current therapeutic advances in the management of spine metastases and highlight emerging areas for research. Given the rapid advancements in surgical technologies and adjuvants, the field is likely to undergo further transformative changes in the coming decade.

BACKGROUND: Operative and nonoperative treatments for spinal metastases are expensive interventions with a high rate of complications. We sought to determine the cost-effectiveness of a surgical procedure compared with nonoperative management as treatment for spinal metastases. METHODS: We constructed a Markov state-transition model with health states defined by ambulatory status and estimated the quality-adjusted life-years (QALYs) and costs for operative and nonoperative management of spine metastases. We considered 2 populations: 1 in which patients presented with independent ambulatory status and 1 in which patients presented with nonambulatory status due to acute (e.g.

As cost-effective measures become increasingly implemented in the US healthcare system, changes in patient-reported outcome measure (PROM) scores can be utilized to indicate patient satisfaction following procedures including total knee arthroplasty (TKA). The primary aim of this study was to develop and evaluate machine learning algorithms to predict achievement of the minimal clinically important difference (MCID) for the Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS) at 1-year following TKA. A retrospective review of primary TKA patients between 2016 and 2018 was performed. Variables considered for prediction included demographics and preoperative PROMs. The KOOS-PS MCID was calculated via a distribution-based method. Five machine learning algorithms were developed and tested by discrimination, calibration, Brier score, and decision curve analysis. Among the 744 patients who met the inclusion criteria, 385 (72.8%) patients achieved the MCID. The elastic-net penalized logistic regression model was selected as the best performing model (c-statistic 0.77, calibration intercept -0.02, calibration slope 1.15, and Brier score 0.14). The most important variables for MCID achievement were preoperative KOOS-PS score, preoperative VAS Pain, preoperative opioid use, preoperative PROMIS global mental health score, age, and sex. Algorithms were incorporated into an open-access digital application available at https://sorg-apps.shinyapps.io/tka_koos_mcid/. This study is the first to predict the probability of achieving the KOOS-PS MCID following TKA using a machine learning-based approach. The results were used to develop a clinical decision aid based on commonly collected predictive variables to preoperatively predict an individual patient's likelihood of attaining an acceptable outcome following TKA.

The microinvasive nature of suprafascial myxofibrosarcoma reduces the accuracy of intraoperative margin assessment, and tumor bed resections after soft-tissue reconstruction are unreliable. In 2017, we began temporizing the excised tumor bed with a wound VAC, delaying soft-tissue coverage until final negative margins were achieved. We compare the oncologic/surgical outcomes of suprafascial myxofibrosarcomas managed with VAC temporization (VT) with single-stage excision/reconstruction (SS). We retrospectively studied suprafascial myxofibrosarcomas managed from January 1, 2000 to January 1, 2019 for patients who received neoadjuvant or adjuvant radiation and had at least 2 years of oncologic follow-up at a tertiary referral cancer center. Our primary outcome was local recurrence. Comparisons were performed by using Fisher’s exact test or Student’s t test. A p value < 0.05 was considered significant. Fifty-three patients (18 VAC temporized, 35 single stage) were included. While VT patients were older (74.9 ± 10.2 vs. 63.9 ± 13.6, p = 0.003), treatment groups did not significantly differ with respect to comorbidity, tumor volume, stage and grade. VT patients had significantly fewer local recurrences (5.6% vs. 28.6% after SS, p = 0.048) and R1 resections that required an unplanned readmission for tumor bed reexcision (0% vs. 37.1% after SS, p = 0.002). VT required more total surgeries (2.8 ± 0.9 vs. 1.8 ± 0.9 for SS, p = 0.0002). Postoperative infectious and wound complications were equivalent. Our VAC temporization strategy had a significantly lower LR than SS treatment. While high quality multi-institutional validation is required, VT may represent a paradigm shift in the management of myxofibrosarcoma.

Background There are no effective systemic therapies for chordoma. The recent successes of immunotherapeutic strategies in other cancers have resulted in a resurgence of interest in using immunotherapy in chordoma. These approaches rely on a functional interaction between the host's immune system and the expression of tumor peptides via the human leukocyte antigen (HLA) Class I antigen. It is not known whether chordoma cells express the HLA Class I antigen. Questions/purposes (1) Do chordoma tumors exhibit defects in HLA Class I antigen expression? (2) What is the pattern of lymphocyte infiltration in chordoma tumors? Methods Patients with chordoma treated at Massachusetts General Hospital between 1989 and 2009 were identified with permission from the institutional review board. Of the 75 patients who were identified, 24 human chordoma tumors were selected from 24 distinct patients based on tissue availability. Histology slides from these 24 formalin-fixed paraffin-embedded chordoma tissue samples were deparaffinized using xylene and ethanol and underwent heat-induced antigen retrieval in a citrate buffer. Samples were incubated with monoclonal antibodies directed against HLA Class I antigen processing machinery components. Antibody binding was detected via immunohistochemical staining. Staining intensity (negative, weakly positive, strongly positive) was assessed semiquantitatively and the percentage of chordoma cells stained for HLA Class I antigen subunits was assessed quantitatively. Hematoxylin and eosin-stained histology slides from the same 24 chordoma samples were assessed qualitatively for the presence of tumor-infiltrating lymphocytes and histologic location of these lymphocytes. Immunohistochemical staining with monoclonal antibodies directed against CD4 and CD8 was performed in a quantitative manner to identify the lymphocyte subtype present in chordoma tumors. All results were scored independently by two investigators and were confirmed by a senior bone and soft tissue pathologist. Results Seven of 24 chordoma samples exhibited no staining by the anti-HLA-A heavy chain monoclonal antibody HC-A2, two had weak staining intensity, and eight had a heterogeneous staining pattern, with fewer than 60% of chordoma cells exhibiting positive staining results. Four of 24 samples tested were not stained by the anti-HLA-B/C heavy chain monoclonal antibody HC-10, five had weak staining intensity, and 11 displayed a heterogeneous staining pattern. For the anti-β-2-microglobulin monoclonal antibody NAMB-1, staining was detected in all samples, but 11 had weak staining intensity and four displayed a heterogeneous staining pattern. Twenty-one of 24 samples tested had decreased expression in at least one subunit of HLA Class I antigens. No tumors were negative for all three subunits. Lymphocytic infiltration was found in 21 of 24 samples. Lymphocytes were primarily found in the fibrous septae between chordoma lobules but also within the tumor lobules and within the fibrous septae and tumor lobules. Twenty-one of 24 tumors had CD4+ T cells and 11 had CD8+ T cells. Conclusion In chordoma tissue samples, HLA Class I antigen defects commonly were present, suggesting a mechanism for escape from host immunosurveillance. Additionally, nearly half of the tested samples had cytotoxic CD8+ T cells present in chordoma tumors, suggesting that the host may be capable of mounting an immune response against chordoma tumors. The resulting selective pressure imposed on chordoma tumors may lead to the outgrowth of chordoma cell subpopulations that can evade the host's immune system. Clinical relevance These findings have implications in the design of immunotherapeutic strategies for chordoma treatment. T cell recognition of tumor cells requires HLA Class I antigen expression on the targeted tumor cells. Defects in HLA Class I expression may play a role in the clinical course of chordoma and may account for the limited or lack of efficacy of T cell-based immunity triggered by vaccines and/or checkpoint inhibitors.

Soft tissue sarcoma (STS) frequently requires high-risk surgery that predisposes patients to complex wounds. Past studies have identified a variety of tumor characteristics as risk factors for wound infection (WI); however, physiologic characteristics have not yet been studied in this population. Thus, the objective of this study is to identify any nutritional indicators and physiologic characteristics associated with the development of WI. 633 patients from a large tertiary care center institution were identified with lower extremity STS removed from 1992 to 2017. The primary outcomes of interest were WI at patient’s surgical site within 90 days of surgery and additional procedure due to wound infection. Patients’ laboratory values, comorbidities, and other characteristics were assessed using multivariable analysis to determine risk factors for WI. Receiver operator characteristic (ROC) curves were used for analysis of plasma glucose and albumin levels to determine a useful risk threshold. Significance was determined to be p < 0.05. Postoperative plasma glucose levels were significantly higher among patients with WI compared with those without (p < 0.001) and showed predictivity in ROC analysis (AUC 0.77, 95% CI 0.72–0.82). Preoperative albumin (p < 0.001) and prognostic nutritional index score (p = 0.002) were significantly lower among patients with WI. Partial thromboplastin time (PTT), international normalized ratio (INR), white blood cell count (WBC), and platelet count values had no effect on WI. Smoking elevated risk for WI (OR 1.64, p < 0.01). Significant risk factors were the same when assessed for those with WI undergoing additional procedures. Postoperative plasma glucose levels, preoperative albumin levels, and smoking status are useful nutritional variables in predicting WI in STS excisional procedures.

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

BACKGROUND CONTENT Cervical laminoplasty (LP) and laminectomy and fusion (LF) are commonly used surgical techniques for cervical spondylotic myelopathy (CSM). Several recent studies have demonstrated superior perioperative metrics and decreased overall costs with LP, yet LF is performed far more often in the United States. PURPOSE To determine the percentage of patients with CSM who are radiographically candidates for LP. STUDY DESIGN Retrospective comparative cohort study. PATIENT SAMPLE Patients >18 years old who underwent LF or LP for CSM at 2 large academic institutions from 2017 to 2019. OUTCOME MEASURES Candidacy for LP based on radiographic criteria. METHODS Radiographs were assessed by 2 spine surgeons not involved in the care of the patients to determine the C2–C7 Cobb angle and the presence and extent of cervical instability. Patients with kyphosis >13°, > 3.5 mm of listhesis on static imaging, or > 2.5 mm of motion on flexion-extension or standing-supine films were not considered candidates for LP. Intraclass coefficient (ICC) was calculated to assess the interobserver reliability of angular measurements and the presence of instability. The percentage of patients for whom LP was contraindicated was calculated. RESULTS One hundred eight patients underwent LF while 142 underwent LP. Of the 108 patients who underwent LF, 79.6% were radiographically deemed candidates for LP, as were all 142 patients who underwent LP. The ICC for C2–C7 alignment was 0.90; there was 97% agreement with respect to the presence of instability. CONCLUSIONS In 250 patients with CSM, 228 (91.2%) were radiographically candidates for LP. These data suggest that LP may be an underutilized procedure for the treatment for CSM.

In the last 5 years, artificial intelligence (AI) algorithms have made rapid advances for diagnosis and prognosis in fields ranging from dermatology to anesthesiology. How do we make sense of the rise of AI in healthcare and specifically in spine? How much of what we see today is “hype” and what will remain when the dust settles? In this special issue, several reviews and original articles help us understand the state of AI in healthcare today, the avenues for future progress, and the implications for spine care. Continued engagement, skepticism, and collaboration with technical experts will allow for the development of AI systems that complement and expand our abilities to diagnose, predict, and operate.

The recent impressive clinical responses to antibody-based immunotherapy have prompted the identification of clinically relevant tumor antigens that can serve as targets in solid tumors. Among them, B7-H3, a member of the B7 ligand family, represents an attractive target for antibody-based immunotherapy, it is overexpressed on differentiated malignant cells and cancer-initiating cells, with limited heterogeneity, and high frequency (60% of 25,000 tumor samples) in many different cancer types, but has a limited expression at low level in normal tissues. In nonmalignant tissues, B7-H3 has a predominantly inhibitory role in adaptive immunity, suppressing T-cell activation and proliferation. In malignant tissues, B7-H3 inhibits tumor antigen–specific immune responses, leading to a protumorigenic effect. B7-H3 also has nonimmunologic protumorigenic functions, such as promoting migration and invasion, angiogenesis, chemoresistance, and endothelial-to-mesenchymal transition, as well as affecting tumor cell metabolism. As a result, B7-H3 expression in tumors is associated with poor prognosis. Although experimental B7-H3 silencing reduces cancer cell malignant potential, there has been limited emphasis on the development of B7-H3–blocking antibodies, most likely because the B7-H3 receptor remains unknown. Instead, many antibody-based strategies utilizing distinct effector mechanisms to target B7-H3–expressing cancer cells have been developed. These strategies have demonstrated potent antitumor activity and acceptable safety profiles in preclinical models. Ongoing clinical trials are assessing their safety and efficacy in patients. Identification of the B7-H3 receptor will improve our understanding of its role in tumor immunity, and will suggest rational strategies to develop blocking antibodies, which may enhance the therapeutic efficacy of tumor immunity.

BACKGROUND CONTEXT Patient-Reported Outcome Measurement Information System (PROMIS) scores are increasingly utilized in clinical care. However, it is unclear if PROMIS can discriminate surgeon performance on an individual level. PURPOSE The purpose of this study was to examine surgeon-level variance in rates of achieving minimal clinically important difference (MCID) after lumbar decompression. PATIENT SAMPLE This is a prospective, observational cohort study performed across a healthcare enterprise (two academic medical centers and three community centers). Patients 18 years or older undergoing one- to two-level primary decompression for lumbar disc herniation (LDH) or lumbar spinal stenosis (LSS) were included. OUTCOME MEASURES The primary outcome was achievement of MCID, using a distribution-based method, on paired PROMIS physical function scores. METHODS Descriptive statistics were generated to examine the baseline characteristics of the study cohort. Bivariate analyses were used to examine the impact of surgeon-level variance on rates of MCID. Multivariable analyses were used to examine the risk-adjusted impact of surgeon-level variance on rates of MCID. RESULTS Overall, 636 patients treated by nine surgeons were included. The median patient age was 58 [interquartile range (IQR): 46–70] and 62.3% (n=396) were female. Among all patients, 56.9% (n=362) underwent surgery for LDH. The overall rate of achieving MCID was 75.8% (n=482). Of the surgeons, the median years in practice were 12 (range 4–31) and 55.6% (n=5) were in academic practice settings. On bivariate analysis, patients treated by one of the surgeons had lower rates of achieving MICD (odds ratio=0.37, 95% confidence interval: 0.15–0.91, p=.03). However, on multivariable analysis adjusting for operative indication (LDH vs. LSS), body mass index, number of comorbidities, percent unemployment in patient zip code, and preoperative PROMIS physical function scores, all surgeons were equally likely to obtain MCID. CONCLUSIONS In this cohort, variance in PROMIS scores after primary lumbar decompression is influenced by patient-related factors and not by individual surgeon. Adequate risk adjustment is needed if ascertaining clinical improvement on an individual surgeon basis. Level of Evidence 2

Background Context The New England Spinal Metastasis Score (NESMS) was proposed as an intuitive and accessible prognostic tool for predicting survival in patients with spinal metastases. We designed an appropriately powered, prospective, longitudinal investigation to validate the NESMS. Purpose To prospectively validate the NESMS. Study Design Prospective longitudinal observational cohort study. Patient Sample Patients, aged 18 and older, presenting for treatment with spinal metastatic disease. Outcome Measures One-year mortality (primary); 6-month mortality and mortality at any time point following enrollment (secondary). Methods The date of enrollment was set as time zero for all patients. The NESMS was assigned based on data collected at the time of enrollment. Patients were prospectively followed to one of two pre-determined end-points: death, or survival at 365 days following enrollment. Survival was visually assessed with Kaplan-Meier curves and then analyzed using multivariable logistic regression, followed by Bayesian regression to assess for robustness of point estimates and 95% confidence intervals (CI). Results This study included 180 patients enrolled between 2017-2018. Mortality within 1-year occurred in 56% of the cohort. Using NESMS 3 as the referent, those with a score of 2 had significantly greater odds of mortality (OR 7.04; 95% CI 2.47, 20.08), as did those with a score of 1 (OR 31.30; 95% CI 8.82, 111.04). A NESMS score of 0 was associated with perfect prediction, as 100% of individuals with this score were deceased at 1-year. Similar determinations were encountered for our mortality at 6-months and overall. Conclusions This study validates the NESMS and demonstrates its utility in prognosticating survival for patients with spinal metastatic disease, irrespective of selected treatment strategy. This is the first study to prospectively validate a prognostic utility for patients with spinal metastases. The NESMS can be directly applied to patient care, hospital-based practice and healthcare policy.

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

INTRODUCTION: Recent advances in immuno-oncology and bioengineering have rekindled the interest in monoclonal antibody (mAb)-based immunotherapies for malignancies. Crucial for their success is the identification of tumor antigens (TAs) that can serve as targets. B7-H3, a member of the B7 ligand family, represents such a TA. Although its exact functions and receptor(s) remain unclear, B7-H3 has predominantly a pro-tumorigenic effect mainly by suppressing the anti-tumor functions of T-cells. AREAS COVERED: Initially we present a historical perspective on TA-specific antibodies for diagnosis and treatment of malignancies. Following a description of the TA requirements to be an attractive antibody-based immunotherapy target, we show that B7-H3 fulfills these criteria. We discuss its structure and functions. In a review and pooled analysis, we describe the limited B7-H3 expression in normal tissues and estimate B7-H3 expression frequency in tumors, tumor-associated vasculature and cancer initiating cells (CICs). Lastly, we discuss the association of B7-H3 expression in tumors with poor prognosis. EXPERT OPINION: B7-H3 is an attractive target for mAb-based cancer immunotherapy. B7-H3-targeting strategies are expected to be highly effective and – importantly – safe. To fully exploit the diagnostic and therapeutic potential of B7-H3, its expression in pre-malignant lesions, serum, metastases, and CICs requires further investigation.

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

Author(s): Mallow, G Michael; Siyaji, Zakariah K; Galbusera, Fabio; Espinoza-Orias, Alejandro A; Giers, Morgan; Lundberg, Hannah; Ames, Christopher; Karppinen, Jaro; Louie, Philip K; Phillips, Frank M; Pourzal, Robin; Schwab, Joseph; Sciubba, Daniel M; Wang, Jeffrey C; Wilke, Hans-Joachim; Williams, Frances MK; Mohiuddin, Shoeb A; Makhni, Melvin C; Shepard, Nicholas A; An, Howard S; Samartzis, Dino

OBJECTIVEReconstruction of the mobile spine following total en bloc spondylectomy (TES) of one or multiple vertebral bodies in patients with malignant spinal tumors is a challenging procedure with high failure rates. A common reason for reconstructive failure is nonunion, which becomes more problematic when using local radiation therapy. Radiotherapy is an integral part of the management of primary malignant osseous tumors in the spine. Vascularized grafts may help prevent nonunion in the radiotherapy setting. The authors have utilized free vascularized fibular grafts (FVFGs) for reconstruction of the spine following TES. The purpose of this article is to describe the surgical technique for vascularized reconstruction of defects after TES. Additionally, the outcomes of consecutive cases treated with this technique are reported.METHODSThirty-nine patients were treated at the authors’ tertiary care institution for malignant tumors in the mobile spine using FVFG following TES between 2010 and 2018. Postoperative union, reoperations, complications, neurological outcome, and survival were reported. The median follow-up duration was 50 months (range 14–109 months).RESULTSThe cohort consisted of 26 males (67%), and the median age was 58 years. Chordoma was the most prevalent tumor (67%), and the lumbar spine was most affected (46%). Complete union was seen in 26 patients (76%), the overall complication rate was 54%, and implant failure was the most common complication, with 13 patients (33%) affected. In 18 patients (46%), one or more reoperations were needed, and the fixation was surgically revised 15 times (42% of reoperations) in 10 patients (26%). A reconstruction below the L1 vertebra had a higher proportion of implant failure (67%; 8 of 12 patients) compared with higher resections (21%; 5 of 24 patients) (p = 0.011). Graft length, number of resected vertebrae, and docking the FVFG on the endplate or cancellous bone was not associated with union or implant failure on univariate analysis.CONCLUSIONSThe FVFG is an effective reconstruction technique, particularly in the cervicothoracic spine. However, high implant failure rates in the lumbar spine have been seen, which occurred even in cases in which the graft completely healed. Methods to increase the weight-bearing capacity of the graft in the lumbar spine should be considered in these reconstructions. Overall, the rates of failure and revision surgery for FVFG compare with previous reports on reconstruction after TES.

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

OBJECTIVEAlthough several prognostic scores for spinal metastatic disease have been developed in the past 2 decades, the applicability and validity of these models to specific cancer types are not yet clear. Most of the data used for model formation are from small population sets and have not been updated or externally validated to assess their performance. Developing predictive models is clinically relevant as prognostic assessment is crucial to optimal decision-making, particularly the decision for or against spine surgery. In this study, the authors investigated the performance of various spinal metastatic disease risk models in predicting prognosis for spine surgery to treat metastatic renal cell carcinoma (RCC).METHODSData of patients who underwent surgery for RCC metastatic to the spine at 2 tertiary centers between 2010 and 2019 were retrospectively retrieved. The authors determined the prognostic value associated with the following scoring systems: the Tomita score, original and revised Tokuhashi scores, original and modified Bauer scores, Katagiri score, the Skeletal Oncology Research Group (SORG) classic algorithm and nomogram, and the New England Spinal Metastasis Score (NESMS). Regression analysis of patient variables in association with 1-year survival after surgery was assessed using Cox proportional hazard models. Calibration and time-dependent discrimination analysis were tested to quantify the accuracy of each scoring system at 3 months, 6 months, and 1 year.RESULTSA total of 86 metastatic RCC patients were included (median age 64 years [range 29–84 years]; 63 males [73.26%]). The 1-year survival rate was 72%. The 1-year survival group had a good performance status (Karnofsky Performance Scale [KPS] score 80%–100%) and an albumin level > 3.5 g/dL (p < 0.05). Multivariable-adjusted Cox regression analysis showed that poor performance status (KPS score < 70%), neurological deficit (Frankel grade A–D), and hypoalbuminemia (< 3.5 g/dL) were associated with a higher risk of death before 1 year (p < 0.05). The SORG nomogram, SORG classic, original Tokuhashi, and original Bauer demonstrated fair performance (0.7 < area under the curve < 0.8). The NESMS differentiates survival among the prognostic categories with the highest accuracy (area under the curve > 0.8).CONCLUSIONSThe present study shows that the most cited and commonly used scoring systems have a fair performance predicting survival for patients undergoing spine surgery for metastatic RCC. The NESMS had the best performance at predicting 1-year survival after surgery.

BACKGROUND Surgical site infections are a major driver of morbidity and increased costs in the postoperative period after spine surgery. Current tools for surveillance of these adverse events rely on prospective clinical tracking, manual retrospective chart review, or administrative procedural and diagnosis codes. PURPOSE The purpose of this study was to develop natural language processing (NLP) algorithms for automated reporting of postoperative wound infection requiring reoperation after lumbar discectomy. PATIENT SAMPLE Adult patients undergoing discectomy at two academic and three community medical centers between January 1, 2000 and July 31, 2019 for lumbar disc herniation. OUTCOME MEASURES Reoperation for wound infection within 90 days after surgery METHODS Free-text notes of patients who underwent surgery from January 1, 2000 to December 31, 2015 were used for algorithm training. Free-text notes of patients who underwent surgery after January 1, 2016 were used for algorithm testing. Manual chart review was used to label which patients had reoperation for wound infection. An extreme gradient-boosting NLP algorithm was developed to detect reoperation for postoperative wound infection. RESULTS Overall, 5,860 patients were included in this study and 62 (1.1%) had a reoperation for wound infection. In patients who underwent surgery after January 1, 2016 (n=1,377), the NLP algorithm detected 15 of the 16 patients (sensitivity=0.94) who had reoperation for infection. In comparison, current procedural terminology and international classification of disease codes detected 12 of these 16 patients (sensitivity=0.75). At a threshold of 0.05, the NLP algorithm had positive predictive value of 0.83 and F1-score of 0.88. CONCLUSION Temporal validation of the algorithm developed in this study demonstrates a proof-of-concept application of NLP for automated reporting of adverse events after spine surgery. Adapting this methodology for other procedures and outcomes in spine and orthopedics has the potential to dramatically improve and automatize quality and safety reporting.

Narrative review.To provide a narrative review for diagnosis and management of Primary spine tumors.A detailed review of literature was done to identify relevant and well cited manuscripts to construct this narrative review.Primary tumors of the spine are rare with some racial differences reported. There are numerous adjuvant technologies and developments that influence the way we currently manage these tumors. Collimated radiation allows for heavy dosage to be delivered and have been reported to give good local control both as an adjuvant and neoadjuvant setting. These have made surgical decision making even more intricate needing a multicentric approach. Dedicated care has been shown to significantly improve health quality of life measures and survival.While, it is beyond the scope of this paper to discuss all primary tumors subtypes individually, this review highlights the developments and approach to primary spine tumors.

Lesions of bone featuring osteoclast-like giant cells comprise a diverse group of entities, including giant cell tumor (GCT) of bone, chondroblastoma, and aneurysmal bone cyst, among others. The receptor activator of nuclear factor-κB ligand (RANKL) has been implicated in the pathogenesis of GCT of bone and may play a role in the pathogenesis of other giant cell-rich lesions as well. In addition, RANKL inhibitors (denosumab) have also been shown to have some efficacy in treating some giant cell-rich lesions. Herein, we examine RANKL expression by RNA in situ hybridization in a total of 84 osseous lesions with a focus on chondroblastoma, GCT, fibrous dysplasia, and aneurysmal bone cyst. The lesions were tested for RANKL expression using a chromogenic RNA in situ hybridization assay. RANKL expression was identified in 24/25 (96%) GCT, 24/26 (92%) chondroblastomas, 6/7 (86%) aneurysmal bone cysts, and 3/16 (19%) patients with fibrous dysplasia. RANKL expression was statistically lower in chondroblastoma and aneurysmal bone cyst compared with GCT. RANKL reactivity in fibrous dysplasia was exclusively seen in the 3 cases with osteoclast-type giant cells. Our results indicate a high proportion of chondroblastomas, GCTs, and aneurysmal bone cysts express RANKL while reactivity in fibrous dysplasia is dependent on the presence of osteoclast-type giant cells. On the basis of the success of denosumab therapy for GCTs, our results indicate that it may be a potential therapeutic option in other primary osseous tumors.

Study Design: Retrospective study. Objective: Multiple studies have shown that osteoporotic patients are at an increased risk for medical and surgical complications, making optimal management of these patients challenging. The purpose of this study was to determine the relationship between patient age and the likelihood of surgical complications, mortality, and 30-day readmission rates following surgery for osteoporotic vertebral compression fractures (OVCFs). Methods: A retrospective analysis of the American College of Surgeons National Surgery Quality Improvement Project (ACS-NSQIP) database from 2007 to 2014 identified 1979 patients who met inclusion criteria. A multivariate logistic regression analysis was conducted to calculate odds ratios (OR), with corresponding P values and 95% confidence intervals, of the relationship between age (treated as a continuous variable) and perioperative mortality, surgical complications, and 30-day readmission rates. Results: Younger patients were statistically more likely to endure a minor (OR = 0.98; P = .002) or major complication (OR = 0.97; P = .009). The older a patient was, on the other hand, the higher the likelihood that patient would be readmitted within 30 days of surgery (OR =1.02; P = .004). Mortality within the 30-day perioperative period was not statistically correlated with age. Conclusions: The impact of age on adverse outcomes following surgery for OVCF is mixed. While younger patients are more likely to endure complications, older patients are more likely to be readmitted within 30 days following surgery. Patient age showed no correlation with mortality rates. In the setting of surgical treatment for an OVCF, a patient’s age can help determine the risk of complications and the rate of readmission following intervention.

IMPORTANCE Preoperative determination of the potential for postoperative opioid dependence in previously naive patients undergoing elective spine surgery may facilitate targeted interventions. OBJECTIVE The purpose of this study was to develop supervised machine learning algorithms for preoperative prediction of prolonged opioid prescription use in opioid-naive patients following lumbar spine surgery. DESIGN Retrospective review of clinical registry data. Variables considered for prediction included demographics, insurance status, preoperative medications, surgical factors, laboratory values, comorbidities, and neighborhood characteristics. Five supervised machine learning algorithms were developed and assessed by discrimination, calibration, Brier score, and decision curve analysis. SETTING One healthcare entity (two academic medical centers, three community hospitals), 2000 to 2018. PARTICIPANTS Opioid-naive patients undergoing decompression and/or fusion for lumbar disk herniation, stenosis, and spondylolisthesis. MAIN OUTCOME Sustained prescription opioid use exceeding 90 days after surgery. RESULTS Overall, of 8,435 patients included, 359 (4.3%) were found to have prolonged postoperative opioid prescriptions. The elastic-net penalized logistic regression achieved the best performance in the independent testing set not used for algorithm development with c-statistic=0.70, calibration intercept=0.06, calibration slope=1.02, and Brier score=0.039. The five most important factors for prolonged opioid prescriptions were use of instrumented spinal fusion, preoperative benzodiazepine use, preoperative antidepressant use, preoperative gabapentin use, and uninsured status. Individual patient-level explanations were provided for the algorithm predictions and the algorithms were incorporated into an open access digital application available here: https://sorg-apps.shinyapps.io/lumbaropioidnaive/ . CONCLUSION AND RELEVANCE The clinician decision aid developed in this study may be helpful to preoperatively risk-stratify opioid-naive patients undergoing lumbar spine surgery. The tool demonstrates moderate discriminative capacity for identifying those at greatest risk of prolonged prescription opioid use. External validation is required to further support the potential utility of this tool in practice.

BACKGROUND CONTEXT There are several prognostic scores available that intend to inform decision-making for patients with spinal metastases. Many of these have not been found to reliably predict survival across the continuum of care. Recently, our group developed the New England Spinal Metastasis Score (NESMS). While the NESMS demonstrated many of the necessary attributes of a useful prediction tool, it has yet to be validated prospectively. PURPOSE To describe the prospective observational study of spinal metastasis treatment (POST). This investigation examined the performance of the NESMS, compared its predictive capacity with other scoring systems and determined its ability to identify patients who benefit the most from surgery. STUDY DESIGN Prospective observational study at two medical centers. PATIENT SAMPLE Patients age 18 and older with spinal metastases involving the spine. OUTCOME MEASURES Survival, post-treatment morbidity and health-related quality of life outcomes. METHODS The POST study assessed patients at baseline and at 1-month, 3-month, 6-month, and 12-month time-points. During the baseline assessment patient demographics, past medical history and assessment of co-morbidities, surgical history, primary tumor histology, and ambulatory status were recorded along with the designated treatment strategy (eg, operative or nonoperative). The NESMS and other predictive scores for each patient were calculated based on baseline data. Study-specific surveys administered at all time-points consisted of the EuroQuol 5-Dimension and Short-Form (SF)-12, Visual Analog Scale (VAS) for pain, and PROMIS assessment of global health. RESULTS Two hundred patients were enrolled in POST from 2017 to 2019. Patients were followed to one of the two predetermined study end-points (ie, mortality, or completion of the 12-month follow-up). Survival was considered the principle dependent variable. Post-treatment morbidity and health-related quality of life outcomes were considered secondarily. Analyses, by aim, relied on Cox proportional hazards regression, repeated measures logistic regression, propensity score matching and multivariable logistic regression. CONCLUSION The POST's findings are anticipated to provide evidence regarding the prognostic capabilities of the NESMS as well as that of other popular grading schemes for survival, post-treatment complications and physical as well as mental function.

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

Artificial Intelligence is gaining traction in medicine for its ease of use and advancements in technology. This study evaluates the current literature on the use of artificial intelligence in adult spinal deformity.

Introduction Maxillofacial (MF) giant cell lesions (GCLs) are benign, often locally aggressive lesions with potential for recurrence. Systemic treatments have included interferon alpha, calcitonin, bisphosphonates, and denosumab. Sclerostin (SOST) is typically thought to be a negative regulator of bone metabolism and anti-SOST agents have been used to treat osteoporosis; however, its role in central giant cell granuloma is unknown. The purpose of this study was to evaluate the expression of SOST in MF GCLs. Materials and methods This was a retrospective study of patients with MF GCLs treated at a single institution between 1993 and 2008 with a minimum follow-up of 6 months. Representative tissue was used to create a tissue microarray and SOST immunohistochemical (IHC) staining and grading was performed. The primary outcomes were IHC staining of the stromal cells and giant cells. The secondary outcomes included correlation of IHC staining and patient predictor variables including clinically benign and aggressive lesions. All analyses were completed using univariate statistical tests. Results A total of 37 subjects were included (29 clinically aggressive and 8 clinically benign). Sclerostin staining was present in 30 of 37 subjects (81%). Of these, 22 (60%) had stromal cell staining and 28 (76%) had giant cell staining. The presence or absence of staining, of either cell type, was not associated with aggressiveness, presence of clinical symptoms, tumor size, previous interferon therapy, previous surgery, or the race or age of the patient. Discussion Maxillofacial GCLs have an overall high level of SOST staining; however, the role of SOST in treatment and prognosis is unknown and warrants further study.

Objectives: Recovery after shoulder arthroscopy can be impaired by sustained postoperative opioid use, yet there are few validated risk calculators for this outcome. The purpose of this study is to develop and validate a machine learning algorithm that can reliably and effectively predict sustained opioid use in patients following elective shoulder arthroscopy. Methods: A retrospective review of an institutional outcomes database was performed at a tertiary academic medical center to identify adult patients who underwent shoulder arthroscopy between January 1, 2014 and October 1, 2019. Extended postoperative opioid consumption was defined as opioid consumption at least 150 days following surgery. Five machine learning algorithms were developed to predict this outcome. Performance of the algorithms were assessed through discrimination, calibration, and decision curve analysis. Results: Overall, of the 1504 patients included, 132 (8.8%) demonstrated sustained postoperative opioid consumption. The factors determined for prediction of prolonged postoperative opioid prescriptions were preoperative opioid use, age, median percentage of population in patient zip code living at the federal poverty line, preoperative pain score, duration of symptoms, BMI. The random forest model achieved the best performance based on discrimination (AUC = 0.78), calibration, and decision curve analysis. This model was integrated into a web-based open-access application able to provide both predictions and explanations. Conclusions: If externally validated in independent populations, the algorithm developed presently could effectively guide preoperative screening in patients at high risk for extended postoperative opioid prescriptions. Early identification and interdisciplinary counseling in high-risk cases can optimize both resource allocation and surgical outcomes.

Study Design: Retrospective study. Objectives: We examined the impact that location of a lumbar disc herniation has on the likelihood that a patient will require surgery after at least 6 weeks of nonoperative management. Methods: Using ICD-10 codes M51.26 and M51.27, we identified patients at a single academic institution from 2015 to 2016 who received a diagnosis of primary lumbar radicular pain, had magnetic resonance imaging confirming a lumbar disc herniation, and underwent at least 6 weeks of nonoperative management. Patients experiencing symptoms suggesting cauda equina syndrome or progressive motor deficits were excluded. Results: Five hundred patients met inclusion/exclusion criteria. Twenty-nine (5.8%) had L3-L4 herniations, 245 (49.0%) had L4-L5 herniations, and 226 (45.2%) had L5-S1 herniations. Overall, 451 (90.2%) patients did not undergo surgery within 1 year of diagnosis. Nonsurgical patients had an average herniation size occupying 31.2% of the canal, compared with 31.5% in patients who underwent surgery. While herniation size, age, sex, and race failed to demonstrate a statistical association with the likelihood for surgery, location of disc herniation demonstrated a strong association. L3-L4 and L4-L5 herniations had odds ratios of 0.19 and 0.45, respectively, relative to L5-S1 herniations ( P = .0047). Patients were more than twice as likely to require a surgery on an L5-S1 herniation in comparison with an L4-L5 herniation ( P < .05). L3-L4 herniations rarely required surgery. Conclusions: Patients with caudal lumbar disc herniations were more likely to require surgery after at least 6 weeks of conservative management than those with disc herniations in the mid-lumbar spine.

Background A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models. Questions/purposes The purposes of this study were (1) to develop machine learning algorithms for 90-day and 1-year survival in patients who received surgical treatment for a bone metastasis of the extremity, and (2) to use these algorithms to identify those clinical factors (demographic, treatment related, or surgical) that are most closely associated with survival after surgery in these patients. Methods All 1090 patients who underwent surgical treatment for a long-bone metastasis at two institutions between 1999 and 2017 were included in this retrospective study. The median age of the patients in the cohort was 63 years (interquartile range [IQR] 54 to 72 years), 56% of patients (610 of 1090) were female, and the median BMI was 27 kg/m (IQR 23 to 30 kg/m). The most affected location was the femur (70%), followed by the humerus (22%). The most common primary tumors were breast (24%) and lung (23%). Intramedullary nailing was the most commonly performed type of surgery (58%), followed by endoprosthetic reconstruction (22%), and plate screw fixation (14%). Missing data were imputed using the missForest methods. Features were selected by random forest algorithms, and five different models were developed on the training set (80% of the data): stochastic gradient boosting, random forest, support vector machine, neural network, and penalized logistic regression. These models were chosen as a result of their classification capability in binary datasets. Model performance was assessed on both the training set and the validation set (20% of the data) by discrimination, calibration, and overall performance. Results We found no differences among the five models for discrimination, with an area under the curve ranging from 0.86 to 0.87. All models were well calibrated, with intercepts ranging from -0.03 to 0.08 and slopes ranging from 1.03 to 1.12. Brier scores ranged from 0.13 to 0.14. The stochastic gradient boosting model was chosen to be deployed as freely available web-based application and explanations on both a global and an individual level were provided. For 90-day survival, the three most important factors associated with poorer survivorship were lower albumin level, higher neutrophil-to-lymphocyte ratio, and rapid growth primary tumor. For 1-year survival, the three most important factors associated with poorer survivorship were lower albumin level, rapid growth primary tumor, and lower hemoglobin level. Conclusions Although the final models must be externally validated, the algorithms showed good performance on internal validation. The final models have been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/extremitymetssurvival/. Pending external validation, clinicians may use this tool to predict survival for their individual patients to help in shared treatment decision making. Level of evidence Level III, therapeutic study.

BACKGROUND Adult spinal deformity (ASD) is a debilitating condition that commonly requires surgical intervention. However, ASD patients may also present with osteoporosis, predisposing them to surgical complications and failure of instrumentation. As a result, proper detection of low bone mineral density (BMD) is critical in order to ensure proper patient care. Typically dual-energy x-ray absorptiometry (DEXA) scans are performed on the hip and spine. Unfortunately, in ASD patients, the latter is often inaccurate PURPOSE In this study, we consider the value of obtaining a forearm DEXA scan in addition to a hip scan in patients suffering from ASD and osteoporosis in order to accurately detect low BMD. STUDY DESIGN Retrospective study. PATIENT SAMPLE Patient data between 2016 and 2018 from a single academic medical center was utilized. Two hundred eighty-six patients met the initial search criteria. OUTCOME MEASURES No outcomes measures related to self-reporting, physiology, or functionality were evaluated in this study. Primary outcome measures analyzed included T-scores across various anatomic locations and diagnoses relating to low bone density (ie, osteopenia and osteoporosis). METHODS This retrospective study examines patients that underwent DEXA studies between 2016 and 2018 and were previously diagnosed with both osteoporosis and adult spinal deformity. For each patient, age, gender, body mass index, and smoking history were noted, as well as whether there was long-term prednisone use. T-scores from both the forearm and hip were recorded and analyzed. Diagnoses from hip DEXA scans were compared with those obtained from forearm scans to identify which region was more sensitive in detecting low BMD. From this data, the frequency of a missed diagnosis, due to reliance on hip or spine T-scores for detection of low BMD, was extrapolated. No external funding source was received in support of this study. RESULTS Two hundred eighty-six patients matched the initial search criteria. Only 68% had one T-score value. However, 24.8% of patients had data for both the hip and forearm, whereas 7.1% had data for the forearm, hip, and spine. Among the 85 patients with more than one anatomical site of study, the forearm was more sensitive than the hip in its ability to detect osteopenia or osteoporosis 41.2% of the time. A two-tailed t test showed no statistically significant difference between hip T-scores and forearm T-scores. However, for more than 17% of patients, the forearm allowed clinicians to detect osteoporosis or osteopenia in a setting where using only the hip data would have missed such a diagnosis. CONCLUSIONS Clinicians need to ensure they survey at least two locations when conducting DEXA studies before precluding a diagnosis of osteopenia or osteoporosis. All ASD patients being evaluated for low bone density should receive DEXA scans that survey at least the hip and the forearm. Misdiagnoses can be costly in the setting of ASD. They occur frequently when only a single hip scan is relied upon to assess BMD.

BACKGROUND CONTEXT Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were previously developed in a single institutional sample but remain to be externally validated. PURPOSE The purpose of this study was to externally validate these algorithms in an independent population from another institution. STUDY DESIGN/SETTING Retrospective study at a large, tertiary care center. PATIENT SAMPLE Patients 18 years or older who underwent surgery between 2003 and 2016. OUTCOME MEASURES Ninety-day and 1-year mortality. METHODS Baseline characteristics of the validation cohort were compared to the developmental cohort for the SORG ML algorithms. Discrimination (c-statistic and receiver operating curve), calibration (calibration slope, intercept, calibration plot, and observed proportions by predicted risk groups), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in the validation cohort. RESULTS Overall, 176 patients underwent surgery for spinal metastatic disease, of which 44 (22.7%) experienced 90-day mortality and 99 (56.2%) experienced 1-year mortality. The validation cohort differed significantly from the developmental cohort on primary tumor histology, metastatic tumor burden, previous systemic therapy, overall comorbidity burden, and preoperative laboratory characteristics. Despite these differences, the SORG ML algorithms generalized well to the validation cohort on discrimination (c-statistic 0.75–0.81 for 90-day mortality and 0.77–0.78 for 1-year mortality), calibration, Brier score, and decision curve analysis. CONCLUSION and RELEVANCE Initial results from external validation of the SORG ML 90-day and 1-year algorithms for survival prediction in spinal metastatic disease suggest potential utility of these digital decision aids in clinical practice. Further studies are needed to validate or refute these algorithms in large patient samples from prospective, international, multi-institutional trials.

BACKGROUND CONTEXT: Laboratory values have been found to be useful predictive measures of survival following surgery. The utility of laboratory values for prognosticating outcomes among patients with spinal metastases has not been studied. PURPOSE: To determine the prognostic capacity of laboratory values at presentation including white blood cell (WBC) count, serum albumin and platelet-lymphocyte ratio (PLR) in patients with spinal metastases. STUDY DESIGN: Retrospective review of records from two tertiary care centers (2005–2017). PATIENT SAMPLE: Patients, aged 40–80, who received operative or non-operative management for spinal metastases. OUTCOME MEASURES: Survival, complications or hospital readmissions within 90 days of treatment and a composite measure for treatment failure accounting for changes in ambulatory function and mortality at 6-months following presentation. METHODS: Multivariable Cox proportional hazard regression analysis was used to analyze the relationship between laboratory values and length of survival, adjusting for confounders. Multivariable logistic regression was used in analyses related to 6-month and 1-year mortality, complications, readmissions and treatment failure. A scoring rubric was developed based on the performance of laboratory values in the multivariable tests. Internal validation was performed using a bootstrap simulation that consisted of sampling with replacement and 1,000 replications. RESULTS: We included 1,216 patients. Thirty-seven percent of patients received a surgical intervention and 63% were treated non-operatively. Median survival for the cohort as a whole was 255 days (inter-quartile range 93–642 days). PLR (HR 1.53; 95% CI 1.29, 1.80; p

Background Context In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning. Purpose The purpose of this study was to develop machine learning algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA. Study Design/Setting Retrospective, case-control study at two academic medical centers and three community hospitals from 1993 to 2016. Patients Sample Adult patients with an inpatient admission for radiologically confirmed diagnosis of SEA. Outcome Measures In-hospital and 90-day postdischarge mortality. Methods Five machine learning algorithms (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed and assessed by discrimination, calibration, overall performance, and decision curve analysis. Results Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count, neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/ . Conclusions Machine learning algorithms show promise on internal validation for prediction of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms in independent populations.

BACKGROUND Local recurrence (LR) of sacral chordoma is a difficult problem and the mortality risk associated with LR remains poorly described. The purpose of this study was to evaluate the risk of mortality in patients with LR and determine if patient age is associated with mortality. METHODS A total of 218 patients (144 male, 69 female; mean age 59 ± 15 years) with sacrococcygeal chordomas were reviewed. Cumulative incidence functions and competing risks for death due to disease and nondisease mortality were employed to analyze mortality trends following LR. RESULTS The 10-year overall survival (OS) was 55%. Patients with LR had 44% 10-year OS, similar to patients without (59%; P = .38). The 10-year OS between those less than 55 compared with ≥55 years were similar (69% vs 48%; P = .52). The 10-year death due to disease was worse in patients with LR compared with those without (44% vs 84%; P

OBJECTIVENonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.METHODSMedical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women’s Hospital (2013–2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.RESULTSOverall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of −0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.CONCLUSIONSThis institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.