Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases.

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98-0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98-0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients.

Can We Geographically Validate a Natural Language Processing Algorithm for Automated Detection of Incidental Durotomy Across Three Independent Cohorts From Two Continents?

BACKGROUND: Incidental durotomy is an intraoperative complication in spine surgery that can lead to postoperative complications, increased length of stay, and higher healthcare costs. Natural language processing (NLP) is an artificial intelligence method that assists in understanding free-text notes that may be useful in the automated surveillance of adverse events in orthopaedic surgery. A previously developed NLP algorithm is highly accurate in the detection of incidental durotomy on internal validation and external validation in an independent cohort from the same country. External validation in a cohort with linguistic differences is required to assess the transportability of the developed algorithm, referred to geographical validation. Ideally, the performance of a prediction model, the NLP algorithm, is constant across geographic regions to ensure reproducibility and model validity. QUESTION/PURPOSE: Can we geographically validate an NLP algorithm for the automated detection of incidental durotomy across three independent cohorts from two continents? METHODS: Patients 18 years or older undergoing a primary procedure of (thoraco)lumbar spine surgery were included. In Massachusetts, between January 2000 and June 2018, 1000 patients were included from two academic and three community medical centers. In Maryland, between July 2016 and November 2018, 1279 patients were included from one academic center, and in Australia, between January 2010 and December 2019, 944 patients were included from one academic center. The authors retrospectively studied the free-text operative notes of included patients for the primary outcome that was defined as intraoperative durotomy. Incidental durotomy occurred in 9% (93 of 1000), 8% (108 of 1279), and 6% (58 of 944) of the patients, respectively, in the Massachusetts, Maryland, and Australia cohorts. No missing reports were observed. Three datasets (Massachusetts, Australian, and combined Massachusetts and Australian) were divided into training and holdout test sets in an 80:20 ratio. An extreme gradient boosting (an efficient and flexible tree-based algorithm) NLP algorithm was individually trained on each training set, and the performance of the three NLP algorithms (respectively American, Australian, and combined) was assessed by discrimination via area under the receiver operating characteristic curves (AUC-ROC; this measures the model's ability to distinguish patients who obtained the outcomes from those who did not), calibration metrics (which plot the predicted and the observed probabilities) and Brier score (a composite of discrimination and calibration). In addition, the sensitivity (true positives, recall), specificity (true negatives), positive predictive value (also known as precision), negative predictive value, F1-score (composite of precision and recall), positive likelihood ratio, and negative likelihood ratio were calculated. RESULTS: The combined NLP algorithm (the combined Massachusetts and Australian data) achieved excellent performance on independent testing data from Australia (AUC-ROC 0.97 [95% confidence interval 0.87 to 0.99]), Massachusetts (AUC-ROC 0.99 [95% CI 0.80 to 0.99]) and Maryland (AUC-ROC 0.95 [95% CI 0.93 to 0.97]). The NLP developed based on the Massachusetts cohort had excellent performance in the Maryland cohort (AUC-ROC 0.97 [95% CI 0.95 to 0.99]) but worse performance in the Australian cohort (AUC-ROC 0.74 [95% CI 0.70 to 0.77]). CONCLUSION: We demonstrated the clinical utility and reproducibility of an NLP algorithm with combined datasets retaining excellent performance in individual countries relative to algorithms developed in the same country alone for detection of incidental durotomy. Further multi-institutional, international collaborations can facilitate the creation of universal NLP algorithms that improve the quality and safety of orthopaedic surgery globally. The combined NLP algorithm has been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/nlp_incidental_durotomy/ . Clinicians and researchers can use the tool to help incorporate the model in evaluating spine registries or quality and safety departments to automate detection of incidental durotomy and optimize prevention efforts. LEVEL OF EVIDENCE: Level III, diagnostic study.

Body Composition Predictors of Adverse Postoperative Events in Patients Undergoing Surgery for Long Bone Metastases.

INTRODUCTION: Body composition assessed using opportunistic CT has been recently identified as a predictor of outcome in patients with cancer. The purpose of this study was to determine whether the cross-sectional area (CSA) and the attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), and paraspinous and abdominal muscles are the predictors of length of hospital stay, 30-day postoperative complications, and revision surgery in patients treated for long bone metastases. METHODS: A retrospective database of patients who underwent surgery for long bone metastases from 1999 to 2017 was used to identify 212 patients who underwent preoperative abdominal CT. CSA and attenuation measurements for subcutaneous adipose tissue, VAT, and muscles were taken at the level of L4 with the aid of an in-house segmentation algorithm. Bivariate and multivariate linear and logistic regression models were created to determine associations between body composition measurements and outcomes while controlling for confounders, including primary tumor, metastasis location, and preoperative albumin. RESULTS: On multivariate analysis, increased VAT CSA {regression coefficient (r) (95% confidence interval [CI]); 0.01 (0.01 to 0.02); P < 0.01} and decreased muscle attenuation (r [95% CI] -0.07 [-0.14 to -0.01]; P = 0.04) were associated with an increased length of hospital stay. In bivariate analysis, increased muscle CSA was associated with increased chance of revision surgery (odds ratio [95% CI]; 1.02 [1.01 to 1.03]; P = 0.04). No body composition measurements were associated with postoperative complications within 30 days. DISCUSSION: Body composition measurements assessed using opportunistic CT predict adverse postoperative outcomes in patients operated for long bone metastases.

Preoperative embolization in surgical treatment of spinal metastases originating from non-hypervascular primary tumors: a propensity score matched study using 495 patients.

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

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

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

Body composition predictors of mortality on computed tomography in patients with spinal metastases undergoing surgical treatment.

BACKGROUND CONTEXT: Although survival of patients with spinal metastases has improved over the last decades due to advances in multi-modal therapy, there are currently no reliable predictors of mortality. Body composition measurements obtained using computed tomography (CT) have been recently proposed as biomarkers for survival in patients with and without cancer. Patients with cancer routinely undergo CT for staging or surveillance of therapy. Body composition assessed using opportunistic CTs might be used to determine survival in patients with spinal metastases. PURPOSE: The purpose of this study was to determine the value of body composition measures obtained on opportunistic abdomen CTs to predict 90-day and 1-year mortality in patients with spinal metastases undergoing surgery. We hypothesized that low muscle and abdominal fat mass were positive predictors of mortality. STUDY DESIGN: Retrospective study at a single tertiary care center in the United States. PATIENT SAMPLE: This retrospective study included 196 patients between 2001 and 2016 that were 18 years of age or older, underwent surgical treatment for spinal metastases, and had a preoperative CT of the abdomen within three months prior to surgery. OUTCOME MEASURES: Ninety-day and 1-year mortality by any cause. METHODS: Quantification of cross-sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and paraspinous and abdominal skeletal muscle were performed on CT images at the level of L4 using an in-house automated algorithm. Sarcopenia was determined by total muscle CSA (cm2) divided by height squared (m2) with cutoff values of <52.4 cm2/m2 for men and <38.5 cm2/m2 for women. Bivariate and multivariate Cox proportional-hazard analyses were used to determine the associations between body compositions and 90-day and 1-year mortality. RESULTS: The median age was 62 years (interquartile range=53-70). The mortality rate for 90-day was 24% and 1-year 54%. The presence of sarcopenia was associated with an increased 1-year mortality rate of 66% compared with a 1-year mortality rate of 41% in patients without sarcopenia (hazard ratio, 1.68; 95% confidence interval, 1.08-2.61; p=.02) after adjusting for various clinical factors including primary tumor type, ECOG performance status, additional metastases, neurology status, and systemic therapy. Additional analysis showed an association between sarcopenia and increased 1-year mortality when controlling for the prognostic modified Bauer score (HR, 1.58; 95%CI, 1.04-2.40; p=.03). Abdominal fat CSAs or muscle attenuation were not independently associated with mortality. CONCLUSIONS: The presence of sarcopenia is associated with an increased risk of 1-year mortality for patients surgically treated for spinal metastases. Sarcopenia retained an independent association with mortality when controlling for the prognostic modified Bauer score. This implies that body composition measurements such as sarcopenia could serve as novel biomarkers for prediction of mortality and may supplement other existing prognostic tools to improve shared decision making for patients with spinal metastases that are contemplating surgical treatment.

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

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

Quality of Life Changes After Surgery for Metastatic Spinal Disease: A Systematic Review and Meta-analysis.

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<0.001), to 3 months (SMD=1.28, P<0.001), to 6 months (SMD=1.21, P<0.001), and to 9-12 months (SMD=1.08, P=0.001). The surgery improved physical well-being during the first 3 months (SMD=0.94, P=0.022), improved emotional (SMD=1.19, P=0.004), and functional well-being (SMD=1.08, P=0.005) during the first 6 months, and only improved social/family well-being at month 6 (SMD=0.28, P=0.001). CONCLUSIONS: The surgery improved QoL for patients with spinal metastases, and rapidly improved physical, emotional, and functional well-being; it had minimal effect on social/family well-being. However, choosing the optimal candidate for surgical intervention in the setting of spinal metastases remains paramount: otherwise postoperative morbidity and complications may outbalance the intended benefits of surgery. Future research should report clear definitions of selection criteria and surgical indication and provide stratified QoL results by indication and clinical characteristics such as primary tumor type, preoperative Karnofsky, and Bilsky scores to elucidate the optimal candidate for surgical intervention.

Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.

Clinical Outcome Differences in the Treatment of Impending Versus Completed Pathological Long-Bone Fractures.

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.

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.

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.

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.

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.

International external validation of the SORG machine learning algorithms for predicting 90-day and one-year survival of patients with spine metastases using a Taiwanese cohort.

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.

Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery.

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.

Do Cohabitants Reliably Complete Questionnaires for Patients in a Terminal Cancer Stage when Assessing Quality of Life, Pain, Depression, and Anxiety?

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.

Postoperative adverse events secondary to iatrogenic vascular injury during anterior lumbar spinal surgery.

BACKGROUND: Anterior lumbar spine surgery (ALSS) requires mobilization of the great vessels, resulting in a high risk of iatrogenic vascular injury (VI). It remains unclear whether VI is associated with increased risk of postoperative complications and other related adverse outcomes. PURPOSE: The purpose of this study was to (1) assess the incidence of postoperative complications attributable to VI during ALSS, and (2) outcomes secondary to VI such as procedural blood loss, transfusion of blood products, length of stay (LOS), and in hospital mortality. STUDY DESIGN: Retrospective propensity-score matched, case-control study at 2 academic and 3 community medical centers, PATIENT SAMPLE: Patients 18 years of age or older, undergoing ALSS between January 1st, 2000 and July 31st, 2019 were included in this analysis. OUTCOME MEASURES: The primary outcome was the incidence of postoperative complications attributable to VI, such as venous thromboembolism, compartment syndrome, transfusion reaction, limb ischemia, and reoperations. The secondary outcomes included estimated operative blood loss (milliliter), transfused blood products, LOS (days), and in-hospital mortality. METHODS: In total, 1,035 patients were identified, of which 75 (7.2%) had a VI. For comparative analyses, the 75 VI patients were paired with 75 comparable non-VI patients by propensity-score matching. The adequacy of the matching was assessed by testing the standardized mean differences (SMD) between VI and non-VI group (>0.25 SMD). RESULTS: Two patients (2.7%) had VI-related postoperative complications in the studied period, which consisted of two deep venous thromboembolisms (DVTs) occurring on day 3 and 7 postoperatively. Both DVTs were located in the distal left common iliac vein (CIV). The VI these patients suffered were to the distal inferior vena cava and the left CIV, respectively. Both patients did not develop additional complications in consequence of their DVTs, however, did require systemic anticoagulation and placement of an inferior vena cava filter. There was no statistical difference with the non-VI group where no instances (0%) of postoperative complications were reported (p=.157). No differences were found in LOS or in hospital mortality between the two groups (p=.157 and p=.999, respectively). Intraoperative blood loss and blood transfusion were both found to be higher in the VI group in comparison to the non-VI group (650 mL, interquartile range [IQR] 300-1400 vs. 150 mL, IQR 50-425, p≤.001; 0 units, IQR 0-3 vs. 0 units, IQR 0-1, p=.012, respectively). CONCLUSION: This study found a low number of serious postoperative complications related to VI in ALSS. In addition, these complications were not significantly different between the VI and matched non-VI ALSS cohort. Although not significant, the found DVT incidence of 2.7% after VI in ALSS warrants vigilance and preventive measures during the postoperative course of these patients.

The use of autologous free vascularized fibula grafts in reconstruction of the mobile spine following tumor resection: surgical technique and outcomes.

OBJECTIVE: Reconstruction 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. METHODS: Thirty-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). RESULTS: The 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. CONCLUSIONS: The 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.

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports.

BACKGROUND: The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. However, processing this rich resource of data for clinical and research purposes, depends on labor-intensive and potentially error-prone manual review. The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases. MATERIAL AND METHODS: Bone scintigraphy reports of patients undergoing surgery for bone metastases were labeled each by three independent reviewers using a binary classification (single metastasis versus two or more metastases) to establish a ground truth. A stratified 80:20 split was used to develop and test an extreme-gradient boosting supervised machine learning NLP algorithm. RESULTS: A total of 704 free-text bone scintigraphy reports from 704 patients were included in this study and 617 (88%) had multiple bone metastases. In the independent test set (n = 141) not used for model development, the NLP algorithm achieved an 0.97 AUC-ROC (95% confidence interval [CI], 0.92-0.99) for classification of multiple bone metastases and an 0.99 AUC-PRC (95% CI, 0.99-0.99). At a threshold of 0.90, NLP algorithm correctly identified multiple bone metastases in 117 of the 124 who had multiple bone metastases in the testing cohort (sensitivity 0.94) and yielded 3 false positives (specificity 0.82). At the same threshold, the NLP algorithm had a positive predictive value of 0.97 and F1-score of 0.96. CONCLUSIONS: NLP has the potential to automate clinical data extraction from free text radiology notes in orthopedics, thereby optimizing the speed, accuracy, and consistency of clinical chart review. Pending external validation, the NLP algorithm developed in this study may be implemented as a means to aid researchers in tackling large amounts of data.

Free Vascularized Fibula Graft with Femoral Allograft Sleeve for Lumbar Spine Defects After Spondylectomy of Malignant Tumors: A Case Report.

CASE: We present a 65-year-old man with an L4 conventional chordoma. Total en bloc spondylectomy (TES) of the involved vertebral bodies and surrounding soft tissues with reconstruction of the spine using a free vascularized fibula autograft (FVFG) is a proven technique, limiting complications and recurrence. However, graft fracture has occurred only in the lumbar spine in our institutional cases. We used a technique in our patient to ensure extra stability and support, with the addition of a femoral allograft sleeve encasing the FVFG. CONCLUSIONS: Our technique for the reconstruction of the lumbar spine after TES of primary malignant spinal disease using a femoral allograft sleeve encasing the FVFG is viable to consider.