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.

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.

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.

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.

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.

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.