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OBJECTIVEVertebral compression fractures (VCFs) in patients with spinal metastasis can lead to destabilization and often carry a high risk profile. It is therefore important to have tools that enable providers to predict the occurrence of new VCFs. The most widely used tool for bone quality assessment, dual-energy x-ray absorptiometry (DXA), is not often available at a patient’s initial presentation and has limited sensitivity. While the Spinal Instability Neoplastic Score (SINS) has been associated with VCFs, it does not take patients’ baseline bone quality into consideration. To address this, the authors sought to develop an MRI-based scoring system to estimate trabecular vertebral bone quality (VBQ) and to assess this system’s ability to predict the occurrence of new VCFs in patients with spinal metastasis.METHODSCases of adult patients with a diagnosis of spinal metastasis, who had undergone stereotactic body radiation therapy (SBRT) to the spine or neurosurgical intervention at a single institution between 2012 and 2019, were retrospectively reviewed. The novel VBQ score was calculated for each patient by dividing the median signal intensity of the L1–4 vertebral bodies by the signal intensity of cerebrospinal fluid (CSF). Multivariable logistic regression analysis was used to identify associations of demographic, clinical, and radiological data with new VCFs.RESULTSAmong the 105 patients included in this study, 56 patients received a diagnosis of a new VCF and 49 did not. On univariable analysis, the factors associated with new VCFs were smoking status, steroid use longer than 3 months, the SINS, and the novel scoring system—the VBQ score. On multivariable analysis, only the SINS and VBQ score were significant predictors of new VCFs and, when combined, had a predictive accuracy of 89%.CONCLUSIONSAs a measure of bone quality, the novel VBQ score significantly predicted the occurrence of new VCFs in patients with spinal metastases independent of the SINS. This suggests that baseline bone quality is a crucial factor that requires assessment when evaluating these patients’ conditions and that the VBQ score is a novel and simple MRI-based measure to accomplish this.

OBJECTIVEIncidental durotomy is a common complication of elective lumbar spine surgery seen in up to 11% of cases. Prior studies have suggested patient age and body habitus along with a history of prior surgery as being associated with an increased risk of dural tear. To date, no calculator has been developed for quantifying risk. Here, the authors’ aim was to identify independent predictors of incidental durotomy, present a novel predictive calculator, and externally validate a novel method to identify incidental durotomies using natural language processing (NLP).METHODSThe authors retrospectively reviewed all patients who underwent elective lumbar spine procedures at a tertiary academic hospital for degenerative pathologies between July 2016 and November 2018. Data were collected regarding surgical details, patient demographic information, and patient medical comorbidities. The primary outcome was incidental durotomy, which was identified both through manual extraction and the NLP algorithm. Multivariable logistic regression was used to identify independent predictors of incidental durotomy. Bootstrapping was then employed to estimate optimism in the model, which was corrected for; this model was converted to a calculator and deployed online.RESULTSOf the 1279 elective lumbar surgery patients included in this study, incidental durotomy occurred in 108 (8.4%). Risk factors for incidental durotomy on multivariable logistic regression were increased surgical duration, older age, revision versus index surgery, and case starts after 4 pm. This model had an area under curve (AUC) of 0.73 in predicting incidental durotomies. The previously established NLP method was used to identify cases of incidental durotomy, of which it demonstrated excellent discrimination (AUC 0.97).CONCLUSIONSUsing multivariable analysis, the authors found that increased surgical duration, older patient age, cases started after 4 pm, and a history of prior spine surgery are all independent positive predictors of incidental durotomy in patients undergoing elective lumbar surgery. Additionally, the authors put forth the first version of a clinical calculator for durotomy risk that could be used prospectively by spine surgeons when counseling patients about their surgical risk. Lastly, the authors presented an external validation of an NLP algorithm used to identify incidental durotomies through the review of free-text operative notes. The authors believe that these tools can aid clinicians and researchers in their efforts to prevent this costly complication in spine surgery.

Accurate prediction of patient survival is an essential component of the preoperative evaluation of patients with spinal metastases. Over the past quarter of a century, a number of predictors have been developed, although none have been accurate enough to be instituted as a staple of clinical practice. However, recently more comprehensive survival calculators have been published that make use of larger data sets and machine learning to predict postoperative survival among patients with spine metastases. Given the glut of calculators that have been published, the authors sought to perform a narrative review of the current literature, highlighting existing calculators along with the strengths and weaknesses of each. In doing so, they identify two “generations” of scoring systems—a first generation based on a priori factor weighting and a second generation comprising predictive tools that are developed using advanced statistical modeling and are focused on clinical deployment. In spite of recent advances, the authors found that most predictors have only a moderate ability to explain variation in patient survival. Second-generation models have a greater prognostic accuracy relative to first-generation scoring systems, but most still require external validation. Given this, it seems that there are two outstanding goals for these survival predictors, foremost being external validation of current calculators in multicenter prospective cohorts, as the majority have been developed from, and internally validated within, the same single-institution data sets. Lastly, current predictors should be modified to incorporate advances in targeted systemic therapy and radiotherapy, which have been heretofore largely ignored.

OBJECTIVE Patients with spine tumors are at increased risk for both hemorrhage and venous thromboembolism (VTE). Tranexamic acid (TXA) has been advanced as a potential intervention to reduce intraoperative blood loss in this surgical population, but many fear it is associated with increased VTE risk due to the hypercoagulability noted in malignancy. In this study, the authors aimed to 1) develop a clinical calculator for postoperative VTE risk in the population with spine tumors, and 2) investigate the association of intraoperative TXA use and postoperative VTE. METHODS A retrospective data set from a comprehensive cancer center was reviewed for adult patients treated for vertebral column tumors. Data were collected on surgery performed, patient demographics and medical comorbidities, VTE prophylaxis measures, and TXA use. TXA use was classified as high-dose (≥ 20 mg/kg) or low-dose (< 20 mg/kg). The primary study outcome was VTE occurrence prior to discharge. Secondary outcomes were deep venous thrombosis (DVT) or pulmonary embolism (PE). Multivariable logistic regression was used to identify independent risk factors for VTE and the resultant model was deployed as a web-based calculator. RESULTS Three hundred fifty patients were included. The mean patient age was 57 years, 53% of patients were male, and 67% of surgeries were performed for spinal metastases. TXA use was not associated with increased VTE (14.3% vs 10.1%, p = 0.37). After multivariable analysis, VTE was independently predicted by lower serum albumin (odds ratio [OR] 0.42 per g/dl, 95% confidence interval [CI] 0.23–0.79, p = 0.007), larger mean corpuscular volume (OR 0.91 per fl, 95% CI 0.84–0.99, p = 0.035), and history of prior VTE (OR 2.60, 95% CI 1.53–4.40, p < 0.001). Longer surgery duration approached significance and was included in the final model. Although TXA was not independently associated with the primary outcome of VTE, high-dose TXA use was associated with increased odds of both DVT and PE. The VTE model showed a fair fit of the data with an area under the curve of 0.77. CONCLUSIONS In the present cohort of patients treated for vertebral column tumors, TXA was not associated with increased VTE risk, although high-dose TXA (≥ 20 mg/kg) was associated with increased odds of DVT or PE. Additionally, the web-based clinical calculator of VTE risk presented here may prove useful in counseling patients preoperatively about their individualized VTE risk.