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Jeff Ehresman, Zach Pennington, Aditya V. Karhade, Sakibul Huq, Ravi Medikonda, Andrew Schilling, James Feghali, Andrew Hersh, A. Karim Ahmed, Ethan Cottrill, Daniel Lubelski, Erick M. Westbroek, Joseph H. Schwab, and Daniel M. Sciubba

OBJECTIVE

Incidental 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).

METHODS

The 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.

RESULTS

Of 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).

CONCLUSIONS

Using 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.

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Zach Pennington, Jeff Ehresman, Ethan Cottrill, Daniel Lubelski, Kurt Lehner, James Feghali, A. Karim Ahmed, Andrew Schilling, and Daniel M. Sciubba

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.

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Jeff Ehresman, Zach Pennington, James Feghali, Andrew Schilling, Andrew Hersh, Bethany Hung, Daniel Lubelski, and Daniel M. Sciubba

OBJECTIVE

More than 8000 patients are treated annually for vertebral column tumors, of whom roughly two-thirds will be discharged to an inpatient facility (nonroutine discharge). Nonroutine discharge is associated with increased care costs as well as delays in discharge and poorer patient outcomes. In this study, the authors sought to develop a prediction model of nonroutine discharge in the population of vertebral column tumor patients.

METHODS

Patients treated for primary or metastatic vertebral column tumors at a single comprehensive cancer center were identified for inclusion. Data were gathered regarding surgical procedure, patient demographics, insurance status, and medical comorbidities. Frailty was assessed using the modified 5-item Frailty Index (mFI-5) and medical complexity was assessed using the modified Charlson Comorbidity Index (mCCI). Multivariable logistic regression was used to identify independent predictors of nonroutine discharge, and multivariable linear regression was used to identify predictors of prolonged length of stay (LOS). The discharge model was internally validated using 1000 bootstrapped samples.

RESULTS

The authors identified 350 patients (mean age 57.0 ± 13.6 years, 53.1% male, and 67.1% treated for metastatic vs primary disease). Significant predictors of prolonged LOS included higher mCCI score (β = 0.74; p = 0.026), higher serum absolute neutrophil count (β = 0.35; p = 0.001), lower hematocrit (β = −0.34; p = 0.001), use of a staged operation (β = 4.99; p < 0.001), occurrence of postoperative pulmonary embolism (β = 3.93; p = 0.004), and surgical site infection (β = 9.93; p < 0.001). Significant predictors of nonroutine discharge included emergency admission (OR 3.09; p = 0.001), higher mFI-5 score (OR 1.90; p = 0.001), lower serum albumin level (OR 0.43 per g/dL; p < 0.001), and operations with multiple stages (OR 4.10; p < 0.001). The resulting statistical model was deployed as a web-based calculator (https://jhuspine4.shinyapps.io/Nonroutine_Discharge_Tumor/).

CONCLUSIONS

The authors found that nonroutine discharge of patients with surgically treated vertebral column tumors was predicted by emergency admission, increased frailty, lower serum albumin level, and staged surgical procedures. The resulting web-based calculator tool may be useful clinically to aid in discharge planning for spinal oncology patients by preoperatively identifying patients likely to require placement in an inpatient facility postoperatively.

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James Feghali, Zach Pennington, Jeff Ehresman, Daniel Lubelski, Ethan Cottrill, A. Karim Ahmed, Andrew Schilling, and Daniel M. Sciubba

Symptomatic spinal metastasis occurs in around 10% of all cancer patients, 5%–10% of whom will require operative management. While postoperative survival has been extensively evaluated, postoperative health-related quality-of-life (HRQOL) outcomes have remained relatively understudied. Available tools that measure HRQOL are heterogeneous and may emphasize different aspects of HRQOL. The authors of this paper recommend the use of the EQ-5D and Spine Oncology Study Group Outcomes Questionnaire (SOSGOQ), given their extensive validation, to capture the QOL effects of systemic disease and spine metastases. Recent studies have identified preoperative QOL, baseline functional status, and neurological function as potential predictors of postoperative QOL outcomes, but heterogeneity across studies limits the ability to derive meaningful conclusions from the data. Future development of a valid and reliable prognostic model will likely require the application of a standardized protocol in the context of a multicenter study design.

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Daniel Lubelski, James Feghali, Amy S. Nowacki, Vincent J. Alentado, Ryan Planchard, Kalil G. Abdullah, Daniel M. Sciubba, Michael P. Steinmetz, Edward C. Benzel, and Thomas E. Mroz

OBJECTIVE

Patient demographics, comorbidities, and baseline quality of life (QOL) are major contributors to postoperative outcomes. The frequency and cost of lumbar spine surgery has been increasing, with controversy revolving around optimal management strategies and outcome predictors. The goal of this study was to generate predictive nomograms and a clinical calculator for postoperative clinical and QOL outcomes following lumbar spine surgery for degenerative disease.

METHODS

Patients undergoing lumbar spine surgery for degenerative disease at a single tertiary care institution between June 2009 and December 2012 were retrospectively reviewed. Nomograms and an online calculator were modeled based on patient demographics, comorbidities, presenting symptoms and duration of symptoms, indication for surgery, type and levels of surgery, and baseline preoperative QOL scores. Outcomes included postoperative emergency department (ED) visit or readmission within 30 days, reoperation within 90 days, and 1-year changes in the EuroQOL-5D (EQ-5D) score. Bootstrapping was used for internal validation.

RESULTS

A total of 2996 lumbar surgeries were identified. Thirty-day ED visits were seen in 7%, 30-day readmission in 12%, 90-day reoperation in 3%, and improvement in EQ-5D at 1 year that exceeded the minimum clinically important difference in 56%. Concordance indices for the models predicting ED visits, readmission, reoperation, and dichotomous 1-year improvement in EQ-5D were 0.63, 0.66, 0.73, and 0.84, respectively. Important predictors of clinical outcomes included age, body mass index, Charlson Comorbidity Index, indication for surgery, preoperative duration of symptoms, and the type (and number of levels) of surgery. A web-based calculator was created, which can be accessed here: https://riskcalc.org/PatientsEligibleForLumbarSpineSurgery/.

CONCLUSIONS

The prediction tools derived from this study constitute important adjuncts to clinical decision-making that can offer patients undergoing lumbar spine surgery realistic and personalized expectations of postoperative outcome. They may also aid physicians in surgical planning, referrals, and counseling to ultimately lead to improved patient experience and outcomes.

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Aymeric Amelot, Louis-Marie Terrier, Ann-Rose Cook, Pierre-Yves Borius, and Bertrand Mathon

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Jeff Ehresman, Daniel Lubelski, Zach Pennington, Bethany Hung, A. Karim Ahmed, Tej D. Azad, Kurt Lehner, James Feghali, Zorica Buser, James Harrop, Jefferson Wilson, Shekar Kurpad, Zoher Ghogawala, and Daniel M. Sciubba

OBJECTIVE

The objective of this study was to evaluate the characteristics and performance of current prediction models in the fields of spine metastasis and degenerative spine disease to create a scoring system that allows direct comparison of the prediction models.

METHODS

A systematic search of PubMed and Embase was performed to identify relevant studies that included either the proposal of a prediction model or an external validation of a previously proposed prediction model with 1-year outcomes. Characteristics of the original study and discriminative performance of external validations were then assigned points based on thresholds from the overall cohort.

RESULTS

Nine prediction models were included in the spine metastasis category, while 6 prediction models were included in the degenerative spine category. After assigning the proposed utility of prediction model score to the spine metastasis prediction models, only 1 reached the grade of excellent, while 2 were graded as good, 3 as fair, and 3 as poor. Of the 6 included degenerative spine models, 1 reached the excellent grade, while 3 studies were graded as good, 1 as fair, and 1 as poor.

CONCLUSIONS

As interest in utilizing predictive analytics in spine surgery increases, there is a concomitant increase in the number of published prediction models that differ in methodology and performance. Prior to applying these models to patient care, these models must be evaluated. To begin addressing this issue, the authors proposed a grading system that compares these models based on various metrics related to their original design as well as internal and external validation. Ultimately, this may hopefully aid clinicians in determining the relative validity and usability of a given model.

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Wuyang Yang, Jordina Rincon-Torroella, James Feghali, Adham M. Khalafallah, Wataru Ishida, Alexander Perdomo-Pantoja, Alfredo Quiñones-Hinojosa, Michael Lim, Gary L. Gallia, Gregory J. Riggins, William S. Anderson, Sheng-Fu Larry Lo, Daniele Rigamonti, Rafael J. Tamargo, Timothy F. Witham, Ali Bydon, Alan R. Cohen, George I. Jallo, Alban Latremoliere, Mark G. Luciano, Debraj Mukherjee, Alessandro Olivi, Lintao Qu, Ziya L. Gokaslan, Daniel M. Sciubba, Betty Tyler, Henry Brem, and Judy Huang

OBJECTIVE

International research fellows have been historically involved in academic neurosurgery in the United States (US). To date, the contribution of international research fellows has been underreported. Herein, the authors aimed to quantify the academic output of international research fellows in the Department of Neurosurgery at The Johns Hopkins University School of Medicine.

METHODS

Research fellows with Doctor of Medicine (MD), Doctor of Philosophy (PhD), or MD/PhD degrees from a non-US institution who worked in the Hopkins Department of Neurosurgery for at least 6 months over the past decade (2010–2020) were included in this study. Publications produced during fellowship, number of citations, and journal impact factors (IFs) were analyzed using ANOVA. A survey was sent to collect information on personal background, demographics, and academic activities.

RESULTS

Sixty-four international research fellows were included, with 42 (65.6%) having MD degrees, 17 (26.6%) having PhD degrees, and 5 (7.8%) having MD/PhD degrees. During an average 27.9 months of fellowship, 460 publications were produced in 136 unique journals, with 8628 citations and a cumulative journal IF of 1665.73. There was no significant difference in total number of publications, first-author publications, and total citations per person among the different degree holders. Persons holding MD/PhDs had a higher number of citations per publication per person (p = 0.027), whereas those with MDs had higher total IFs per person (p = 0.048). Among the 43 (67.2%) survey responders, 34 (79.1%) had nonimmigrant visas at the start of the fellowship, 16 (37.2%) were self-paid or funded by their country of origin, and 35 (81.4%) had mentored at least one US medical student, nonmedical graduate student, or undergraduate student.

CONCLUSIONS

International research fellows at the authors’ institution have contributed significantly to academic neurosurgery. Although they have faced major challenges like maintaining nonimmigrant visas, negotiating cultural/language differences, and managing self-sustainability, their scientific productivity has been substantial. Additionally, the majority of fellows have provided reciprocal mentorship to US students.

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Zach Pennington, Jeff Ehresman, Andrew Schilling, James Feghali, Andrew M. Hersh, Bethany Hung, Eleni N. Kalivas, Daniel Lubelski, and Daniel M. Sciubba

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.