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Camilo A. Molina, Christopher F. Dibble, Sheng-fu Larry Lo, Timothy Witham, and Daniel M. Sciubba

En bloc spinal tumor resections are technically demanding procedures with high morbidity because of the conventionally large exposure area and aggressive resection goals. Stereotactic surgical navigation presents an opportunity to perform the smallest possible resection plan while still achieving an en bloc resection. Augmented reality (AR)–mediated spine surgery (ARMSS) via a mounted display with an integrated tracking camera is a novel FDA-approved technology for intraoperative “heads up” neuronavigation, with the proposed advantages of increased precision, workflow efficiency, and cost-effectiveness. As surgical experience and capability with this technology grow, the potential for more technically demanding surgical applications arises. Here, the authors describe the use of ARMSS for guidance in a unique osteotomy execution to achieve an en bloc wide marginal resection of an L1 chordoma through a posterior-only approach while avoiding a tumor capsule breach. A technique is described to simultaneously visualize the navigational guidance provided by the contralateral surgeon’s tracked pointer and the progress of the BoneScalpel aligned in parallel with the tracked instrument, providing maximum precision and safety. The procedure was completed by reconstruction performed with a quad-rod and cabled fibular strut allograft construct, and the patient did well postoperatively. Finally, the authors review the technical aspects of the approach, as well as the applications and limitations of this new technology.

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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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Alvaro Ibaseta, Rafa Rahman, Nicholas S. Andrade, Richard L. Skolasky, Khaled M. Kebaish, Daniel M. Sciubba, and Brian J. Neuman

OBJECTIVE

The aim of this study was to determine the concurrent validity, discriminant ability, and responsiveness of the Patient-Reported Outcomes Measurement Information System (PROMIS) in adult spinal deformity (ASD) and to calculate minimal clinically important differences (MCIDs) for PROMIS scores.

METHODS

The authors used data obtained in 186 surgical patients with ASD. Concurrent validity was determined through correlations between preoperative PROMIS scores and legacy measure scores. PROMIS discriminant ability between disease severity groups was determined using the preoperative Oswestry Disability Index (ODI) value as the anchor. Responsiveness was determined through distribution- and anchor-based methods, using preoperative to postoperative changes in PROMIS scores. MCIDs were estimated on the basis of the responsiveness analysis.

RESULTS

The authors found strong correlations between PROMIS Pain Interference and ODI and the Scoliosis Research Society 22-item questionnaire Pain component; PROMIS Physical Function and ODI; PROMIS Anxiety and Depression domains and the 12-Item Short Form Health Survey version 2, Physical and Mental Components, Scoliosis Research Society 22-item questionnaire Mental Health component (anxiety only), 9-Item Patient Health Questionnaire (anxiety only), and 7-Item Generalized Anxiety Disorder questionnaire; PROMIS Fatigue and 9-Item Patient Health Questionnaire; and PROMIS Satisfaction with Participation in Social Roles (i.e., Social Satisfaction) and ODI. PROMIS discriminated between disease severity groups in all domains except between none/mild and moderate Anxiety, with mean differences ranging from 3.7 to 8.4 points. PROMIS showed strong responsiveness in Pain Interference; moderate responsiveness in Physical Function and Social Satisfaction; and low responsiveness in Anxiety, Depression, Fatigue, and Sleep Disturbance. Final PROMIS MCIDs were as follows: –6.3 for Anxiety, –4.4 for Depression, –4.6 for Fatigue, –5.0 for Pain Interference, 4.2 for Physical Function, 5.7 for Social Satisfaction, and –3.5 for Sleep Disturbance.

CONCLUSIONS

PROMIS is a valid assessment of patient health, can discriminate between disease severity levels, and shows responsiveness to changes after ASD surgery. The MCIDs provided herein may help clinicians interpret postoperative changes in PROMIS scores, taking into account the fact that they are pending external validation.

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

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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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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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Zach Pennington, Ethan Cottrill, Daniel Lubelski, Jeff Ehresman, Kurt Lehner, Mari L. Groves, Paul Sponseller, and Daniel M. Sciubba

OBJECTIVES

More than 7500 children undergo surgery for scoliosis each year, at an estimated annual cost to the health system of $1.1 billion. There is significant interest among patients, parents, providers, and payors in identifying methods for delivering quality outcomes at lower costs. Enhanced recovery after surgery (ERAS) protocols have been suggested as one possible solution. Here the authors conducted a systematic review of the literature describing the clinical and economic benefits of ERAS protocols in pediatric spinal deformity surgery.

METHODS

The authors identified all English-language articles on ERAS protocol use in pediatric spinal deformity surgery by using the following databases: PubMed/MEDLINE, Web of Science, Cochrane Reviews, EMBASE, CINAHL, and OVID MEDLINE. Quantitative analyses of comparative articles using random effects were performed for the following clinical outcomes: 1) length of stay (LOS); 2) complication rate; 3) wound infection rate; 4) 30-day readmission rate; 5) reoperation rate; and 6) postoperative pain scores.

RESULTS

Of 950 articles reviewed, 7 were included in the qualitative analysis and 6 were included in the quantitative analysis. The most frequently cited benefits of ERAS protocols were shorter LOS, earlier urinary catheter removal, and earlier discontinuation of patient-controlled analgesia pumps. Quantitative analyses showed ERAS protocols to be associated with shorter LOS (mean difference −1.12 days; 95% CI −1.51, −0.74; p < 0.001), fewer postoperative complications (OR 0.37; 95% CI 0.20, 0.68; p = 0.001), and lower pain scores on postoperative day (POD) 0 (mean −0.92; 95% CI −1.29, −0.56; p < 0.001) and POD 2 (−0.61; 95% CI −0.75, −0.47; p < 0.001). There were no differences in reoperation rate or POD 1 pain scores. ERAS-treated patients had a trend toward higher 30-day readmission rates and earlier discontinuation of patient-controlled analgesia (both p = 0.06). Insufficient data existed to reach a conclusion about cost differences.

CONCLUSIONS

The results of this systematic review suggest that ERAS protocols may shorten hospitalizations, reduce postoperative complication rates, and reduce postoperative pain scores in children undergoing scoliosis surgery. Publication biases exist, and therefore larger, prospective, multicenter data are needed to validate these results.

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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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Zach Pennington, Ethan Cottrill, Daniel Lubelski, Jeff Ehresman, Nicholas Theodore, and Daniel M. Sciubba

OBJECTIVE

Spine surgery has been identified as a significant source of healthcare expenditures in the United States. Prolonged hospitalization has been cited as one source of increased spending, and there has been drive from providers and payors alike to decrease inpatient stays. One strategy currently being explored is the use of Enhanced Recovery After Surgery (ERAS) protocols. Here, the authors review the literature on adult spine ERAS protocols, focusing on clinical benefits and cost reductions. They also conducted a quantitative meta-analysis examining the following: 1) length of stay (LOS), 2) complication rate, 3) wound infection rate, 4) 30-day readmission rate, and 5) 30-day reoperation rate.

METHODS

Using the PRISMA guidelines, a search of the PubMed/Medline, Web of Science, Cochrane Reviews, Embase, CINAHL, and OVID Medline databases was conducted to identify all full-text articles in the English-language literature describing ERAS protocol implementation for adult spine surgery. A quantitative meta-analysis using random-effects modeling was performed for the identified clinical outcomes using studies that directly compared ERAS protocols with conventional care.

RESULTS

Of 950 articles reviewed, 34 were included in the qualitative analysis and 20 were included in the quantitative analysis. The most common protocol types were general spine surgery protocols and protocols for lumbar spine surgery patients. The most frequently cited benefits of ERAS protocols were shorter LOS (n = 12), lower postoperative pain scores (n = 6), and decreased complication rates (n = 4). The meta-analysis demonstrated shorter LOS for the general spine surgery (mean difference −1.22 days [95% CI −1.98 to −0.47]) and lumbar spine ERAS protocols (−1.53 days [95% CI −2.89 to −0.16]). Neither general nor lumbar spine protocols led to a significant difference in complication rates. Insufficient data existed to perform a meta-analysis of the differences in costs or postoperative narcotic use.

CONCLUSIONS

Present data suggest that ERAS protocol implementation may reduce hospitalization time among adult spine surgery patients and may lead to reductions in complication rates when applied to specific populations. To generate high-quality evidence capable of supporting practice guidelines, though, additional controlled trials are necessary to validate these early findings in larger populations.

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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.