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James Feghali, Yangyiran Xie, Yuxi Chen, Sean Li, and Judy Huang

OBJECTIVE

The Chiari Severity Index (CSI) and points-based algorithm of Thakar et al. are two prognostic tools that have been developed to predict the likelihood of improvement after suboccipital decompression in adult patients with Chiari malformation type I (CM-I). This study aimed to externally validate and critically evaluate these algorithms in the interest of guiding the development of improved prediction systems.

METHODS

A consecutive cohort of CM-I patients undergoing suboccipital decompression between September 2006 and September 2018 were included. The CSI and Thakar point score were computed for all patients, and associations with improvement were analyzed. The ability of both prediction systems to predict improvement as measured by different Chicago Chiari Outcome Scale (CCOS) cutoffs was assessed using receiver operating curve analysis. Post hoc correlations between the algorithms and different CCOS subcomponents were also assessed.

RESULTS

The surgical cohort was composed of 149 adult CM-I patients, of whom 39 (26%) had a syrinx. Most patients experienced improvement after surgery (80% CCOS ≥ 13; 96% CCOS ≥ 11). The proportion of patients improving decreased with increasing CSI, but the results were not statistically significant (p = 0.246). No statistically significant difference in the mean Thakar point score was identified between improved and nonimproved patients using both CCOS cutoffs (p = 0.246 for a cutoff of 13 and p = 0.480 for a cutoff of 11). The CSI had a poor ability in identifying improved patients at a CCOS cutoff of 13 (area under the curve [AUC] 0.582) and 11 (AUC 0.646). The Thakar point score similarly had poor discrimination at a cutoff of 13 (AUC 0.467) and 11 (AUC 0.646). Neither algorithm had significant correlation with any of the CCOS subcomponents except for CSI and nonpain symptom improvement (coefficient = −0.273, p = 0.001).

CONCLUSIONS

Previously published algorithms failed to provide prediction value with regard to clinically meaningful improvement following suboccipital decompression in adult CM-I patients. Future models and practical scoring systems are still required to improve the decision-making process.

Free access

James Feghali, Yuxi Chen, Yangyiran Xie, Christopher Chen, and Judy Huang

OBJECTIVE

The effect of depression on outcomes in Chiari malformation type I (CM-1) is unclear. The authors sought to determine whether depression affects outcome in a surgical cohort of CM-1 patients by using a validated outcome assessment tool, the Chicago Chiari Outcome Scale (CCOS).

METHODS

The authors performed a retrospective analysis of a prospectively maintained database of 149 adult CM-1 patients undergoing suboccipital decompression with duraplasty and cranioplasty. Baseline presentation characteristics and composite as well as subcomponent CCOS scores at last follow-up were compared between depressed and nondepressed patients. Outcome comparisons included both a univariable analysis and a logistic regression model adjusting for several covariates.

RESULTS

The prevalence of depression in the study cohort was 28% (41/149). Baseline demographic and imaging characteristics were similar between the 2 patient groups. Dizziness (p = 0.019) and imbalance (p = 0.015) were significantly more common among depressed patients, but clinical symptoms and severity were otherwise comparable. On univariable analysis, depressed patients were significantly less likely to experience improvement in pain symptoms (OR 0.14, 95% CI 0.03–0.61, p = 0.003) and functionality (OR 0.17, 95% CI 0.03–0.99, p = 0.049). No significant difference was identified in complications, nonpain symptom improvement, or overall composite CCOS improvement. Similar results were obtained on multivariable analysis controlling for several covariates.

CONCLUSIONS

Depression is independently associated with poor surgical outcome in adult CM-1 patients, namely when evaluating improvement in pain symptoms and functionality. Optimizing the management of depression preoperatively and ensuring follow-up for psychiatric comorbidity in the postoperative period may possibly lead to improved 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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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, Risheng Xu, Wuyang Yang, Jason Anthony Liew, Jaishri Blakeley, Edward S. Ahn, Rafael J. Tamargo, and Judy Huang

OBJECTIVE

Phenotypic differences between moyamoya disease (MMD) and moyamoya syndrome (MMS) remain unclear. The purpose of this study was to evaluate whether such differences exist when presentation, procedure-related, and outcome variables are compared quantitatively.

METHODS

The study cohort included 185 patients with moyamoya presenting to the Johns Hopkins Medical Institutions between 1994 and 2015. Baseline demographic, angiographic, and clinical characteristics were compared between patients with MMS and MMD, in addition to procedure-related complications and length of stay (LOS) after surgery. Stroke-free survival was compared between both disease variants after diagnosis. Kaplan-Meier analysis and Cox proportional hazards regression were used to compare stroke-free survival between surgically treated and conservatively managed hemispheres in both types of disease, while evaluating interaction between disease variant and management.

RESULTS

The cohort consisted of 137 patients with MMD (74%) with a bimodal age distribution and 48 patients with MMS (26%) who were mostly under 18 years of age (75%). Underlying diseases included sickle cell disease (48%), trisomy 21 (12%), neurofibromatosis (23%), and other disorders (17%). Patients with MMS were younger (p < 0.001) and less likely to be female (p = 0.034). Otherwise, baseline characteristics were statistically comparable. The rate of surgical complications was 33% in patients with MMD and 16% in patients with MMS (p = 0.097). Both groups of patients had a similar LOS after surgery (p = 0.823). Survival analysis (n = 330 hemispheres) showed similar stroke-free survival after diagnosis (p = 0.856) and lower stroke hazard in surgically managed patients in both MMD (hazard ratio [HR] 0.29, p = 0.028) and MMS (HR 0.62, p = 0.586). The disease variant (MMD vs MMS) did not affect the relationship between management approach (surgery vs conservative) and stroke hazard (p = 0.787).

CONCLUSIONS

MMD and MMS have largely comparable clinical and angiographic phenotypes with analogously favorable responses to surgical revascularization.

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James Feghali, Risheng Xu, Wuyang Yang, Jason Liew, Rafael J. Tamargo, Elisabeth B. Marsh, and Judy Huang

OBJECTIVE

The authors aimed to determine whether differences exist in presentation and natural history when comparing Asian patients with moyamoya disease (MMD) to those of other ethnicities in North America.

METHODS

A database of 137 patients with MMD presenting to their institution between 1994 and 2015 was reviewed. Baseline characteristics and outcome variables, including stroke and functional outcome, were compared between Asian and non-Asian patients. Unadjusted Kaplan-Meier survival analysis and adjusted Cox regression models were used to compare stroke-free survival and stroke hazard after diagnosis among hemispheres of both racial groups. The analysis was stratified by age group, and censoring was performed until last follow-up or at the time of surgery. Because the relative rate of stroke changed between Asian and non-Asian adults after 1.5 years of follow-up, a time-segmented analysis focusing on the period 1.5 years after diagnosis was performed.

RESULTS

The cohort comprised 23% (31/137) Asian and 77% (106/137) non-Asian patients with MMD with a bimodal age distribution. Non-Asian patients had a higher prevalence of increased BMI (p = 0.02) and smoking (p = 0.04). Among patients who presented with stroke (n = 90), hemorrhage was significantly more common among Asians (p = 0.02). The natural history analysis included 250 hemispheres: 67 pediatric and 183 adult hemispheres. The overall mean follow-up duration since diagnosis was 3.3 years. Among adults, Asian patients had a higher incidence of stroke (8.0 per 100 person-years vs 3.0 per 100 person-years) over a mean follow-up of 3.3 years, but results were not statistically significant (p = 0.45). In the period beginning 1.5 years after diagnosis, Asian adults had a significantly higher hazard of stroke over a mean follow-up of 7.7 years, while controlling for sex, hypertension, and stroke before diagnosis (hazard ratio 8.8, p = 0.02). Among pediatric patients, Asians also had a higher stroke incidence (10.0 per 100 person-years vs 3.5 per 100 person-years) over a mean follow-up of 3.2 years; however, results did not reach statistical significance (p = 0.40). Functional outcome was similar between both ethnic groups at last follow-up (p = 0.57).

CONCLUSIONS

This study suggests a comparatively more progressive course of MMD in Asians. Further studies are required to fully characterize the phenotypic distinctions between different races and underlying pathophysiological mechanisms.

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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, 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.

Free access

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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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.