Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction. SUMMARY OF BACKGROUND DATA: Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities. Accurate preoperative prediction of patients who may require access to these resources can facilitate a more efficient referral and discharge process, thereby reducing hospital and patient costs in addition to minimizing the risk of hospital-acquired complications. METHODS: Electronic medical records were retrospectively reviewed from a single-center data warehouse (SCDW) to identify patients undergoing cervical spine surgeries between 2008 and 2019 for machine learning algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for external validation of algorithm performance. Gradient-boosted trees were constructed to predict nonhome discharge across patient cohorts. The area under the receiver operating characteristic curve (AUROC) was used to measure model performance. SHAP values were used to identify nonlinear risk factors for nonhome discharge and to interpret algorithm predictions. RESULTS: A total of 3523 cases of cervical spine fusion surgeries were included from the SCDW data set, and 311,582 cases were isolated from NIS. The model demonstrated robust prediction of nonhome discharge across all cohorts, achieving an area under the receiver operating characteristic curve of 0.87 (SD=0.01) on both the SCDW and nationwide NIS test sets. Anterior approach only, age, elective admission status, Medicare insurance status, and total Elixhauser Comorbidity Index score were the most important predictors of discharge destination. CONCLUSIONS: Machine learning algorithms reliably predict nonhome discharge across single-center and national cohorts and identify preoperative features of importance following cervical spine fusion surgery.

Surgical markup in lung cancer resection, 2015-2020.

Objective: The objective of this study was to assess procedure markup (charge-to-cost ratio) across lung resection procedures and examine variability by geographic region. Methods: Provider-level data for common lung resection operations was obtained from the 2015 to 2020 Medicare Provider Utilization and Payment Data datasets using Healthcare Common Procedure Coding System codes. Procedures studied included wedge resection; video-assisted thoracoscopic surgery; and open lobectomy, segmentectomy, and mediastinal and regional lymphadenectomy. Procedure markup ratio and coefficient of variation (CoV) was assessed and compared across procedure, region, and provider. The CoV, a measure of dispersion defined as the ratio of the SD to the mean, was likewise compared across procedure and region. Results: Median markup ratio across all procedures was 3.56 (interquartile range, 2.87-4.59) with right skew (mean, 4.13). Median markup ratio was 3.59 for lymphadenectomy (CoV, 0.51), 3.13 for open lobectomy (CoV, 0.45), 3.55 for video-assisted thoracoscopic surgery lobectomy (CoV, 0.59), 3.77 for segmentectomy (CoV, 0.74), and 3.80 for wedge resection (CoV, 0.67). Increased beneficiaries, services, and Healthcare Common Procedure Coding System score (total) were associated with a decreased markup ratio (P < .0001). Markup ratio was highest in the Northeast at 4.14 (interquartile range, 3.09-5.56) and lowest in the South (Markup ratio 3.26; interquartile range, 2.68-4.02). Conclusions: We observe geographic variation in surgical billing for thoracic surgery.

Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

Carbon fiber-reinforced PEEK spinal implants for primary and metastatic spine tumors: a systematic review on implant complications and radiotherapy benefits.

OBJECTIVE: By minimizing imaging artifact and particle scatter, carbon fiber-reinforced polyetheretherketone (CF-PEEK) spinal implants are hypothesized to enhance radiotherapy (RT) planning/dosing and improve oncological outcomes. However, robust clinical studies comparing tumor surgery outcomes between CF-PEEK and traditional metallic implants are lacking. In this paper, the authors performed a systematic review of the literature with the aim to describe clinical outcomes in patients with spine tumors who received CF-PEEK implants, focusing on implant-related complications and oncological outcomes. METHODS: A systematic review of the literature published between database inception and May 2022 was performed in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The PubMed database was queried using the terms "carbon fiber" and "spine" or "spinal." The inclusion criteria were articles that described patients with CF-PEEK pedicle screw fixation and had a minimum of 5 patients. Case reports and phantom studies were excluded. RESULTS: This review included 11 articles with 326 patients (237 with CF-PEEK-based implants and 89 with titanium-based implants). The mean follow-up period was 13.5 months, and most tumors were metastatic (67.1%). The rates of implant-related complications in the CF-PEEK and titanium groups were 7.8% and 4.7%, respectively. The rate of pedicle screw fracture was 1.7% in the CF-PEEK group and 2.4% in the titanium group. The rates of reoperation were 5.7% (with 60.0% because of implant failure or junctional kyphosis) and 4.8% (all because of implant failure or junctional kyphosis) in the CF-PEEK and titanium groups, respectively. When reported, 72.5% of patients received postoperative RT (41.0% stereotactic body RT, 30.8% fractionated RT, 25.6% proton, 2.6% carbon ion). Four articles suggested that implant artifact was reduced in the CF-PEEK group. Local recurrence occurred in 14.4% of CF-PEEK and 10.7% of titanium-implanted patients. CONCLUSIONS: While CF-PEEK harbors similar implant failure rates to traditional metallic implants with reduced imaging artifact, it remains unclear whether CF-PEEK implants improve oncological outcomes. This study highlights the need for prospective, direct comparative clinical studies.

Developing an Automated Registry (Autoregistry) of Spine Surgery Using Natural Language Processing and Health System Scale Databases.

BACKGROUND AND OBJECTIVES: Clinical registries are critical for modern surgery and underpin outcomes research, device monitoring, and trial development. However, existing approaches to registry construction are labor-intensive, costly, and prone to manual error. Natural language processing techniques combined with electronic health record (EHR) data sets can theoretically automate the construction and maintenance of registries. Our aim was to automate the generation of a spine surgery registry at an academic medical center using regular expression (regex) classifiers developed by neurosurgeons to combine domain expertise with interpretable algorithms. METHODS: We used a Hadoop data lake consisting of all the information generated by an academic medical center. Using this database and structured query language queries, we retrieved every operative note written in the department of neurosurgery since our transition to EHR. Notes were parsed using regex classifiers and compared with a random subset of 100 manually reviewed notes. RESULTS: A total of 31 502 operative cases were downloaded and processed using regex classifiers. The codebase required 5 days of development, 3 weeks of validation, and less than 1 hour for the software to generate the autoregistry. Regex classifiers had an average accuracy of 98.86% at identifying both spinal procedures and the relevant vertebral levels, and it correctly identified the entire list of defined surgical procedures in 89% of patients. We were able to identify patients who required additional operations within 30 days to monitor outcomes and quality metrics. CONCLUSION: This study demonstrates the feasibility of automatically generating a spine registry using the EHR and an interpretable, customizable natural language processing algorithm which may reduce pitfalls associated with manual registry development and facilitate rapid clinical research.

Surgical management of Hirayama disease in a pediatric patient presenting with severe cervical kyphosis and focal myelopathy: illustrative case.

BACKGROUND: Hirayama disease (HD) is a rare, nonfamilial neuromuscular disease causing cervical myelopathy and deformity, most commonly effecting pubertal Asian males. Patients whose nonoperative treatment fails and who cannot tolerate long-term cervical immobilization, experience relapse after arrest of symptoms, or present with severe features warrant surgical treatment. Here, the authors present an unusual case of HD that resulted in rapid progression of severe cervical kyphosis and discuss surgical management strategies. OBSERVATIONS: A 15-year-old male presented with unprovoked neck pain, progressive chin-on-chest phenomenon, and cervical myelopathy. Imaging revealed a severe subaxial cervical kyphosis of 88° and severe spinal cord compression secondary to changes within the thecal sac, ligaments, and bony elements. He underwent a multistage surgery involving halo gravity traction, C3-6 anterior cervical discectomy and fusion, and C2 to T2 posterior instrumented fusion with C3-5 Smith-Petersen osteotomies. Cervical subaxial pedicle screws facilitated deformity correction through a cantilever technique. LESSONS: HD is rare and often self-limited. For severe or refractory cases of HD, guidelines for surgical management have been suggested, with a variety of approaches deemed efficacious. This is the first case of a patient presenting with such severe cervical deformity; early diagnosis and recognition is the first step toward prompt, adequate management.

Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning.

PURPOSE: Predict nonhome discharge (NHD) following elective anterior cervical discectomy and fusion (ACDF) using an explainable machine learning model. METHODS: 2227 patients undergoing elective ACDF from 2008 to 2019 were identified from a single institutional database. A machine learning model was trained on preoperative variables, including demographics, comorbidity indices, and levels fused. The validation technique was repeated stratified K-Fold cross validation with the area under the receiver operating curve (AUROC) statistic as the performance metric. Shapley Additive Explanation (SHAP) values were calculated to provide further explainability regarding the model's decision making. RESULTS: The preoperative model performed with an AUROC of 0.83 ± 0.05. SHAP scores revealed the most pertinent risk factors to be age, medicare insurance, and American Society of Anesthesiology (ASA) score. Interaction analysis demonstrated that female patients over 65 with greater fusion levels were more likely to undergo NHD. Likewise, ASA demonstrated positive interaction effects with female sex, levels fused and BMI. CONCLUSION: We validated an explainable machine learning model for the prediction of NHD using common preoperative variables. Adding transparency is a key step towards clinical application because it demonstrates that our model's "thinking" aligns with clinical reasoning. Interactive analysis demonstrated that those of age over 65, female sex, higher ASA score, and greater fusion levels were more predisposed to NHD. Age and ASA score were similar in their predictive ability. Machine learning may be used to predict NHD, and can assist surgeons with patient counseling or early discharge planning.

A Nationwide Study Characterizing the Risk and Outcome Profiles of Multilevel Fusion Procedures in Neuromuscular Scoliosis Patients with Neurofibromatosis Type 1.

BACKGROUND: Spine abnormalities are a common manifestation of Neurofibromatosis Type 1 (NF1); however, the outcomes of surgical treatment for NF1-associated spinal deformity are not well explored. The purpose of this study was to investigate the outcome and risk profiles of multilevel fusion surgery for NF1 patients. METHODS: The National Inpatient Sample was queried for NF1 and non-NF1 patient populations with neuromuscular scoliosis who underwent multilevel fusion surgery involving eight or more vertebral levels between 2004 and 2017. Multivariate regression modeling was used to explore the relationship between perioperative variables and pertinent outcomes. RESULTS: Of the 55,485 patients with scoliosis, 533 patients (0.96%) had NF1. Patients with NF1 were more likely to have comorbid solid tumors (P < 0.0001), clinical depression (P < 0.0001), peripheral vascular disease (P < 0.0001), and hypertension (P < 0.001). Following surgery, NF1 patients had a higher incidence of hydrocephalus (0.6% vs. 1.9% P = 0.002), seizures (4.9% vs. 5.7% P = 0.006), and accidental vessel laceration (0.3% vs.1.9% P = 0.011). Although there were no differences in overall complication rates or in-hospital mortality, multivariate regression revealed NF1 patients had an increased probability of pulmonary (OR 0.5, 95%CI 0.3-0.8, P = 0.004) complications. There were no significant differences in utilization, including nonhome discharge or extended hospitalization; however, patients with NF1 had higher total hospital charges (mean -$18739, SE 3384, P < 0.0001). CONCLUSIONS: These findings indicate that NF1 is associated with certain complications following multilevel fusion surgery but does not appear to be associated with differences in quality or cost outcomes. These results provide some guidance to surgeons and other healthcare professionals in their perioperative decision making by raising awareness about risk factors for NF1 patients undergoing multilevel fusion surgery. We intend for this study to set the national baseline for complications after multilevel fusion in the NF1 population.

Comparative Analysis of Early Outcomes and Complications of PSO Among Neurosurgeons and Orthopedic Surgeons.

STUDY DESIGN: Retrospective comparative cohort study using the National Surgical Quality Improvement Program. OBJECTIVE: The aim of this study was to evaluate trends in the annual number of PSOs performed, describe the patient populations associated with each cohort, and compare outcomes between specialties.Summary of Background Data:Pedicle subtraction osteotomies (PSO) are complex and advanced spine deformity surgical procedures performed by neurosurgeons and orthopedic surgeons. Though both sets of surgeons can be equally qualified and credentialed to perform a PSO, it is possible that differences in training and exposure could translate into differences in patient management and outcomes. METHODS: Patients that underwent lumbar PSO from 2005 to 2014 in the American College of Surgeons-National Surgical Quality Improvement Program registry were identified. Relevant demographic, preoperative comorbidity, and postoperative 30-day complications were queried and analyzed. The data was divided into 2 cohorts consisting of those patients who were treated by neurosurgeons versus orthopedic surgeons. Additional data from the Scoliosis Research Society Morbidity and Mortality database was queried and analyzed for comparison. RESULTS: Demographic and comorbidity factors were similar between the neurosurgery and orthopedic surgery cohorts, except there were higher rates of hypertension among orthopedic surgeon-performed PSOs (65.66% vs. 48.67%, P =0.004). Except for 2012, in every year queried, orthopedic surgeons reported more PSOs than neurosurgeons. In patients who underwent lumbar fusion surgery, there was a higher rate of PSOs if the surgery was performed by an orthopedic surgeon (OR 1.7824, 95% CI: 1.4017-2.2665). The incidence of deep vein thrombosis after PSOs was higher for neurosurgery compared with orthopedic surgery (8.85% vs. 1.20%, P =0.004). However, besides deep vein thrombosis, there were no salient differences in surgical complication rates between neurosurgeon-performed PSOs and orthopedic surgeon-performed PSOs. CONCLUSIONS: The number of PSO procedures performed by neurosurgeons and orthopedic surgeons has increased annually. Differences in outcomes between neurosurgeons and orthopedic surgeons suggest an opportunity for wider assessment and alignment of adult spinal deformity surgery exposure and training across specialties.

The Impact of Obstructive Sleep Apnea on Clinical, Perioperative, and Cost Outcomes in Patients Who Underwent Posterior Cervical Decompression and Fusion: A Single-Center Retrospective Analysis From 2008 to 2016.

BACKGROUND: Obstructive sleep apnea (OSA) is a pervasive problem that can result in diminished neurocognitive performance, increased risk of all-cause mortality, and significant cardiovascular disease. While previous studies have examined risk factors that influence outcomes following cervical fusion procedures, to our knowledge, no study has examined the cost or outcome profiles for posterior cervical decompression and fusion (PCDF) procedures in patients with OSA. METHODS: All cases at a single institution between 2008 and 2016 involving a PCDF were included. The primary outcome was prolonged extubation, defined as an extubation that took place outside of the operating room. Secondary outcomes included admission to the intensive care unit (ICU), complications, extended hospitalization, nonhome discharge, readmission within 30 and 90 days, emergency room visit within 30 and 90 days, and higher total costs. RESULTS: We reviewed 1191 PCDF cases, of which 93 patients (7.81%) had a history of OSA. At the univariate level, patients with OSA had higher rates of ICU admissions (33.3% vs 16.8%, P < 0.0001), total complications (29.0% vs 19.0%, P = 0.0202), and respiratory complications (12.9% vs 6.6%, P = 0.0217). Multivariate regression analyses revealed no difference in the odds of a prolonged extubation (P = 0.4773) and showed that history of OSA was not predictive of higher costs. However, a significant difference was observed in the odds of having an ICU admission (P = 0.0046). CONCLUSION: While patients with sleep apnea may be more likely to be admitted to the ICU postoperatively, OSA status a lone is not a risk factor for poor primary and secondary clinical outcomes following posterior cervical fusion procedures. CLINICAL RELEVANCE: Various deformities of the cervical spine can exert extraluminal forces that partially collapse or obstruct the airway, thereby predisposing patients to OSA; however, no study has examined the cost or outcome profiles for PCDF procedures in patients with OSA. Therefore, this investigation highlights the ways in which OSA influences the risks, outcomes, and costs following PCDF using medical data from an institutional registry.

Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale.

BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical harm and resource utilization. OBJECTIVE: To develop a machine learning algorithm aimed at predicting extended LOS after cervical spine surgery on a national level and elucidate drivers of prediction. METHODS: Electronic medical records from a large, urban academic medical center were retrospectively examined to identify patients who underwent cervical spine fusion surgeries between 2008 and 2019 for machine learning algorithm development and in-sample validation. The National Inpatient Sample database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for out-of-sample validation of algorithm performance. Gradient-boosted trees predicted LOS and efficacy was assessed using the area under the receiver operating characteristic curve (AUROC). Shapley values were calculated to characterize preoperative risk factors for extended LOS and explain algorithm predictions. RESULTS: Gradient-boosted trees accurately predicted extended LOS across cohorts, achieving an AUROC of 0.87 (SD = 0.01) on the single-center validation set and an AUROC of 0.84 (SD = 0.00) on the nationwide National Inpatient Sample data set. Anterior approach only, elective admission status, age, and total number of Elixhauser comorbidities were important predictors that affected the likelihood of prolonged LOS. CONCLUSION: Machine learning algorithms accurately predict extended LOS across single-center and national patient cohorts and characterize key preoperative drivers of increased LOS after cervical spine surgery.

Population scale latent space cohort matching for the improved use and exploration of observational trial data.

A significant amount of clinical research is observational by nature and derived from medical records, clinical trials, and large-scale registries. While there is no substitute for randomized, controlled experimentation, such experiments or trials are often costly, time consuming, and even ethically or practically impossible to execute. Combining classical regression and structural equation modeling with matching techniques can leverage the value of observational data. Nevertheless, identifying variables of greatest interest in high-dimensional data is frequently challenging, even with application of classical dimensionality reduction and/or propensity scoring techniques. Here, we demonstrate that projecting high-dimensional medical data onto a lower-dimensional manifold using deep autoencoders and post-hoc generation of treatment/control cohorts based on proximity in the lower-dimensional space results in better matching of confounding variables compared to classical propensity score matching (PSM) in the original high-dimensional space (P<0.0001) and performs similarly to PSM models constructed by experts with prior knowledge of the underlying pathology when evaluated on predicting risk ratios from real-world clinical data. Thus, in cases when the underlying problem is poorly understood and the data is high-dimensional in nature, matching in the autoencoder latent space might be of particular benefit.

Robust Prediction of Non-home Discharge After Thoracolumbar Spine Surgery With Ensemble Machine Learning and Validation on a Nationwide Cohort.

BACKGROUND: Delays in postoperative referrals to rehabilitation or skilled nursing facilities contribute toward extended hospital stays. Facilitating more efficient referrals through accurate preoperative prediction algorithms has the potential to reduce unnecessary economic burden and minimize risk of hospital-acquired complications. We develop a robust machine learning algorithm to predict non-home discharge after thoracolumbar spine surgery that generalizes to unseen populations and identifies markers for prediction. METHODS: Retrospective electronic health records were obtained from our single-center data warehouse (SCDW) to identify patients undergoing thoracolumbar spine surgeries between 2008 and 2019 for algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify thoracolumbar surgeries between 2009 and 2017 for out-of-sample validation. Ensemble decision trees were constructed for prediction and area under the receiver operating characteristic curve (AUROC) was used to assess performance. Shapley additive explanations values were derived to identify drivers of non-home discharge for interpretation of algorithm predictions. RESULTS: A total of 5224 cases of thoracolumbar spine surgeries were isolated from the SCDW and 492,312 cases were identified from NIS. The model achieved an AUROC of 0.81 (standard deviation [SD] = 0.01) on the SCDW test set and 0.77 (SD = 0.01) on the nationwide NIS data set, thereby demonstrating robust prediction of non-home discharge across all diverse patient cohorts. Age, total Elixhauser comorbidities, Medicare insurance, weighted Elixhauser score, and female sex were among the most important predictors of non-home discharge. CONCLUSIONS: Machine learning algorithms reliably predict non-home discharge after thoracolumbar spine surgery across single-center and national cohorts and identify preoperative features of importance that elucidate algorithm decision-making.

Management and surgical outcomes of dystrophic scoliosis in neurofibromatosis type 1: a systematic review.

OBJECTIVE: Neurofibromatosis type 1 (NF1) dystrophic scoliosis is an early-onset, rapidly progressive multiplanar deformity. There are few studies on the surgical management of this patient population. Specifically, perioperative morbidity, instrument-related complications, and quality-of-life outcomes associated with surgical management have not been systematically evaluated. In this study, the authors aimed to perform a systematic review on the natural history, management options, and surgical outcomes in patients who underwent NF1 dystrophic scoliosis surgery. METHODS: A PubMed search for articles with "neurofibromatosis" and either "dystrophic" or "scoliosis" in the title or abstract was performed. Articles with 10 or more patients undergoing surgery for NF1 dystrophic scoliosis were included. Data regarding indications, treatment details, morbidity, and outcomes were summarized and analyzed with descriptive statistics. RESULTS: A total of 310 articles were identified, 48 of which were selected for full-text review; 30 studies describing 761 patients met the inclusion criteria. The mean age ranged from 7 to 22 years, and 99.7% of patients were younger than 18 years. The mean preoperative coronal Cobb angle was 75.2°, and the average correction achieved was 40.3°. The mean clinical follow-up in each study was at least 2 years (range 2.2-19 years). All patients underwent surgery with the intent of deformity correction. The scoliosis regions addressed were thoracic curves (69.6%) and thoracolumbar (11.1%) and lumbar (14.3%) regions. The authors reported on a variety of approaches: posterior-only, combined anterior-posterior, and growth-friendly surgery. For fixation techniques, 42.5% of patients were treated with hybrid constructs, 51.5% with pedicle screw-only constructs, and 6.0% with hook-based constructs. Only 0.9% of patients underwent a vertebral column resection. The nonneurological complication rate was 14.0%, primarily dural tears and wound infections. The immediate postoperative neurological deficit rate was 2.1%, and the permanent neurological deficit rate was 1.2%. Ultimately, 21.5% required revision surgery, most commonly for implant-related complications. Loss of correction in both the sagittal and coronal planes commonly occurred at follow-up. Five papers supplied validated patient-reported outcome measures, showing improvement in the mental health, self-image, and activity domains. CONCLUSIONS: Data on the surgical outcomes of dystrophic scoliosis correction are heterogeneous and sparse. The perioperative complication rate appears to be high, although reported rates of neurological deficits appear to be lower than clinically observed and may be underreported. The incidence of implant-related failures requiring revision surgery is high. There is a great need for multicenter prospective studies of this complex type of deformity.

Diabetes Is Predictive of Postoperative Outcomes and Readmission Following Posterior Lumbar Fusion.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: The present study analyzes complication rates and episode-based costs for patients with and without diabetes mellitus (DM) following posterior lumbar fusion (PLF). METHODS: PLF cases at a single institution from 2008 to 2016 were queried (n = 3226), and demographic and perioperative data were analyzed. Patients with and without the diagnosis of DM were compared using chi-square, Student's t test, and multivariable regression modeling. RESULTS: Patients with diabetes were older (63.10 vs 56.48 years, P < .001) and possessed a greater number of preoperative comorbidities (47.84% of patients had Elixhauser Comorbidity Index >0 vs 42.24%, P < .001) than did patients without diabetes. When controlling for preexisting differences, diabetes remained a significant risk factor for prolonged length of stay (OR = 1.59, 95% CI 1.26-2.01, P < .001), intensive care unit stay (OR = 1.52, 95% CI 1.07-2.17, P = .021), nonhome discharge (OR = 1.86, 95% CI 1.46-2.37, P < .001), 30-day readmission (OR = 2.15, 95% CI 1.28-3.60, P = .004), 90-day readmission (OR = 1.65, 95% CI 1.05-2.59, P = .031), 30-day emergency room visit (OR = 2.15, 95% CI 1.27-3.63, P = .004), and 90-day emergency room visit (OR = 2.27, 95% CI 1.41-3.65, P < .001). Cost modeling controlling for overall comorbidity burden demonstrated that diabetes was associated with a $1709 increase in PLF costs (CI $344-$3074, P = .014). CONCLUSIONS: The present findings indicate a correlation between diabetes and a multitude of postoperative adverse outcomes and increased costs, thus illustrating the substantial medical and financial burdens of diabetes for PLF patients. Future studies should explore preventive measures that may mitigate these downstream effects.

MIS-TLIF Procedure is Improving With Experience: Systematic Review of the Learning Curve Over the Last Decade.

STUDY DESIGN: This was a systematic review. OBJECTIVE: This review evaluates the minimally invasive transforaminal lumbar interbody fusions (MIS-TLIF) learning curve in the literature and compares outcomes during and after completing the curve. SUMMARY OF BACKGROUND DATA: MIS-TLIF are performed for various spine conditions. Proponents cite improved clinical outcomes while critics highlight the steep learning curve to attain proficiency. METHODS: Literature searches on Medline and Embase utilized relevant subject headings and keywords. Manuscripts reporting learning curve statistics were included. Monotonic trends of operative duration were assessed with Mann-Kendall nonparametric testing. RESULTS: Nine studies met inclusion criteria. Number of patients ranged from 26 to 150 (average 83.2, median of 86). Commonly reported metrics included number of procedures to complete the curve, operative duration, blood loss, ambulation time, length of stay, complication rate, follow-up visual analogue scale (VAS) for back and leg pain, and fusion rate. Various methods were employed to determine number of cases to complete the curve, all involving operative duration. Number of cases ranged from 14 to 44. A significant negative trend for operative duration of cases during the learning curve (τ=-0.733, P =0.039) was found over the years that studies were published. Initial complication rates varied from 6.8% to 23.8%. Initial VAS-back and VAS-leg ranged from 0.8 to 2.9 and 0.5 to 2.3, respectively. While definitions of "good" fusion varied, fusion rates meeting Bridwell grade I or II during the learning curve ranged from 84.0% to 95.2%. CONCLUSIONS: Surgeons in their learning curve have become faster at the MIS-TLIF procedure. Clinical outcomes including postoperative pain and fusion rates showed satisfactory results, but surgeons learning the procedure should take measures to minimize complications in early cases, such as utilizing novel navigation technology or supervision from more experienced surgeons. Learning curve research methodology could benefit from standardization.

Neurosurgical Performance in the First 2 Years of Merit-Based Incentive Payment System: A Descriptive Analysis and Predictors of Receiving Bonus Payments.

BACKGROUND: The merit-based incentive payment system (MIPS) program was implemented to tie Medicare reimbursements to value-based care measures. Neurosurgical performance in MIPS has not yet been described. OBJECTIVE: To characterize neurosurgical performance in the first 2 years of MIPS. METHODS: Publicly available data regarding MIPS performance for neurosurgeons in 2017 and 2018 were queried. Descriptive statistics about physician characteristics, MIPS performance, and ensuing payment adjustments were performed, and predictors of bonus payments were identified. RESULTS: There were 2811 physicians included in 2017 and 3147 in 2018. Median total MIPS scores (99.1 vs 90.4, P < .001) and quality scores (97.9 vs 88.5, P < .001) were higher in 2018 than in 2017. More neurosurgeons (2758, 87.6%) received bonus payments in 2018 than in 2017 (2013, 71.6%). Of the 2232 neurosurgeons with scores in both years, 1347 (60.4%) improved their score. Reporting through an alternative payment model (odds ratio [OR]: 32.3, 95% CI: 16.0-65.4; P < .001) and any practice size larger than 10 (ORs ranging from 2.37 to 10.2, all P < .001) were associated with receiving bonus payments. Increasing years in practice (OR: 0.99; 95% CI: 0.982-0.998, P = .011) and having 25% to 49% (OR: 0.72; 95% CI: 0.53-0.97; P = .029) or ≥50% (OR: 0.48; 95% CI: 0.28-0.82; P = .007) of a physician's patients eligible for Medicaid were associated with lower rates of bonus payments. CONCLUSION: Neurosurgeons performed well in MIPS in 2017 and 2018, although the program may be biased against surgeons who practice in small groups or take care of socially disadvantaged patients.

Seasonal Effects on Surgical Site Infections Following Spine Surgery.

BACKGROUND: Studies investigating seasonality as a risk factor for surgical site infections (SSIs) after spine surgery show mixed results. This study used national data to analyze seasonal effects on spine surgery SSIs. METHODS: National Surgical Quality Improvement Program data (2011-2018) were queried for posterior cervical fusions (PCFs), cervical laminoplasties, posterior lumbar fusions (PLFs), lumbar laminectomies, and deformity surgeries. Patients aged >89 and procedures for tumors, fractures, infections, and nonelective indications were excluded. Patients were divided into warm (admitted April-September) and cold (admitted October-March) seasonal groups. End points were SSIs and reoperations for wound débridement/drainage. Stratified analyses were performed by surgery type and pre-versus postdischarge infections. RESULTS: Overall (N = 208,291), SSIs were more likely in the warm season (odds ratio [OR] 1.15, 95% confidence interval [CI] 1.08-1.23, P < 0.0001) and for PCFs (OR 1.40, 95% CI 1.08-1.80, P = 0.011), PLFs (OR 1.15, 95% CI 1.04-1.28, P = 0.006), and lumbar laminectomies (OR 1.13, 95% CI 1.03-1.25, P = 0.014). Postdischarge infections were also more likely in the warm season overall (OR 1.15, 95% CI 1.07-1.23, P < 0.0001) and for PCFs (OR 1.32, 95% CI 1.01-1.73, P = 0.041), PLFs (OR 1.14, 95% CI 1.03-1.27, P = 0.014), and lumbar laminectomies (OR 1.15, CI 1.04-1.27, P = 0.007). In-hospital infections were more likely during the warm season only for PCFs (OR 2.54, 95% CI 1.06-6.10, P = 0.037). Reoperations for infection were more likely during the warm season for PLFs (OR 1.29, 95% CI 1.08-1.54, P = 0.005). CONCLUSIONS: PCF, PLF, and lumbar laminectomy performed during the warm season had significantly higher odds of SSI, especially postdischarge SSIs. Reoperation rates for wound management were significantly increased during the warm season for PLFs. Identifying seasonal causes merits further investigation and may influence surgeon scheduling and expectations.

Intraoperative Navigation in Spine Surgery: Effects on Complications and Reoperations.

INTRODUCTION: Intraoperative navigation during spine surgery improves pedicle screw placement accuracy. However, limited studies have correlated the use of navigation with clinical factors, including operative time and safety. In the present study, we compared the complications and reoperations between surgeries with and without navigation. METHODS: The National Surgical Quality Improvement Project database was queried for posterior cervical and lumbar fusions and deformity surgeries from 2011 to 2018 and divided by navigation use. Patients aged >89 years, patients with deformity aged <25 years, and patients undergoing surgery for tumors, fractures, infections, or nonelective indications were excluded. The demographics and perioperative factors were compared via univariate analysis. The outcomes were compared using multivariable logistic regression adjusting for age, sex, body mass index, American Society of Anesthesiologists class, surgical region, and multiple treatment levels. The outcomes were also compared stratifying by revision status. RESULTS: Navigation surgery patients had had higher American Society of Anesthesiologists class (P < 0.0001), more multiple level surgeries (P < 0.0001), and longer operative times (P < 0.0001). The adjusted analysis revealed that navigated lumbar surgery had lower odds of complications (odds ratio [OR], 0.82; 95% confidence interval [CI], 0.77-0.90; P < 0.0001), blood transfusion (OR, 0.79; 95% CI, 0.72-0.87; P < 0.0001), and wound debridement and/or drainage (OR, 0.66; 95% CI, 0.44-0.97; P = 0.04) compared with non-navigated lumbar surgery. Navigated cervical fusions had increased odds of transfusions (OR, 1.53; 95% CI, 1.06-2.23; P = 0.02). Navigated primary fusion had decreased odds of complications (OR, 0.91; 95% CI, 0.85-0.98; P = 0.01). However, no differences were found in revisions (OR, 0.89; 95% CI, 0.69-1.14; P = 0.34). CONCLUSIONS: Navigated surgery patients experienced longer operations owing to a combination of the time required for navigation, more multilevel procedures, and a larger comorbidity burden, without differences in the incidence of infection. Fewer complications and wound washouts were required for navigated lumbar surgery owing to a greater proportion percentage of minimally invasive cases. The combined use of navigation and minimally invasive surgery might benefit patients with the proper indications.

The Impact of Diabetes on Outcomes and Health Care Costs Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Anterior cervical discectomy and fusion (ACDF) is commonly used to treat an array of cervical spine pathology and is associated with good outcomes and low complication rates. Diabetes mellitus (DM) is a common comorbidity for patients undergoing ACDF, but the literature is equivocal about the impact it has on outcomes. Because DM is a highly prevalent comorbidity, it is crucial to determine if it is an associated risk factor for outcomes after ACDF procedures. METHODS: Patients at a single institution from 2008 to 2016 undergoing ACDF were compared on the basis of having a prior diagnosis of DM versus no DM. The 2 cohorts were compared utilizing univariate tests and multivariate logistic and linear regressions. RESULTS: Data for 2470 patients was analyzed. Diabetic patients had significantly higher Elixhauser scores (P < .0001). Univariate testing showed diabetic patients were more likely to suffer from sepsis (0.82% vs 0.10%, P = .03) and bleeding complications (3.0% vs 1.5%, P = .04). In multivariate analyses, diabetic patients had higher rates of non-home discharge (odds ratio [OR] = 1.37, 95% confidence interval [CI] = 1.07-1.75, P = .013) and prolonged length of stay (OR = 1.95, 95% CI = 1.25-3.05, P = .003), but similar complication (OR = 1.46, 95% CI = 0.85-2.52, P = .17), reoperation (OR = 0.77, 95% CI = 0.33-1.81, P = .55), and 90-day readmission (OR = 1.53, 95% CI = 0.97-2.43) rates compared to nondiabetic patients. Direct cost was also shown to be similar between the cohorts after adjusting for patient, surgical, and hospital-related factors (estimate = -$30.25, 95% CI = -$515.69 to $455.18, P = .90). CONCLUSIONS: Diabetic patients undergoing ACDF had similar complication, reoperation, and readmission rates, as well as similar cost of care compared to nondiabetic patients.