Medicare payment trends compared to inflation for anesthesia services.

STUDY OBJECTIVE: Identify changes and trends in the real value of Medicare payments for anesthesia services between 2000 and 2020 and how it may affect practices. DESIGN: Retrospective analysis. SETTING: We utilized the Physician/Supplier Procedure Summary (PSPS) datasets of Medicare Part B claims to identify high volume anesthesia services in 2020 with 20 years of data. The Consumer Price Index was used as a measure of inflation to adjust prices. PATIENTS: The PSPS datasets contain summaries of all annual Medicare Part B claims and payment amounts by carrier and locality. INTERVENTIONS: Patients receiving anesthesia services. MEASUREMENTS: For each service, identified by Current Procedural Terminology (CPT) codes, we trended the average Medicare payment per procedure from 2000 to 2020 and calculated year to year changes and compound annual growth rate (CAGR). We also evaluated base and time units for each CPT code and the national Medicare anesthesia conversion factor (CF) for the same years. MAIN RESULTS: The average Medicare payment in the study sample increased 20.1% from 2000 to 2020. After adjusting for inflation, the average Medicare payment per anesthesia service decreased by 20.8% over that period. The Medicare anesthesia CF increased 24.9% in the same period, and after adjusting for inflation, the real value of the CF decreased 16.9%. Average CAGR across the 20 anesthesia services was 0.88%, compared to the average annual inflation at 2.06%. CONCLUSIONS: Average Medicare payment for common anesthesia services after adjusting for inflation have decreased from 2000 to 2020, consistent with findings in other physician specialties. Understanding these trends is important for practice viability and suggests significant financial implications for anesthesia practices and hospitals if the trend were to continue.

Scoping Review: Anesthesiologist Involvement in Alternative Payment Models, Value Measurement, and Nonclinical Capabilities for Success in the United States of America.

The US healthcare sector is undergoing significant payment reforms, leading to the emergence of Alternative Payment Models (APMs) aimed at improving clinical outcomes and patient experiences while reducing costs. This scoping review provides an overview of the involvement of anesthesiologists in APMs as found in published literature. It specifically aims to categorize and understand the breadth and depth of their participation, revolving around 3 main axes or "Aims": (1) shaping APMs through design and implementation, (2) gauging the value and quality of care provided by anesthesiologists within these models, and (3) enhancing nonclinical abilities of anesthesiologists for promoting more value in care. To map out the existing literature, a comprehensive search of relevant electronic databases was conducted, yielding a total of 2173 articles, of which 24 met the inclusion criteria, comprising 21 prospective or retrospective cohort studies, 2 surveys, and 1 case-control cohort study. Eleven publications (45%) discussed value-based, bundled, or episode-based payments, whereas the rest discussed non-payment-based models, such as Enhanced Recovery After Surgery (7 articles, 29%), Perioperative Surgical Home (4 articles, 17%), or other models (3 articles, 13%).The review identified key themes related to each aim. The most prominent themes for aim 1 included protocol standardization (16 articles, 67%), design and implementation leadership (8 articles, 33%), multidisciplinary collaboration (7 articles, 29%), and role expansion (5 articles, 21%). For aim 2, the common themes were Process-Based & Patient-Centric Metrics (1 article, 4%), Shared Accountability (3 articles, 13%), and Time-Driven Activity-Based Costing (TDABC) (3 articles, 13%). Furthermore, we identified a wide range of quality metrics, spanning 8 domains that were used in these studies to evaluate anesthesiologists' performance. For aim 3, the main extracted themes included Education on Healthcare Transformation and Policies (3 articles, 13%), Exploring Collaborative Leadership Skills (5 articles, 21%), and Embracing Advanced Analytics and Data Transparency (4 articles, 17%).Findings revealed the pivotal role of anesthesiologists in the design, implementation, and refinement of these emerging delivery and payment models. Our results highlight that while payment models are shifting toward value, patient-centered metrics have yet to be widely accepted for use in measuring quality and affecting payment for anesthesiologists. Gaps remain in understanding how anesthesiologists assess their direct impact and strategies for enhancing the sustainability of anesthesia practices. This review underscores the need for future research contributing to the successful adaptation of clinical practices in this new era of healthcare delivery.

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.

Clinical Considerations and Outcomes for Spine Surgery Patients with a History of Transplant: A Systematic Scoping Review.

OBJECTIVE: The objective of this study was to investigate the perioperative management and outcomes of patients with a prior history of successful transplantation undergoing spine surgery. METHODS: We searched Medline, Embase, and Cochrane Central Register of Controlled Trials for matching reports in July 2021. We included case reports, cohort studies, and retrospective analyses, including terms for various transplant types and an exhaustive list of key words for various forms of spine surgery. RESULTS: We included 45 studies consisting of 34 case reports (published 1982-2021), 3 cohort analyses (published 2005-2006), and 8 retrospective analyses (published 2006-2020). The total number of patients included in the case reports, cohort studies, and retrospective analysis was 35, 48, and 9695, respectively. The mean 1-year mortality rate from retrospective analyses was 4.6% ± 1.93%, while the prevalence of perioperative complications was 24%. Cohort studies demonstrated an 8.5% ± 12.03% 30-day readmission rate. The most common procedure performed was laminectomy (38.9%) among the case reports. Mortality after spine surgery was noted for 4 of 35 case report patients (11.4%). CONCLUSIONS: This is the first systematic scoping review examining the population of transplant patients with subsequent unrelated spine surgery. There is significant heterogeneity in the outcomes of post-transplant spine surgery patients. Given the inherent complexity of managing this group and elevated mortality and complications compared to the general spine surgery population, further investigation into their clinical care is warranted.

An 11-Year-Long Analysis of the Risks Associated With Age in Patients Undergoing Anterior Cervical Discectomy and Fusion in a Large, Urban Academic Hospital.

STUDY DESIGN: A retrospective database study of patients at an urban academic medical center undergoing an Anterior Cervical Discectomy and Fusion (ACDF) surgery between 2008 and 2019. OBJECTIVE: ACDF is one of the most common spinal procedures. Old age has been found to be a common risk factor for postoperative complications across a plethora of spine procedures. Little is known about how this risk changes among elderly cohorts such as the difference between elderly (60+) and octogenarian (80+) patients. This study seeks to analyze the disparate rates of complications following elective ACDF between patients aged 60-69 or 70-79 and 80+ at an urban academic medical center. METHODS: We identified patients who had undergone ACDF procedures using CPT codes 22,551, 22,552, and 22,554. Emergent procedures were excluded, and patients were subdivided on the basis of age. Then each cohort was propensity matched for univariate and univariate logistic regression analysis. RESULTS: The propensity matching resulted in 25 pairs in both the 70-79 and 80+ y.o. cohort comparison and 60-69 and 80+ y.o. cohort comparison. None of the cohorts differed significantly in demographic variables. Differences between elderly cohorts were less pronounced: the 80+ y.o. cohort experienced only significantly higher total direct cost (P = .03) compared to the 70-79 y.o. cohort and significantly longer operative time (P = .04) compared to the 60-69 y.o. cohort. CONCLUSIONS: Octogenarian patients do not face much riskier outcomes following elective ACDF procedures than do younger elderly patients. Age alone should not be used to screen patients for ACDF.

Analgesic options for anterior approach to scoliosis repair: a scoping review.

PURPOSE: The ideal analgesic regimen for the anterior approach to scoliosis repair is not clearly defined. The purpose of the study was to summarize and identify gaps in the current literature specific to the anterior approach to scoliosis repair. METHODS: A scoping review was conducted in July 2022 utilizing PubMed, Cochrane, and Scopus databases guided by the PRISMA-ScR framework. RESULTS: The database search generated 641 possible articles, 13 of which met all inclusion criteria. All articles focused on the effectiveness and safety of regional anesthetic techniques, while a minority also provided both opioid and non-opioid medication frameworks. CONCLUSION: Continuous Epidural Analgesia (CEA) is the most well-studied intervention for pain control in anterior scoliosis repair, but other, more novel regional anesthetic techniques offer safe and effective potential alternatives. More research is indicated to compare the effectiveness of different regional techniques and perioperative medication regimens specific to anterior scoliosis repair.

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.

Seasonal Effects on Postoperative Complications After Spinal Surgery: A National Database Analysis.

OBJECTIVE: To investigate the role of seasonality on postoperative complications after spinal surgery. METHODS: Data were obtained from the American College of Surgeons National Surgical Quality Improvement Program database from 2011 to 2018. Current Procedural Terminology codes were used to identify the following procedures: posterior cervical decompression and fusion, cervical laminoplasty, posterior lumbar fusion, lumbar laminectomy, and spinal deformity surgery. The database was queried for deep vein thrombosis (DVT), pulmonary embolism, pneumonia, sepsis, septic shock, Clostridium difficile infection, stroke, cardiac arrest, myocardial infarction, urinary tract infection (UTI), and early unplanned hospital readmission (readmission). Warm season was defined as April-September, whereas cold season was defined as October-March. Statistical analysis included computing overall complication rates and comparison between seasons using univariate analysis and multivariable logistic regression. RESULTS: A total of 208,291 individuals underwent spinal surgery from 2011 to 2018. There was a statistically significant increase in UTI (odds ratio [OR], 1.16; 95% confidence interval [CI], 1.07-1.26; P = 0.0002) and readmission (OR, 1.06; 95% CI, 1.02-1.11, P = 0.007) in the warm season compared with the cold season. An investigation into the July effect showed increases in DVT (OR, 1.24; 95% CI, 1.03-1.48; P = 0.020) and thromboembolic events (OR 1.17; 95% CI, 1.01-1.35; P = 0.032) in July-September compared with the preceding 3 months. CONCLUSIONS: The results showed a higher incidence of UTI and readmission among spine surgery patients in the warm season and a higher incidence of DVT and thromboembolic events from July to September. In both cases, the effect of seasonality is statistically significant, but the absolute difference is small and may not suggest policy changes.

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.

Visual Analytics to Leverage Anesthesia Electronic Health Record.

BACKGROUND: Visual analytics is the science of analytical reasoning supported by interactive visual interfaces called dashboards. In this report, we describe our experience addressing the challenges in visual analytics of anesthesia electronic health record (EHR) data using a commercially available business intelligence (BI) platform. As a primary outcome, we discuss some performance metrics of the dashboards, and as a secondary outcome, we outline some operational enhancements and financial savings associated with deploying the dashboards. METHODS: Data were transferred from the EHR to our departmental servers using several parallel processes. A custom structured query language (SQL) query was written to extract the relevant data fields and to clean the data. Tableau was used to design multiple dashboards for clinical operation, performance improvement, and business management. RESULTS: Before deployment of the dashboards, detailed case counts and attributions were available for the operating rooms (ORs) from perioperative services; however, the same level of detail was not available for non-OR locations. Deployment of the yearly case count dashboards provided near-real-time case count information from both central and non-OR locations among multiple campuses, which was not previously available. The visual presentation of monthly data for each year allowed us to recognize seasonality in case volumes and adjust our supply chain to prevent shortages. The dashboards highlighted the systemwide volume of cases in our endoscopy suites, which allowed us to target these supplies for pricing negotiations, with an estimated annual cost savings of $250,000. Our central venous pressure (CVP) dashboard enabled us to provide individual practitioner feedback, thus increasing our monthly CVP checklist compliance from approximately 92% to 99%. CONCLUSIONS: The customization and visualization of EHR data are both possible and worthwhile for the leveraging of information into easily comprehensible and actionable data for the improvement of health care provision and practice management. Limitations inherent to EHR data presentation make this customization necessary, and continued open access to the underlying data set is essential.

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.

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.

Clinical Considerations and Outcomes for Spine Surgery Patients with a History of Transplant: A Systematic Scoping Review Protocol.

Spine surgery patients with a history of organ transplantation are a complex population due to their unique anesthetic considerations, immunologic profiles, drug interactions, and potential organ dysfunction. It is common for these patients to develop neck/back pain and pathology that warrants surgical intervention. However, there is a relative dearth of literature examining their outcomes and clinical considerations. The purpose of this protocol is to investigate their clinical outcomes following spine surgery and medical management. We perform a systematic literature search using PRISMA-ScR guidelines to include case reports, cohort studies, and retrospective analyses. The search terms include kidney, liver, heart, pancreas, lung, and bone marrow for transplants of interest and contain an extensive list of terms covering spine surgery. The search is conducted using Medline, Embase, and the Cochrane Central Register of Controlled Trials. A thorough examination of titles and abstracts is performed followed by data extraction. The data points include patient demographics, past medical history, spine procedural information, and clinical outcomes. This systematic review will aid clinicians in identifying demographics, medical management, and clinical outcomes for spine surgery patients with a previous organ transplant. These findings will highlight the gaps in the knowledge of this complex population and stimulate further research.

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.

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.

Incidence and Severity of Concussions Among Young Soccer Players Based on Age, Sex, and Player Position.

BACKGROUND: Previously studied risk factors for sports-related concussion in soccer players include sex, age, and player position. However, prior studies were limited in number, they reported conflicting results, and most did not assess initial concussion severity. PURPOSE/HYPOTHESIS: The purpose of this study was to conduct an in-depth analysis of soccer players across key demographic groups (sex, age, position) for both concussion incidence and severity. It was hypothesized that concussion incidence and severity would be higher among male players, players aged ≥17 years, and goalkeepers. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: The authors analyzed baseline and postinjury ImPACT (Immediate Post-Concussion Assessment and Cognitive Testing) scores for athletes aged 12 to 22 years between July 2009 and June 2019. Players were assigned to an age group based on when they had their most recent baseline test. Concussion incidence and concussion severity index were compared using t tests and multivariate logistic regression. RESULTS: For 1189 individuals who reported soccer as their primary sport, 1032 contributed 1754 baseline ImPACT tests (some individuals had multiple baseline tests), whereas 445 individuals were suspected of sustaining a concussion and then referred for a postinjury 1 test. Of these players, 254 (24.6%) had both a baseline and a postinjury test and were analyzed for concussion severity. Linear regression showed that forwards had a lower incidence of ImPACT-proxied concussions than goalkeepers had (P = .008). Female players had a significantly higher incidence of ImPACT concussions compared with male players (mean, 0.07 [female] vs 0.04 [male] concussions per person-year; P = .05). Players in the ≥17-year age group had a higher incidence of ImPACT concussions than players in the 15- to 16-year age group (P = .04), although the 15- to 16-year age group had more severe concussions than the ≥17-year age group (mean severity index, 2.91 [age 15-16 years] vs 1.73 [age ≥17 years]; P = .001). CONCLUSION: Female soccer players experienced a higher incidence of concussion than did male players, and goalkeepers experienced a greater incidence of ImPACT concussions than did forwards. Players of both sexes and all positions in the 15- to 16-year age group showed increased initial concussion severity compared with the ≥17-year age group, despite a lower comparative incidence of ImPACT concussions. Further study is needed to understand whether sex and player position affect concussion severity.

Clinical Trials in Spinal Tumors: A Two-Decade Review.

OBJECTIVE: Clinical trials are essential for assessing the advancements in spine tumor therapeutics. The purpose of the present study was to characterize the trends in clinical trials for primary and metastatic tumor treatment during the past 2 decades. METHODS: The ClinicalTrials.gov database was queried using the search term "spine" for all interventional studies from 1999 to 2020 with the categories of "cancer," "neoplasm," "tumor," and/or "metastasis." The tumor type, phase data, enrollment numbers, and home institution country were recorded. The sponsor was categorized as an academic institution, industry, government, or other and the intervention type as procedure, drug, device, radiation therapy, or other. The frequency of each category and the cumulative frequency during the 20-year period were calculated. RESULTS: A total of 106 registered trials for spine tumors were listed. All, except for 2, that had begun before 2008 had been completed. An enrollment of 51-100 participants (29.8%) was the most common, and most were phase II studies (54.4%). Most of the studies had examined metastatic tumors (58.5%), and the number of new trials annually had increased 3.4-fold from 2009 to 2020. Most of the studies had been conducted in the United States (56.4%). The most common intervention strategy was radiation therapy (32.1%), although from 2010 to 2020, procedural studies had become the most frequent (2.4/year). Most of the studies had been sponsored by academic institutions (63.2%), which during the 20-year period had sponsored 3.2-fold more studies compared with the industry partners. CONCLUSIONS: The number of clinical trials for spine tumor therapies has rapidly increased during the past 15 years, owing to studies at U.S. academic medical institutions investigating radiosurgery for the treatment of metastases. Targeted therapies for tumor subtypes and sequelae have updated international best practices.

The Impact of American Society of Anesthesiologists Status on Cost of Care and Length of Stay Following Posterior Cervical Decompression and Fusion.

BACKGROUND: Increased posterior cervical decompression and fusion (PCDF) procedures over the past decade have raised the prospect of bundled payment plans. The American Society of Anesthesiologists (ASA) Physical Status Classification system may enable accurate estimation of health care costs, length of stay (LOS), and other postoperative outcomes in patients undergoing PCDF. METHODS: Low (I and II) versus high (III and IV) ASA class was used to evaluate 971 patients who underwent PCDF between 2008 and 2016 at a single institution. Demographics were compared using univariate analysis. Cost of care, LOS, and postoperative complications were compared using multivariable logistic and linear regression, controlling for sex, age, length of surgery, and number of segments fused. RESULTS: The high ASA class cohort was older (mean age 62 years vs. 55 years, P < 0.0001) and had higher Elixhauser comorbidity index scores (P < 0.0001). ASA class was independently associated with longer LOS (2.1 days, 95% confidence interval [CI] 1.3-2.9, P < 0.0001) and higher cost ($2936, 95% CI $1457-$4415, P < 0.0001). Patients with high ASA class were more likely to have a nonhome discharge (3.9, 95% CI 2.8-5.6, P < 0.0001), delayed extubation (3.2, 95% CI 1.4-7.3, P = 0.006), intensive care unit stay (2.4, 95% CI 1.5 3.7, P = 0.0001), in-hospital complications (1.5, 95% CI 1.0-2.2, P = 0.03), and 30-day (3.2, 95% CI 1.5-6.8, P = 0.003) and 90-day (3.2, 95% CI 1.8-5.7, P = 0.0001) readmission. CONCLUSIONS: High ASA class is strongly associated with increased costs, LOS, and adverse outcomes following PCDF and could be useful for preoperative prediction of these outcomes.

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.