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.

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.

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.

Intraoperative Electronic Alerts Improve Compliance With National Quality Program Measure for Perioperative Temperature Management.

BACKGROUND: Reimbursement for anesthesia services has been shifting from a fee-for-service model to a value-based model that ties payment to quality metrics. The Centers for Medicare & Medicaid Service's (CMS) value-based payment program includes a quality measure for perioperative temperature management (Measure #424, Perioperative Temperature Management). Compliance may impose new challenges in clinical practice, data collection, and reporting. We investigated the impact of an electronic decision-support tool on adherence to this emerging standard. METHODS: In this retrospective observational study, perioperative temperature data were collected from cases eligible for reporting this measure to CMS from a single academic medical center before and after the implementation of an electronic decision-support tool that prompted temperature measurement and maintenance of normothermia. Proportions of measure compliance were assessed using segmented regression analysis. Proportions of intraoperative temperature measurement were also assessed, and multivariable logistic regression was performed to assess the association between patient and surgical factors and measure compliance. RESULTS: A total of 24,755 cases eligible for reporting in 2017 were assessed, and 25,274 cases from 2016 were included as an extended baseline. Segmented time-series regression did not show a significant baseline trend in measure compliance. Introduction of the alerts was associated with an increase in overall compliance from 84.4% (95% confidence interval [CI], 83.6%-85.2%) to 92.4% (91.4%-93.4%), and an increase in intraoperative compliance from 26.8% (25.8%-27.8%) to 71.0% (69.6%-72.4%). The association between the alerts and overall compliance was also present on multivariable analysis. CONCLUSIONS: Implementation of an intraoperative decision-support tool was associated with statistically significant improvement in the maintenance of normothermia in cases eligible for reporting to CMS. This led to improved compliance with Measure #424 and suggests that electronic alerts can help practices improve their performance and payment bonus eligibility.

Anesthetic considerations for a novel anterior surgical approach to pediatric scoliosis correction.

BACKGROUND: Idiopathic scoliosis is a condition that may require surgical correction. Limitations of previous surgical modalities, however, created the need for novel methods of repair. One such technique, a newer form of anterolateral scoliosis correction, has shown considerable promise, which our center has had substantial experience performing. AIM: In this article, we present the case details of our first 105 patients for the purposes of describing the evolution and details of the anesthetic management and considerations for this procedure. METHODS: A retrospective review of medical records for 105 patients undergoing anterolateral instrumentation procedure for idiopathic scoliosis correction done at a single institution from May 2014 to June 2016 was performed. The details of perioperative management as well as surgical technique were reported for all patients. RESULTS: The mean age for patients was 14.8 years (range 10-18); the mean weight was 49.9 kg (range 25-82). Unilateral procedures were performed on 46.7%, with bilateral and hybrid procedures performed on 50.5% and 4.7%, respectively. The median number of levels corrected was 8 (interquartile range [IQR] 7-9) for unilateral, right 7 (IQR 6-7) and left 5 (IQR 4-5) for bilateral, and 4 (IQR 4-4.5) for hybrids. The average estimated blood loss (EBL) was 310 mL±138, with cell salvaged blood transfused in 61% of patients, and allogenic blood transfusion required in only two patients. CONCLUSIONS: The described anesthetic and analgesic management provides a framework for delivering perioperative care for this challenging procedure, which is gaining popularity as a modality for scoliosis correction.

A Retrospective Study of Success, Failure, and Time Needed to Perform Awake Intubation.

BACKGROUND: Awake intubation is the standard of care for management of the anticipated difficult airway. The performance of awake intubation may be perceived as complex and time-consuming, potentially leading clinicians to avoid this technique of airway management. This retrospective review of awake intubations at a large academic medical center was performed to determine the average time taken to perform awake intubation, its effects on hemodynamics, and the incidence and characteristics of complications and failure. METHODS: Anesthetic records from 2007 to 2014 were queried for the performance of an awake intubation. Of the 1,085 awake intubations included for analysis, 1,055 involved the use of a flexible bronchoscope. Each awake intubation case was propensity matched with two controls (1:2 ratio), with similar comorbidities and intubations performed after the induction of anesthesia (n = 2,170). The time from entry into the operating room until intubation was compared between groups. The anesthetic records of all patients undergoing awake intubation were also reviewed for failure and complications. RESULTS: The median time to intubation for patients intubated post induction was 16.0 min (interquartile range: 13 to 22) from entrance into the operating room. The median time to intubation for awake patients was 24.0 min (interquartile range: 19 to 31). The complication rate was 1.6% (17 of 1,085 cases). The most frequent complications observed were mucous plug, endotracheal tube cuff leak, and inadvertent extubation. The failure rate for attempted awake intubation was 1% (n = 10). CONCLUSIONS: Awake intubations have a high rate of success and low rate of serious complications and failure. Awake intubations can be performed safely and rapidly.

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.

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.

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.

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.

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.

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.

Anesthesia provider performance in the first two years of merit-based incentive payment system: Shifts in reporting and predictors of receiving bonus payments.

STUDY OBJECTIVE: The Merit-Based Incentive Payment System (MIPS) program was intended to align CMS quality and incentive programs. To date, no reports have described anesthesia clinician performance in the first two years of the program. DESIGN: Observational retrospective cohort study. SETTING: Centers for Medicare and Medicaid Services public datasets for their Quality Payment Program. PATIENTS: Anesthesia clinicians who participated in MIPS for 2017 and 2018 performance years. INTERVENTIONS: Descriptive statistics compared anesthesia clinician characteristics, practice setting, and MIPS performance between the two years to determine associations with MIPS-based payment adjustments. MEASUREMENTS: Logistic regression identified independent predictors of bonus payments for exceptional performance. MAIN RESULTS: Compared with participants in 2017 (n = 25,604), participants in 2018 (n = 54,381) had a higher proportion of reporting through groups and alternative payment models (APMs) than as individuals (p < 0.001). The proportion of clinicians earning performance bonuses increased from 2017 to 2018 except for those MIPS participants reporting as individuals. Median total MIPS scores were higher in 2018 than 2017 (84.6 vs. 82.4, p < 0.001), although median total scores fell for participants reporting as individuals (40.9 vs 75.5, p < 0.001). Among clinicians with scores in both years (n = 20,490), 10,559 (51.3%) improved their total score between 2017 and 2018, and 347 (1.7%) changed reporting from individual to APM. Reporting as an individual compared with group reporting (OR: 0.75; 95% CI: 0.71 to 0.80; p < 0.001) was associated with lower rates of bonus payments, as was having a greater proportion of patients dual-eligible for Medicaid and Medicare. Reporting through an APM (OR: 149.6; 95% CI: 110 to 203.4; p < 0.001) and increasing practice group size were associated with higher likelihood of bonus payments. CONCLUSIONS: Anesthesia clinician MIPS participation and performance were strong during 2017 and 2018 performance years. Providers who reported through groups or APMs have a higher likelihood of receiving bonus payments.

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.

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.

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.

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.

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.

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.