Underrepresentation of Non-White Participants in the American Academy of Orthopaedic Surgeons Guidelines for Surgical Management of Knee Osteoarthritis.

BACKGROUND: The aim of this study was to examine the racial and ethnic representation in studies included in the 2015 American Academy of Orthopaedic Surgeons Surgical Management of the Knee Evidence-Based Clinical Practice Guideline relative to their representation of the United States (US). METHODS: The demographic characteristics reported in articles included in the 2015 American Academy of Orthopaedic Surgeons Surgical Management of the Knee Evidence-Based Clinical Practice Guideline were analyzed. The primary outcome of interest was the representation quotient, which is the ratio of the proportion of a racial/ethnic group in the guideline studies relative to their proportion in the US. There were 211 studies included, of which 15 (7%) reported race. There were 35 studies based in the US and 7 of the US-based studies reported race. RESULTS: No US-based studies reported race and ethnicity separately, no studies reported American Indian/Alaska Native participants and no US-based studies reported Asian participants. The representation quotient of US-based studies was 0.66 for Black participants, 0.33 for Hispanic participants, and 1.30 for White participants, which indicates a relative over-representation of White participants compared to national proportions. CONCLUSION: This study illustrated that the evidence base for the surgical management of knee osteoarthritis has been constructed from studies which fail to consider race and ethnicity. Of those US-based studies which do report race or ethnicity, study cohorts do not reflect the US population. These results illustrate a disparity in clinical orthopedic surgical evidence and highlight the need for improved research recruitment strategies.

Relative Value Unit (RVU) and Medicare Severity Diagnosis-related Group (MS-DRG) Reimbursement in Cervical Spinal Fusion: A 2011-2023 Trends Report.

STUDY DESIGN: Level 3 retrospective database study. OBJECTIVE: This study aims to compare work RVU (wRVU), practice expense RVU (peRVU), malpractice RVU (mpRVU), and inflation-adjusted facility price alongside MS-DRG relative weight length of stay (LOS) for cervical spine fusions between 2011 and 2023. SUMMARY OF BACKGROUND DATA: Both RVU and MS-DRG reimbursement have been studied in various surgical subspecialties; however, little investigation has centered on cervical spine fusions. To the best of our knowledge, this is the first study to investigate trends in RVU and MS-DRG reimbursement in cervical spine fusion throughout the COVID-19 pandemic. METHODS: Center for Medicaid and Medicare Services (CMS) physician fee schedule was queried between 2011 and 2023 for RVU and facility reimbursement using common single and multilevel anterior and posterior cervical fusion codes. RVU facility prices were inflation adjusted to 2023. MS-DRG reimbursement data from 2011 to 2022 were compiled for cervical spinal fusion procedures with major complication or comorbidity (MCC) 471, complication or comorbidity (CC) 472, and without CC/MCC 473. Compound annual growth rates (CAGRs), Mean Annual Change, and yearly percent changes were calculated. RESULTS: No changes in wRVU were seen for all cervical CPT codes; however, the CAGR of peRVU (-0.51%±0.60%) and mpRVU (0.69%±0.41%) demonstrated marginal fluctuations. Every CPT code displayed an inflation-adjusted facility price decrease (-2.18%±0.24%). When assessing MS-DRG, there were marginal changes in geometric mean LOS (0.17%±0.45%), arithmetic mean LOS (-0.15%±0.84%), and relative weight (1.09%±0.68%). Unlike RVU reimbursement, the yearly percent change differs between each MS-DRG code. CONCLUSIONS: Inflation-adjusted RVU reimbursement facility prices demonstrated a consistent decrease, while DRG code reimbursement stayed relatively consistent over the study period. This data may help surgeons and hospitals become cognizant of temporal variations in reimbursement patterns as it may affect their personal practice. LEVEL OF EVIDENCE: Level III retrospective study.

Impact of Social Determinants of Health in Spine Surgery.

PURPOSE OF REVIEW: Social determinants of health (SDH) are factors that affect patient health outcomes outside the hospital. SDH are "conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks." Current literature has shown SDH affecting patient reported outcomes in various specialties; however, there is a dearth in research relating spine surgery with SDH. The aim of this review article is to identify connections between SDH and post-operative outcomes in spine surgery. These are important, yet understudied predictors that can impact health outcomes and affect health equity. RECENT FINDINGS: Few studies have shown associations between SDH pillars (environment, race, healthcare, economic, and education) and spine surgery outcomes. The most notable relationships demonstrate increased disability, return to work time, and pain with lower income, education, environmental locations, healthcare status and/or provider. Despite these findings, there remains a significant lack of understanding between SDH and spine surgery. Our manuscript reviews the available literature comparing SDH with various spine conditions and surgeries. We organized our findings into the following narrative themes: 1) education, 2) geography, 3) race, 4) healthcare access, and 5) economics.

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: A retrospective cohort study from a multisite academic medical center. OBJECTIVE: To construct, evaluate, and interpret a series of machine learning models to predict outcomes related to inpatient health care resource utilization for patients undergoing anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: Reducing postoperative health care utilization is an important goal for improving the delivery of surgical care and serves as a metric for quality assessment. Recent data has shown marked hospital resource utilization after ACDF surgery, including readmissions, and ED visits. The burden of postoperative health care use presents a potential application of machine learning techniques, which may be capable of accurately identifying at-risk patients using patient-specific predictors. METHODS: Patients 18-88 years old who underwent ACDF from 2011 to 2021 at a multisite academic center and had preoperative lab values within 3 months of surgery were included. Outcomes analyzed included 90-day readmissions, postoperative length of stay, and nonhome discharge. Four machine learning models-Extreme Gradient Boosted Trees, Balanced Random Forest, Elastic-Net Penalized Logistic Regression, and a Neural Network-were trained and evaluated through the Area Under the Curve estimates. Feature importance scores were computed for the highest-performing model per outcome through model-specific metrics. RESULTS: A total of 1026 cases were included in the analysis cohort. All machine learning models were predictive for outcomes of interest, with the Random Forest algorithm consistently demonstrating the strongest average area under the curve performance, with a peak performance of 0.84 for nonhome discharge. Important features varied per outcome, though age, body mass index, American Society of Anesthesiologists classification >2, and medical comorbidities were highly weighted in the studied outcomes. CONCLUSIONS: Machine learning models were successfully applied and predictive of postoperative health utilization after ACDF. Deployment of these tools can assist clinicians in determining high-risk patients. LEVEL OF EVIDENCE: III.