Lower Hounsfield Units and Severe Multifidus Sarcopenia Are Independent Predictors of Increased Risk for Proximal Junctional Kyphosis and Failure Following Thoracolumbar Fusion.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: The purpose of the present study was to assess the impact of sarcopenia on the development of proximal junctional kyphosis (PJK) and proximal junctional failure (PJF) following thoracolumbar spine fusion surgery using opportunistic evaluation of paraspinal fatty degeneration on preoperative magnetic resonance imaging. SUMMARY OF BACKGROUND DATA: While paraspinal sarcopenia has been shown to have detrimental consequences following posterior cervicothoracic fusions, the impact of paraspinal sarcopenia on PJK and PJF following thoracolumbar spine fusion surgery remains unknown. MATERIALS AND METHODS: We performed a retrospective review of patients who underwent posterior spine fusion surgery that extended caudally to the pelvis and terminated cranially between T10 and L2 between 2010 and 2017. The cohort was divided into three groups: (1) patients without PJK or PJF, (2) patients with PJK but no PJF, and (3) patients with PJF. Univariate and multivariate analyses were performed to determine risk factors for the development of proximal junctional complications. RESULTS: We identified 150 patients for inclusion in this study. Mean Hounsfield Units at the upper instrumented vertebra (UIV) was 148.3±34.5 in the cohort of patients without PJK or PJF, which was substantially higher than values recorded in the PJK (117.8±41.9) and PJF (118.8±41.8) subgroups (P<0.001). Severe multifidus sarcopenia was identified at a much higher rate in the subgroups of patients who developed PJK (76.0%) and PJF (78.9%) than in the subgroup of patients who developed neither PJK nor PJF (34.0%; P<0.001). Multivariate analysis demonstrated both low HU at the UIV and moderate-severe multifidus sarcopenia to be risk factors for the development of PJK and PJF. CONCLUSION: The results of this study suggest severe paraspinal sarcopenia and diminished bone density at the UIV impart an increased risk of developing PJK and PJF, while markers of systemic frailty such as modified Frailty Index and Charlson Comorbidity Index are not associated with an increased risk of these complications. LEVEL OF EVIDENCE: III.

Identifying modifiable and nonmodifiable cost drivers of ambulatory rotator cuff repair: a machine learning analysis.

INTRODUCTION: Implementing novel tools that identify contributors to the cost of orthopedic procedures can help hospitals maximize efficiency, minimize waste, improve surgical decision-making, and practice value-based care. The purpose of this study was to develop and internally validate a machine learning algorithm to identify key drivers of total charges after ambulatory arthroscopic rotator cuff repair and compare its performance with a state-of-the-art statistical learning model. METHODS: A retrospective review of the New York State Ambulatory Surgery and Services Database was performed to identify patients who underwent elective outpatient rotator cuff repair (RCR) from 2015 to 2016. Initial models were constructed using patient characteristics (age, gender, insurance status, patient income, Elixhauser Comorbidity Index) as well as intraoperative variables (concomitant procedures and services, operative time). These were subsequently entered into 5 separate machine learning algorithms and a generalized additive model using natural splines. Global variable importance and partial dependence curves were constructed to identify the greatest contributors to cost. RESULTS: A total of 33,976 patients undergoing ambulatory RCR were included. Median total charges after ambulatory RCR were $16,017 (interquartile range: $11,009-$22,510). The ensemble model outperformed the generalized additive model and demonstrated the best performance on internal validation (root mean squared error: $7112, 95% confidence interval: 7036-7188; logarithmic root mean squared error: 0.354, 95% confidence interval: 0.336-0.373, R2: 0.53), and identified major drivers of total charges after RCR as increasing operating room time, patient income level, number of anchors used, use of local infiltration anesthesia/peripheral nerve blocks, non-White race/ethnicity, and concurrent distal clavicle excision. The model was integrated into a web-based open-access application capable of providing individual predictions and explanations on a case-by-case basis. CONCLUSION: This study developed an ensemble supervised machine learning algorithm that outperformed a sophisticated statistical learning model in predicting total charges after ambulatory RCR. Important contributors to total charges included operating room time, duration of care, number of anchors used, type of anesthesia, concomitant distal clavicle excision, community characteristics, and patient demographic factors. Generation of a patient-specific payment schedule based on the Agency for Healthcare Research and Quality risk of mortality highlighted the financial risk assumed by physicians in flat episodic reimbursement schedules given variable patient comorbidities and the importance of an accurate prediction algorithm to appropriately reward high-value care at low costs.