The Olerud Extensile Anterior Approach for Complex Distal Femoral Fractures: A Systematic Review.

The management of AO/OTA (AO Foundation/Orthopaedic Trauma Association) 33-C3 complex distal femoral fractures presents challenges for orthopaedic surgeons. The extensile anterior approach may be an appropriate alternative treatment technique. The purpose of this systematic review is to (1) evaluate the Olerud approach (extensile anterior approach) for the treatment of 33-C3 complex distal femoral fractures and (2) evaluate postoperative patient outcomes. A systematic literature search was performed to retrieve studies that evaluated the treatment of 33-C3 complex distal femoral fractures. The PubMed database query resulted in 429 studies. Two reviewers independently reviewed the studies, of which six were identified. Patient demographics, AO/OTA fracture, surgical intervention, follow-up duration, clinical outcomes, and postoperative complications were collected and analyzed. The systematic review included six studies reporting on 85 patients (43 males and 42 females); the mean patient age was 45 years (range: 16-101 years). The most common cause of injury was road/traffic accident (51 patients). The mean follow-up time was 26 months (range: 3-72 months). In five studies, the mean time for union was 5.8 months (range: 2-9 months). The three most commonly reported complications were infection (seven [8.2%] patients), failure/malunion (three [3.5%] patients), and delayed tibial tuberosity osteotomy healing (three [3.5%] patients). Seventy (82.4%) patients did not suffer any postoperative complications. Out of 72 patients, 7 (9.7%) had resultant varus/valgus deformity. Overall, 57.1% had excellent/good functional outcomes. Based on the systematic literature review, the extensile anterior approach may be a viable alternative surgical option for 33-C3 complex distal femoral fractures. Given the current literature, more comprehensive and extensive studies need to be performed to ensure the best possible outcome.

Evaluation of the volume-value relationship in hip fracture care using evidence-based thresholds.

BACKGROUND: Studies have shown high-volume surgeons and hospitals deliver higher value care. The aims of this study were to establish meaningful thresholds defining high-volume surgeons and hospitals performing hip fracture surgery and to examine the relative market share of hip fracture cases using these surgeon and hospital strata. METHODS: We performed a retrospective cohort study in a database of 103,935 patients undergoing hip fracture repair. We generated stratum-specific likelihood ratio (SSLR) models of a receiver operating characteristic (ROC) curve using length of stay (LOS) and cost value metrics. Volume thresholds predictive of decreased LOS and costs for surgeons and hospitals were identified. RESULTS: Analysis of annual surgeon hip fracture volume produced two volume categories for LOS and cost: 0-30 (low) and 31+ (high). Analysis of LOS by annual hospital hip fracture volume produced strata at: 0-59 (low), 60-146 (medium), and 147 or more (high). Analysis of cost by annual hospital volume produced strata at: 0-125 (low) and 126+ (high). LOS and cost both decreased significantly (p < 0.05) in progressively higher volume categories. Low-volume surgeons performed the majority of hip fracture cases, although they were performed at medium- or high-volume centres. CONCLUSIONS: This study demonstrates a direct relationship between volume and value, translating to improvement in hip fracture care delivery for both surgeons and hospitals. Higher volume hospitals while lower volume surgeons perform the majority of hip fracture cases, suggesting optimisation opportunities. However, systems-based practices at the hospital level likely drive value to a greater extent than individual surgeons.

Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.

OBJECTIVES: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level factors that may not be optimized preoperatively due to acuity of care. The objectives of this study are to (1) develop a supervised naive Bayes machine-learning algorithm using preoperative patient data to predict length of stay and cost after hip fracture and (2) propose a patient-specific payment model to project reimbursements based on patient comorbidities. METHODS: Using the New York Statewide Planning and Research Cooperative System database, we studied 98,562 Medicare patients who underwent operative management for hip fracture from 2009 to 2016. A naive Bayes machine-learning model was built using age, sex, ethnicity, race, type of admission, risk of mortality, and severity of illness as predictive inputs. RESULTS: Accuracy was demonstrated at 76.5% and 79.0% for length of stay and cost, respectively. Performance was 88% for length of stay and 89% for cost. Model error analysis showed increasing model error with increasing risk of mortality, which thus increased the risk-adjusted payment for each risk of mortality. CONCLUSIONS: Our naive Bayes machine-learning algorithm provided excellent accuracy and responsiveness in the prediction of length of stay and cost of an episode of care for hip fracture using preoperative variables. This model demonstrates that the cost of delivery of hip fracture care is dependent on largely nonmodifiable patient-specific factors, likely making bundled care an implausible payment model for hip fractures.