A predictive algorithm for perioperative complications and readmission after ankle arthrodesis.

PURPOSE: Ankle arthrodesis is a mainstay of surgical management for ankle arthritis. Accurately risk-stratifying patients who undergo ankle arthrodesis would be of great utility. There is a paucity of accurate prediction models that can be used to pre-operatively risk-stratify patients for ankle arthrodesis. We aim to develop a predictive model for major perioperative complication or readmission after ankle arthrodesis. METHODS: This is a retrospective cohort study of adult patients who underwent ankle arthrodesis at any non-federal California hospital between 2015 and 2017. The primary outcome is readmission within 30 days or major perioperative complication. We build logistic regression and ML models spanning different classes of modeling approaches, assessing discrimination and calibration. We also rank the contribution of the included variables to model performance for prediction of adverse outcomes. RESULTS: A total of 1084 patients met inclusion criteria for this study. There were 131 patients with major complication or readmission (12.1%). The XGBoost algorithm demonstrates the highest discrimination with an area under the receiver operating characteristic curve of 0.707 and is well-calibrated. The features most important for prediction of adverse outcomes for the XGBoost model include: diabetes, peripheral vascular disease, teaching hospital status, morbid obesity, history of musculoskeletal infection, history of hip fracture, renal failure, implant complication, history of major fracture. CONCLUSION: We report a well-calibrated algorithm for prediction of major perioperative complications and 30-day readmission after ankle arthrodesis. This tool may help accurately risk-stratify patients and decrease likelihood of major complications.

Predictive Modeling for Spinal Metastatic Disease.

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians' ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow.

Fracture Lines and Patterns in Intra-articular Distal Femur Fractures.

INTRODUCTION: Distal femur fractures are complex injuries that often present with multiple fragments, posing notable challenges to fixation. This study aimed to (1) use preoperative CT scans to graphically display fracture lines in intra-articular distal femur fractures and (2) identify common fracture patterns in these injuries. METHODS: All skeletally mature patients that underwent surgical fixation of Orthopaedic Trauma Association type 33C distal femur fractures between 2012 and 2022 were identified across two level 1 trauma centers (n = 63). Preoperative axial, sagittal, and coronal computed tomography scans were obtained. Fracture lines in each plane were traced out and superimposed on standardized distal femur cross-sections, generating a fracture map for each plane. Injury and fracture characteristics were summarized and compared between fracture patterns. RESULTS: On axial scans, 59 of 63 fractures contained a central intercondylar split from the intercondylar notch to the trochlea. On coronal scans, fracture lines originated at the notch and exited laterally and medially in the supracondylar region, creating a Y-shape. One-third of all fractures contained coronal fracture lines, with most involving the lateral condyle. Based on fracture line orientation and location, fractures were divided into four main fracture pattern types. Type 4 fractures (central split and medial coronal fracture line) were associated with lower average medial fracture height and a lower rate of medial metaphyseal comminution. DISCUSSION: We found that C-type distal femur fractures can present with four main fracture patterns. Most fractures contain a central sagittal intercondylar split, and a high proportion of fractures contain either medial or lateral coronal fracture lines. Fracture pattern was associated with mechanism of injury, presence of medial comminution, and medial fracture line height. Future studies should focus on clinical outcomes and surgical management of these distinct fracture patterns. LEVEL OF EVIDENCE: IV.

Similar Medium-Term Revision Rates Following Acute Total Hip Arthroplasty Versus Open Reduction and Internal Fixation for Acetabular Fractures in the Elderly.

BACKGROUND: The management of elderly acetabular fractures is complex, with high rates of conversion total hip arthroplasty (THA) after open reduction and internal fixation (ORIF), but potentially higher rates of complications after acute THA. METHODS: The California Office of Statewide Health Planning and Development database was queried between 2010 and 2017 for all patients aged 60 years or older who sustained a closed, isolated acetabular fracture and underwent ORIF, THA, or a combination. Chi-square tests and Student t tests were used to identify demographic differences between groups. Multivariate regression was used to evaluate predictors of 30-day readmission and 90-day complications. Kaplan-Meier (KM) survival analysis and Cox proportional hazards model were used to estimate the revision surgery-free survival (revision-free survival [RFS]), with revision surgery defined as conversion THA, revision ORIF, or revision THA. RESULTS: A total of 2,184 surgically managed acetabular fractures in elderly patients were identified, with 1,637 (75.0%) undergoing ORIF and 547 (25.0%) undergoing THA with or without ORIF. Median follow-up was 295 days (interquartile range, 13 to 1720 days). 99.4% of revisions following ORIF were for conversion arthroplasty. Unadjusted KM analysis showed no difference in RFS between ORIF and THA (log-rank test P = 0.27). RFS for ORIF patients was 95.1%, 85.8%, 78.3%, and 71.4% at 6, 12, 24 and 60 months, respectively. RFS for THA patients was 91.6%, 88.9%, 87.2%, and 78.8% at 6, 12, 24 and 60 months, respectively. Roughly 50% of revisions occurred within the first year postoperatively (49% for ORIF, 52% for THA). In propensity score-matched analysis, there was no difference between RFS on KM analysis ( P = 0.22). CONCLUSIONS: No difference was observed in medium-term RFS between acute THA and ORIF for elderly acetabular fractures in California. Revision surgeries for either conversion or revision THA were relatively common in both groups, with roughly half of all revisions occurring within the first year postoperatively. LEVEL OF EVIDENCE: III.