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Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and internal fixation (ORIF). Several patient-related risk factors may contribute to poor short-term outcomes, and machine learning may be a valuable tool for predicting outcomes. The objective of this study was to evaluate machine-learning algorithms for accurately predicting short-term outcomes after ORIF for ankle fractures. The Nationwide Inpatient Sample and Nationwide Readmissions Database were queried for adult patients ≥18 years old who underwent ORIF of an ankle fracture during 2013 or 2014. Morbidity and mortality, length of stay >3 days, and 30-day all-cause readmission were the outcomes of interest. Two machine-learning models were created to identify patient and hospital characteristics associated with the 3 outcomes. The machine learning models were evaluated using confusion matrices and receiver operating characteristic area under the curve values. A total of 16,501 cases were drawn from the Nationwide Inpatient Sample and used to assess morbidity and mortality and length of stay >3 days, and 33,504 cases were drawn from the Nationwide Readmissions Database to assess 30-day readmission. Older age, Medicaid, Medicare, deficiency anemia, congestive heart failure, chronic lung disease, diabetes, hypertension, and renal failure were the variables associated with a statistically significant increased risk of developing all 3 adverse events. Logistic regression and gradient boosting had similar area under the curve values for each outcome, but gradient boosting was more accurate and more specific for predicting each outcome. Our results suggest that several comorbidities may be associated with adverse short-term outcomes after ORIF of ankle fractures, and that machine learning can accurately predict these outcomes., (Copyright © 2018 the American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.)

Rigid flatfoot deformity is a debilitating condition that can be managed by triple arthrodesis surgery. Triple arthrodesis has the potential to restore health-related quality of life, but it is also associated with several complications. Few studies have examined the 30-day readmission rates after triple arthrodesis. The objective of this study was to investigate risk factors for 30-day all-cause readmissions after triple arthrodesis. The nationwide readmission database was queried from 2013. By using International Classification of Disease, Ninth Revision, procedure codes, all triple arthrodesis procedures were identified. Demographic factors, comorbidities, insurance status, and hospital characteristics were statistically compared between patients who experienced a 30-day readmission and those who did not. Multivariable logistic regression was used to identify independent risk factors for 30-day readmission. Overall, 1916 triple arthrodesis cases were identified. The overall 30-day readmission rate after triple arthrodesis was 4.6%. Univariate analysis revealed a statistically higher proportion of patients with electrolyte abnormalities (13.8% vs 4.6%; p < .01) in the patients who were readmitted within 30 days compared with those who were not. Multivariable analysis demonstrated Medicaid insurance, relative to private insurance, as the only statistically significant predictor of 30-day readmission with an odds ratio of 4.43 (p < .05). These results suggest that patients of lower socioeconomic status may be at a greater risk for development of a short-term readmission after triple arthrodesis surgery. These findings are important for surgeon and patient communication, counseling, and postoperative care when choosing to pursue triple arthrodesis surgery., (Copyright © 2018. Published by Elsevier Inc.)