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Background: End-stage ankle arthritis is a debilitating condition that negatively impacts patient quality of life. Tibiotalar fusion and total ankle replacement are treatment options for managing ankle arthritis. Few studies have examined short term readmission rates of these two procedures. The objective of this study was compare all-cause 30-day readmission rates between patients undergoing tibiotalar fusion vs. total ankle replacement., Methods: This study queried the Nationwide Readmission Database (NRD) from 2013-2014 and used international classification of disease, 9th revision (ICD-9) procedure codes to identify all patients who underwent a tibiotalar fusion or a total ankle replacement. Comorbidities, insurance status, hospital characteristics, and readmission rates were statistically compared between the two cohorts. Risk factors were then identified for 30-day readmission., Results: A total of 5660 patients were analyzed with 2667 in the tibiotalar fusion cohort and 2993 in the total ankle replacement cohort. Univariate analysis revealed that the readmission rate after tibiotalar fusion (4.4%) was statistically greater than after total ankle replacement (1.4%). Multivariable regression analysis indicated that deficiency anemia (OR 2.18), coagulopathy (OR 3.51), renal failure (OR 2.83), other insurance relative to private (OR 3.40), and tibiotalar fusion (OR 2.51) were all statistically significant independent risk factors for having a readmission within 30-days., Conclusions: These findings suggest that during the short-term period following discharge from the hospital, patients who received a tibiotalar fusion are more likely to experience a 30-day readmission. These findings are important for decision making when a surgeon encounters a patient with end stage ankle arthritis., Level of Evidence: Level III, cohort study., (Published by Elsevier Ltd.)

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.)

Individual aspects of heparin or protamine dosing have been better controlled than previously as useful tests have become available. Although many variables including drug potency, drug source, and individual patient response have been separately identified, there has not been an attempt to integrate them into a single management strategy. This study was undertaken to learn whether more precise control of drug variables and patient response would affect blood loss and transfusion requirements. Adult patients having primary cardiac operations were prospectively randomized into two groups. A control group received heparin and protamine by conventional methods. The test group received heparin and protamine according to in vitro predictive tests integrating drugs, tests, and patient response. Supplemental protamine was given in this group only if heparin was specifically found by testing. Anticoagulation in all patients was maintained at an activated coagulation time greater than 400 seconds, and any other treatment for bleeding was at the discretion of the clinical team caring for the patients. Testing and treatment for both groups followed routine practice after patient arrival in the intensive care unit. Test patients received slightly more heparin and a markedly lower dose of protamine than the control patients. Testing identified patients with decreased heparin sensitivity (preoperative heparin therapy) and correctly predicted the effective heparin dose. Supplemental protamine was given twice as often to control patients and frequently when no heparin was detectable (retrospectively). Test patients exhibited less 24-hour chest tube drainage (671 ml versus 1298 ml) and fewer patients received transfusion (9/22 versus 18/24) with fewer donor exposures (22/22 versus 101/24). The management strategy used for heparin and protamine added accuracy and precision, which was associated with improved hemostasis. Although the observation is valid, the mechanism or mechanisms are not completely clear. Nevertheless, it is reasonable to apply basic pharmacologic principles and establishment of consistent, predictable protocols that are beneficial. It is against this background that the efficacy of additional drugs or equipment should be assessed. It is quite possible that only marginal if any improvement in hemostasis may be found in patients having primary, uncomplicated cardiac operation with the addition of more costly drugs or equipment.