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Aims: To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods: Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results: Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion: The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407.

Modular components allow for the precise adjustment of sizing and balancing in knee replacement and are widely used in revision total knee arthroplasty. While they have a significant advantage over monoblock implants, these components may be associated with fretting and corrosion at modular junctions. We report the case of a fracture of a morse taper adapter bolt in a 65-year-old female with a history of multiple revision knee arthroplasties. Only a few cases of fracture of the taper adapter bolt have been previously reported. We reinforce 2 learning points in this report: the utility of magnetic resonance imaging as an aid in diagnosing total knee failure when initial radiographs are unremarkable and the use of techniques such as anterior quadrangular femoral osteotomy when an implant is unable to be removed via conventional techniques.

Introduction The majority of total hip arthroplasty (THA) patients are discharged home postoperatively, however, many still require continued medical care. We aimed to identify important characteristics that predict nonhome discharge in geriatric patients undergoing THA using machine learning. We hypothesize that our analyses will identify variables associated with decreased functional status and overall health to be predictive of non-home discharge. Materials and Methods Elective, unilateral, THA patients above 65 years of age were isolated in the NSQIP database from 2018-2020. Demographic, pre-operative, and intraoperative variables were analyzed. After splitting the data into training (75%) and validation (25%) data sets, various machine learning models were used to predict non-home discharge. The model with the best area under the curve (AUC) was further assessed to identify the most important variables. Results In total, 19,840 geriatric patients undergoing THA were included in the final analyses, of which 5194 (26.2%) were discharged to a non-home setting. The RF model performed the best and identified age above 78 years (OR: 1.08 [1.07, 1.09], P < .0001), as the most important variable when predicting non-home discharge in geriatric patients with THA, followed by severe American Society of Anesthesiologists grade (OR: 1.94 [1.80, 2.10], P < .0001), operation time (OR: 1.01 [1.00, 1.02], P < .0001), anemia (OR: 2.20 [1.87, 2.58], P < .0001), and general anesthesia (OR: 1.64 [1.52, 1.79], P < .0001). Each of these variables was also significant in MLR analysis. The RF model displayed good discrimination with AUC = .831. Discussion The RF model revealed clinically important variables for assessing discharge disposition in geriatric patients undergoing THA, with the five most important factors being older age, severe ASA grade, longer operation time, anemia, and general anesthesia. Conclusions With the rising emphasis on patient-centered care, incorporating models such as these may allow for preoperative risk factor mitigation and reductions in healthcare expenditure.

Category: Trauma; Ankle Introduction/Purpose: Pilon fractures usually result from high energy mechanisms and are associated with signifcant soft tissue injuries in addition to complex fracture patterns. Treatment typically involves staged management with initial placement of an external fixator and subsequent definitive internal fixation once soft tissues are amenable. The purpose of this study was to compare the risk of fracture-related infection (FRI) and unplanned re-operation after staged open reduction and internal fixation (ORIF) of pilon fractures between cases in which elements of the temporizing external fixator (ex-fix) were prepped into the surgical field and cases in which it was completely removed prior to definitive fixation. Methods: This was a multi-surgeon retrospective comparative cohort study on patients 18 years and older with pilon fractures who underwent operative treatment over a ten-year period between January 1, 2010 to January 1, 2020 at an academic-level 1 trauma center. Medical records and radiographic images were reviewed for each patient to assess demographics, clinical characteristics and surgical outcomes. The primary outcome measures were FRI and unplanned re-operation, including arthrodesis and amputation. All analyses were completed using JMP Software (Version 15.0, SAS Institute Inc., Cary, NC), and an alpha of 0.05 was used for statistical significance. Results: 133 patients underwent staged ORIF, 47 with retained elements of the ex-fix prepped in situ for definitive ORIF and 86 with the ex-fix entirely removed. The overall rate of FRI was 23.3% while the overall rate of unplanned re-operation was 11.3% (4.5% rate of arthrodesis and 6.8% rate of amputation). There was no difference in FRI between the group in which ex-fix was prepped in versus the group in which it was completely removed (23.4% vs 23.3%, p=0.985) and no difference in overall rate of unplanned re-operation (10.6% vs 11.6%, p=0.863). Assessing the subset of patients with FRI, those with ex-fix prepped in had a higher prevalence of infection with MRSA and MSSA (81.8% vs 40%, p=0.021). Using alpha=0.05, and power=0.8, a sample size of 42 patients with FRI post-ORIF (27 with ex-fix prepped in, and 15 with ex-fix removed) would be needed to detect a difference in MRSA/MSSA bacteriology. Conclusion: Although there were relatively high complication rates in this cohort of pilon fractures treated with staged ORIF, prepping in elements of the external fixator did not lead to a significant increase in rates of fracture-related infection nor unplanned re-operation, including arthrodesis and amputation. This study offers clinical support regarding a common practice used to assist in reduction and definitive fixation of distal tibial pilon fractures.