Objective: To determine patient-specific and injury-specific factors that may predict infection and other adverse clinical results in the setting of tibial pilon fractures.Design: Retrospective chart review.Setting: Level 1 academic trauma center. Patients:Two hundred forty-eight patients who underwent operative treatment for tibial pilon fractures between 2010 and 2020. Intervention: External fixation and/or open reduction and internal fixation.Main Outcome Measurements: Fracture-related infection rates and specific bacteriology, risk factors associated with development of a fracture-related infection, and predictors of adverse clinical results.Results: Two hundred forty-eight patients were enrolled. There was an infection rate of 21%. The 3 most common pathogens cultured were methicillin-resistant Staphylococcus aureus (20.3%), Enterobacter cloacae (16.7%), and methicillin-resistant Staphylococcus aureus (15.5%). There was no significant difference in age, sex, race, body mass index, or smoking status between those who developed an infection and those who did not. Patients with diabetes mellitus (P = 0.0001), open fractures (P = 0.0043), and comminuted fractures (OTA/AO 43C2 and 43C3) (P = 0.0065) were more likely to develop a fracture-related infection. The presence of a polymicrobial infection was positively associated with adverse clinical results (P = 0.006). History of diabetes was also positively associated with adverse results (P = 0.019).Conclusions: History of diabetes and severe fractures, such as those that were open or comminuted fractures, were positively associated with developing a fracture-related infection after the operative fixation of tibial pilon fractures. History of diabetes and presence of a polymicrobial infection were independently associated with adverse clinical results.

AimsTo 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.MethodsData 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.ResultsOf 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.ConclusionThe 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.

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AimsThe aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients.MethodsDemographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset.ResultsA total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752.ConclusionThis study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment.Cite this article: Bone Joint J 2023;105-B(6):702–710.

Introduction:The purpose of this study was to analyze posts shared on social media sites, Twitter and Instagram, referencing scoliosis surgery for tone, content, and perspective of the posts. Methods: Public Twitter and Instagram posts from November 2020 to April 2021 were isolated using the hashtag #ScoliosisSurgery or the words "scoliosis surgery." A total of 5,022 Instagram and 1,414 Twitter posts were collected, of which 500 of each were randomly selected to be analyzed by the authors for the variables previously listed. Results: Of the Instagram posts, 91.8% were associated with an image, and 47.8% were postoperative. 96.9% of the posts had either a positive or neutral tone. 38% delivered a progress update, and 29.9% disseminated education or sought to provide awareness. 48.6% of the posts were from the perspective of the patient. Of the Twitter posts, 60.1% contained only words, and 37.8% were postoperative. 75% of the posts had either a negative or neutral tone. 38.4% described a personal story, and 19.3% provided a progress update. 42.3% of the posts were from the perspective of the patient. Conclusion: Patients reported a positive tone on Instagram, displaying their progress updates and demonstrating contentment with scoliosis surgery, and a negative tone on Twitter, showing discontentment toward inadequate access to surgery. Although both platforms were used to distribute information and provide awareness, only a small percentage of posts were from physicians and hospitals, indicating opportunities for surgeons to use social media to connect with patients.I n recent years, the incidence of scoliosis surgery has increased rapidly and markedly with orthopaedic surgeons more frequently recommending surgical treatment. [1][2][3][4] The benefits of scoliosis surgery, also known as spinal fusion surgery, in certain patient populations are evident. For example, when considering the most common form of pediatric scoliosis, adolescent idiopathic scoliosis, surgery has been shown to provide a better arrest of progression of scoliosis, achieve greater correction of the curves in the Teja Yeramosu, BS

IntroductionSignificant advancements in human immunodeficiency virus (HIV) treatment have led to an increasing life expectancy among patients living with HIV (PLWH). Given this rise in life expectancy, as well as the ability to lead a more active lifestyle, the rate of total joint arthroplasty (TJA) in this population is increasing. Unfortunately, the current medical literature surrounding the safety and efficacy of TJA in this patient population is indeterminant. Therefore, the purpose of this study was to determine if optimization of PLWH prior to TJA would result in any changes in the incidence of postoperative complications and hospital length of stay (LOS) when compared to historically reported data. Materials and methodsA retrospective study was performed of all PLWH 18 years and older who underwent either a primary total knee arthroplasty (TKA) or total hip arthroplasty (THA) between 2009 and 2019 at our academic institution. Medical records were reviewed for each patient to assess demographics, comorbidities, preoperative laboratory studies, operative details, length of hospital stay, complications, and follow-up time. Patients were optimized using our institution's current optimization guidelines: body mass index (BMI) less than 40 kg/m 2 , hemoglobin >12 g/dL, no tobacco use within 30 days of surgery, albumin >3.5 g/dL. Independentsample t-tests and Pearson's chi-square tests were used to evaluate the continuous and categorical variables, respectively. ResultsThis study included 47 TJA in PLWH, including 14 TKA and 33 THA. Out of the 47 patients, 13 (27.7%) were fully optimized for all four variables: BMI, hemoglobin, non-smoking status, and albumin. There was no significant difference between the group of PLWH that was completely optimized and the group that was not in any patient characteristics, preoperative labs, intraoperative variables, or postoperative variables, including length of hospital stay and complications. A larger proportion of patients not completely optimized was found to be active smokers (p=0.0003). All complications occurred in cases in which the patients were not fully optimized. Subgroup analysis of PLWH, who were completely optimized, showed an average LOS of 4.3+/-1.5 days following TKA and 2.9+/-1.1 days following THA. Subgroup analysis of PLWH not completely optimized showed that each case was optimized for at least one variable and that those optimized for albumin had the largest (12.2%) number of complications. ConclusionPLWH can achieve a low rate of complications and LOS similar to that of the general population if medically and nutritionally optimized. Additional research is necessary to reveal well-defined parameters for achieving a higher rate of optimization prior to surgery in this important patient population.

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