Aims We studied the impact of direct anterior (DA) versus non-anterior (NA) surgical approaches on prosthetic joint infection (PJI), and examined the impact of new perioperative protocols on PJI rates following all surgical approaches at a single institution. Patients and Methods A total of 6086 consecutive patients undergoing primary total hip arthroplasty (THA) at a single institution between 2013 and 2016 were retrospectively evaluated. Data obtained from electronic patient medical records included age, sex, body mass index (BMI), medical comorbidities, surgical approach, and presence of deep PJI. There were 3053 male patients (50.1%) and 3033 female patients (49.9%). The mean age and BMI of the entire cohort was 62.7 years (18 to 102, sd 12.3) and 28.8 kg/m2 (13.3 to 57.6, sd 6.1), respectively. Infection rates were calculated yearly for the DA and NA approach groups. Covariates were assessed and used in multivariate analysis to calculate adjusted odds ratios (ORs) for risk of development of PJI with DA compared with NA approaches. In order to determine the effect of adopting a set of infection prevention protocols on PJI, we calculated ORs for PJI comparing patients undergoing THA for two distinct time periods: 2013 to 2014 and 2015 to 2016. These periods corresponded to before and after we implemented a set of perioperative infection protocols. Results There were 1985 patients in the DA group and 4101 patients in the NA group. The overall rate of PJI at our institution during the study period was 0.82% (50/6086) and decreased from 0.96% (12/1245) in 2013 to 0.53% (10/1870) in 2016. There were 24 deep PJIs in the DA group (1.22%) and 26 deep PJIs in the NA group (0.63%; p = 0.023). After multivariate analysis, the DA approach was 2.2 times more likely to result in PJI than the NA approach (OR 2.2 (95% confidence interval 1.1 to 3.9); p = 0.006) for the overall study period. Conclusion We found a higher rate of PJI in DA versus NA approaches. Infection prevention protocols such as use of aspirin, dilute povidone-iodine lavage, vancomycin powder, and Gram-negative coverage may have been positively associated with diminished PJI rates observed for all approaches over time. Cite this article: Bone Joint J 2019;101-B(6 Supple B):2–8.

Objectives: To determine the factors that influence the choice of myomectomy, uterine fibroid embolization (UFE), or hysterectomy in women with symptomatic uterine fibroids and to assess women's perception of body image and sexual functioning before and after treatment. Methods:In this prospective observational cohort pilot study, women scheduled to undergo hysterectomy, myomectomy, or UFE were surveyed before surgery and again at 3 to 6 months after their procedure to assess body image and sexual function using the validated 10-item Body Image Scale and 19-item Female Sexual Function Index. Logistic regression was used to determine predictive factors for surgery choice and paired t tests were used to determine changes in perceived sexual function and body image.Results: Of the 71 women surveyed, 69 underwent their scheduled procedure and 68 participants completed the preoperative questionnaire completely (98%). A total of 33 participants completed the postoperative questionnaire (49%). The predictive factor for hysterectomy versus myomectomy/UFE was older age (odds ratio 1.13, P = 0.017, 95% confidence interval [CI] 1.02-1.24). Regardless of surgery type, there were significant improvements in perceived body image and sexual function, including an increase in the overall sexual satisfaction of the participant (mean difference 0.50, P = 0.021, 95% CI 0.92-0.08) and less likely to feel less sexually attractive because of their menstrual bleeding problem (P < 0.0001, 95% CI 0.637-1.675).Conclusions: Women of older age are more likely to choose hysterectomy over myomectomy or UFE for the treatment of symptomatic uterine fibroids. In addition, women who underwent any treatment for fibroids have increases in perception of body image and sexual functioning after their procedure.

Background: Readmissions to hospitals are common, costly and often preventable, notably readmissions due to infections. A 30-day readmission analysis following hospital discharges, found much of the variation in Medicare spending between hospitals was related to readmissions and skilled nursing facility (SNF) care. Although some readmissions of patients with advanced disease are not preventable, efforts to decrease readmission are most effectively directed towards those patients with intermediate levels of a specific risk. A prediction model to identify patients at highest (or intermediate) risk of infection readmission will help healthcare administrators and providers to allocate appropriate resources. Hospitals should use electronic health record (EHR) data with modern data mining techniques to create more curated, sophisticated models as part of a comprehensive transitional care program. We propose using the risk estimates of a validated prediction model to notify stakeholders and develop readmission rate reports by SNF or discharging physician. Methods: We applied machine learning (ML) methods to predict the risk of 30-day readmission due to sepsis and pneumonia of patients discharged to SNF. We used our EHR data during 2012–2017 to train and data from 2018 to validate. We applied ML algorithms to data including logistic regression, random forest, gradient boosting trees, and support vector machine. Data from EDW and EPIC clarity tables were extracted and managed using SAS Base 9.4 and Enterprise Miner 14.3 (SAS Institute, Cary, NC). We assessed the discrimination and calibration to select the most effective prediction model. Using the resulted risk estimates, we created a notification system and reports for key stakeholders. Results: Figures 1 and 2 show the discrimination and calibration results of the final selected gradient boosting model (GBM). For predicting unplanned readmissions with sepsis and with pneumonia within 30 days after discharge to SNF, the c-statistic for final GBM model with 140 features was 0.69 (95% CI 0.65-0.73) and 73 features was 0.71 (95% CI 0.66-0.75), respectively. Table 1 lists features important to the validation set of the prediction model. We used estimates from these models to develop a daily email notification of patients discharged to SNF stratified into a low, medium, and high risk group for sepsis and pneumonia. We additionally created reports with case-mix adjustments to benchmark SNFs and discharging physicians to monitor and understand performance. Conclusions: Hospitals should leverage the plethora of data found in EHRs to curate readmission prediction models, and promote collaboration among transitional care teams and IPC to ultimately reduce readmissions due to sepsis and pneumonia.Funding: NoneDisclosures: None

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