Severe Obesity Increases Risk of Infection After Revision Total Hip Arthroplasty

BackgroundThe increasing prevalence of obesity has resulted in an increased number of revision total hip arthroplasties (rTHAs) performed in patients with a high body mass index (BMI). The aim of this study is to evaluate whether obesity negatively affects (1) complication rate, (2) reoperation and revision rate, and (3) patient-reported outcome in rTHA.MethodsIn this registry-based study, we prospectively followed 444 rTHAs (cup: n = 265, stem: n = 57, both: n = 122) performed in a specialized high-volume orthopedic center between 2013 and 2015. The number of complications, and reoperation and revision surgery was registered until 5 years postoperatively. Oxford Hip Score (OHS) was evaluated preoperatively, and at 1 and 2 years postoperatively. Patients were categorized based on BMI to nonobese (<30 kg/m², n = 328), obese (30-35 kg/m², n = 82), and severe obese (≥35 kg/m², n = 34).ResultsSevere obese patients, but not obese patients, had higher risks of complications and re-revision than nonobese patients. In particular, the risk of infection following rTHA was higher in severe obese patients (24%) compared to nonobese patients (3%; relative risk, 7.7). Severe obese patients had overall poorer OHS than nonobese patients, but improvement in OHS did not differ between severe obese and nonobese patients. No differences between obese and nonobese groups on OHS were observed.ConclusionIn our study, severe obesity was associated with an increased risk of infection following rTHA. Patients with high BMI should be counseled appropriately before surgery.     

Personalized treatment selection using data from crossover designs with carry-over effects

In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest single-index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single-index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.