Patellofemoral joint arthroplasty: patient selection,surgical technique and outcomes

Isolated patellofemoral arthritis is an increasingly recognized entity, and is usually associated with previous patellofemoral dysplasia or instability. Patellofemoral arthroplasty (PFA) has evolved significantly in recent years, both in terms of implant design and importantly in the understanding of appropriate patient selection. This review outlines the indications and investigations for PFA, provides a brief history of the development of contemporary implants, and presents the clinical outcomes for the prostheses most commonly used in the UK. In addition, it provides a detailed surgical technique for implantation of an onlay implant, with tips on how to optimize patellofemoral biomechanics and thus achieve a consistently good outcome.     

Peribursal fat plane of the shoulder: anatomic study and clinical experience

A comprehensive anatomic and radiographic analysis of the peribursal fat plane in 12 cadavers confirmed that the fat plane seen on radiographs represents extrasynovial fat lining the subacromial bursa and documented the anatomic relations of the bursa. A three-part retrospective clinical evaluation of rotator cuff tears, calcific tendinitis, and rheumatoid arthritis was performed. Two osteoradiologists blindly graded the appearance of the peribursal fat plane with the shoulder in external versus internal rotation in 21 patients with arthrographically intact rotator cuffs and 21 patients with disrupted rotator cuffs. The peribursal fat plane was seen better with disrupted rotator cuffs. The peribursal fat plane was seen better with the shoulder in internal rotation and was seen in 60% of control subjects but only 21% of patients with rotator cuff tears. Partial or complete obliteration of this fat plane is a sensitive (79%) but less specific (60%) indicator of rotator cuff tears. Obliteration of the peribursal fat plane by inflammatory processes in adjacent tissues, including calcific tendinitis and rheumatoid arthritis, occurred with a high frequency.     

Performance in population models for count data,part II: A new SAEM algorithm

Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (Plan et al., 2008, Abstr 1372 []). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13% for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7% for all explored scenarios. The longest CPU time was 95 s for parameter estimation and 56 s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009).