The hip in arthrogryposis

Journal of Children's Orthopaedics - Hip dislocation is seen in approximately 30 % of children with amyoplasia and approximately 50 % of these will be bilateral. Closed...     

Prevalence and Predictors of Quality of Recovery at Home After Day Surgery

Traditionally, major complications and unanticipated admission/readmission rates were used to assess outcome after day surgery. However, in view of the relative absence of major complications the quality of recovery (QOR) should be considered one of the principal endpoints after day surgery. In our study, the level of QOR is defined by a combination of the Global Surgical Recovery (GSR) Index and the Quality of Life (QOL).The aim of this study was to analyze prevalence and predictors of QOR after day surgery on the fourth postoperative day.Elective patients scheduled for day surgery from November 2008 to April 2010 were enrolled in a prospective cohort study. Outcome parameters were measured by using questionnaire packages at 2 time points: 1 week preoperatively and 4 days postoperatively. Primary outcome parameter is the QOR and is defined as good if the GSR index >80% as well as the postoperative QOL is unchanged or improved as compared with baseline. QOR is defined as poor if both the GSR index ≤80% and if the postoperative QOL is decreased as compared with baseline. QOR is defined as intermediate in all other cases. Three logistic regression analyses were performed to determine predictors for poor QOR after day surgery.A total of 1118 patients were included. A good QOR was noted in 17.3% of patients, an intermediate QOR in 34.8%, and a poor QOR in 47.8% 4 days after day surgery. The best predictor for poor QOR after day surgery was type of surgery. Other predictors were younger age, work status, and longer duration of surgery. A history of previous surgery, expected pain (by the patient) and high long-term surgical fear were significant predictors of poor QOR in only 1 of 3 prediction models.The QOR at home 4 days after day surgery was poor in the majority of patients and showed a significant procedure-specific variation. Patients at risk for poor QOR can be identified during the preoperative period based on type of surgery, age, work status, and the duration of the surgery.     

The Importance of Venous Return in Starling‐Like Control of Rotary Ventricular Assist Devices

Rotary ventricular assist devices (VADs) are less sensitive to preload than the healthy heart, resulting in inadequate flow regulation in response to changes in patient cardiac demand. Starling‐like physiological controllers (SLCs) have been developed to automatically regulate VAD flow based on ventricular preload. An SLC consists of a cardiac response curve (CRC) which imposes a nonlinear relationship between VAD flow and ventricular preload, and a venous return line (VRL) which determines the return path of the controller. This study investigates the importance of a physiological VRL in SLC of dual rotary blood pumps for biventricular support. Two experiments were conducted on a physical mock circulation loop (MCL); the first compared an SLC with an angled physiological VRL (SLC‐P) against an SLC with a vertical VRL (SLC‐V). The second experiment quantified the benefit of a dynamic VRL, represented by a series of specific VRLs, which could adapt to different circulatory states including changes in pulmonary (PVR) and systemic (SVR) vascular resistance versus a fixed physiological VRL which was calculated at rest. In both sets of experiments, the transient controller responses were evaluated through reductions in preload caused by the removal of fluid from the MCL. The SLC‐P produced no overshoot or oscillations following step changes in preload, whereas SLC‐V produced 0.4 L/min (12.5%) overshoot for both left and right VADs. Additionally, the SLC‐V had increased settling time and reduced controller stability as evidenced by transient controller oscillations. The transient results comparing the specific and standard VRLs demonstrated that specific VRL rise times were improved by between 1.2 and 4.7 s ( = 3.05 s), while specific VRL settling times were improved by between 2.8 and 16.1 seconds ( = 8.38 s) over the standard VRL. This suggests only a minor improvement in controller response time from a dynamic VRL compared to the fixed VRL. These results indicate that the use of a fixed physiologically representative VRL is adequate over a wide variety of physiological conditions.