Liver Fibrosis in HIV Patients Receiving a Modern cART: Which Factors Play a Role?

Liver-related death in human immunodeficiency virus (HIV)-infected individuals is about 10 times higher compared with the general population, and the prevalence of significant liver fibrosis in those with HIV approaches 15%. The present study aimed to assess risk factors for development of hepatic fibrosis in HIV patients receiving a modern combination anti-retroviral therapy (cART).This cross-sectional prospective study included 432 HIV patients, of which 68 (16%) patients were anti-hepatitis C virus (HCV) positive and 23 (5%) were HBsAg positive.Health trajectory including clinical characteristics and liver fibrosis stage assessed by transient elastography were collected at inclusion. Liver stiffness values >7.1 kPa were considered as significant fibrosis, while values >12.5 kPa were defined as severe fibrosis. Logistic regression and Cox regression uni- and multivariate analyses were performed to identify independent factors associated with liver fibrosis.Significant liver fibrosis was detected in 10% of HIV mono-infected, in 37% of HCV co-infected patients, and in 18% of hepatitis B virus co-infected patients. The presence of diabetes mellitus (odds ratio [OR] = 4.6) and FIB4 score (OR = 2.4) were independently associated with presence of significant fibrosis in the whole cohort. Similarly, diabetes mellitus (OR = 5.4), adiposity (OR = 4.6), and the FIB4 score (OR = 3.3) were independently associated with significant fibrosis in HIV mono-infected patients. Importantly, cumulative cART duration protected, whereas persistent HIV viral replication promoted the development of significant liver fibrosis along the duration of HIV infection.Our findings strongly indicate that besides known risk factors like metabolic disorders, HIV may also have a direct effect on fibrogenesis. Successful cART leading to complete suppression of HIV replication might protect from development of liver fibrosis.     

Use of a Checklist During Observation of a Simulated Cardiac Arrest Scenario Does Not Improve Time to CPR and Defibrillation Over Observation Alone for Subsequent Scenarios

Theory: Immersive simulation is a common mode of education for medical students. Observation of clinical simulations prior to participation is believed to be beneficial, though this is often a passive process. Active observation may be more beneficial. Hypotheses: The hypothesis tested in this study was that the active use of a simple checklist during observation of an immersive simulation would result in better participant performance in a subsequent scenario compared with passive observation alone. Methods: Medical students were randomized to either passive or active (with checklist) observation of an immersive simulation involving cardiac arrest prior to participating in their own simulation. Performance measures included time to cardiopulmonary resuscitation (CPR) and time to defibrillation and were compared between first and second scenarios as well as between passive and active observers. Results: Seventy-nine simulations involving 232 students were conducted. Mean time to CPR was 18 seconds (SD = 11.6) for those using the checklist and 24 seconds (SD = 15.8) for those who observed passively (M difference = 6 seconds), t(35) = 1.46, p =.153. Time to defibrillation was 94 seconds (SD = 26.4) for those using the checklist and 92 seconds (SD = 23.8) for those who observed passively (M difference = –2 seconds), t(38) =.21, p =.837. Time to CPR was 24 seconds (SD = 15.8) for passive observers and 31 seconds (SD = 21.0; M difference = 7 seconds), t(35) = 1.13, p =.265, for their first scenario counterparts. Time to CPR was 18 seconds (SD = 11.6) for active observers and 36 seconds (SD = 26.2; M difference = 18 seconds), t(24) = 2.81, p =.010, for their first scenario counterparts. Time to defibrillation was 92 seconds (SD = 23.8) for passive observers and 125 seconds (SD = 32.2; M difference = 33 seconds), t(33) = 3.63, p =.001, for their first scenario counterparts. Time to defibrillation was 94 seconds (SD = 26.4) for the active observers and 132 seconds (SD = 52.9; M difference = 38 seconds), t(28) =.46, p =.008, for their first scenario counterparts. Conclusions: Observation alone leads to improved performance in the management of a simulated cardiac arrest. The active use of a simple skills-based checklist during observation did not appear to improve performance over passive observation alone.     

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.