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BACKGROUND AND OBJECTIVE: Simulation-based medical education (SBME) is used to teach residents. However, few studies have evaluated its clinical impact. The goal of this study was to evaluate the impact of an SBME session on pediatric interns’ clinical procedural success. METHODS: This randomized trial was conducted at 10 academic medical centers. Interns were surveyed on infant lumbar puncture (ILP) and child intravenous line placement (CIV) knowledge and watched audiovisual expert modeling of both procedures. Participants were randomized to SBME mastery learning for ILP or CIV and for 6 succeeding months reported clinical performance for both procedures. ILP success was defined as obtaining a sample on the first attempt with <1000 red blood cells per high-power field or fluid described as clear. CIV success was defined as placement of a functioning catheter on the first try. Each group served as the control group for the procedure for which they did not receive the intervention. RESULTS: Two-hundred interns participated (104 in the ILP group and 96 in the CIV group). Together, they reported 409 procedures. ILP success rates were 34% (31 of 91) for interns who received ILP mastery learning and 34% (25 of 73) for controls (difference: 0.2% [95% confidence interval: –0.1 to 0.1]). The CIV success rate was 54% (62 of 115) for interns who received CIV mastery learning compared with 50% (58 of 115) for controls (difference: 3% [95% confidence interval: –10 to 17]). CONCLUSIONS: Participation in a single SBME mastery learning session was insufficient to affect pediatric interns’ subsequent procedural success.

AbstractSulindac is a prescription‐based non‐steroidal anti‐inflammatory drug (NSAID) that continues to be actively investigated as a candidate cancer chemoprevention agent. To further current understanding of sulindac bioavailability, metabolism, and disposition, we developed a population pharmacokinetic model for the parent compound and its active metabolites, sulindac sulfide, and exisulind. This analysis was based on data from 24 healthy subjects who participated in a bioequivalence study comparing two formulations of sulindac. The complex disposition of sulindac and its metabolites was described by a seven‐compartment model featuring enterohepatic recirculation and is the first reported population pharmacokinetic model for sulindac. The derived model was used to explore effects of clinical variables on sulindac pharmacokinetics and revealed that body weight, creatinine clearance, and gender were significantly correlated with pharmacokinetic parameters. Moreover, the model quantifies the relative bioavailability of the sulindac formulations and illustrates the utility of population pharmacokinetics in bioequivalence assessment. This novel population pharmacokinetic model provides new insights regarding the factors that may affect the pharmacokinetics of sulindac and the exisulind and sulindac sulfide metabolites in generally healthy subjects, which have implications for future chemoprevention trial design for this widely available agent.

ObjectivesThe purpose of this study was to evaluate the left main (LM) coronary artery anatomy using three‐dimensional (3D) quantitative coronary angiography (QCA) software as compared to intravascular ultrasound (IVUS).BackgroundPercutaneous intervention of the LM coronary artery is becoming more common in selected patients with LM coronary artery disease (CAD). Quantification of LM CAD by conventional angiography can be difficult. IVUS is considered the gold standard to evaluate LM anatomy and severity of CAD but entails additional steps, catheters, and expertise. Our objective was to compare a novel quantitative angiographic analysis system with IVUS for LM anatomy.MethodsFifty five patients underwent both coronary angiography and IVUS of the LM. LM measurements were analyzed with 3D QCA (IC‐PRO, Paieon, Israel) software using IVUS as the reference standard. The measurements included proximal, middle, distal minimal luminal diameter (MLD) and area. Additionally, lesion MLD, minimal luminal area were recorded by both systems. Bland–Altman plots were used to investigate agreement between the two imaging systems.ResultsOf the 55 patients in our cohort, average age was 66 ± 11 years (25% female). By Bland‐Altman analysis there was very good agreement between 3D QCA and IVUS for measures of MLD and minimal lumen area (MLA). However, there was poor concordance in the estimation of plaque burden between the two methods.ConclusionsOur data demonstrate that 3D QCA software has fair agreement when compared with IVUS for imaging of LM MLD and MLA. These results suggest that 3D QCA could potentially be helpful to guide intervention of the LM. © 2012 Wiley Periodicals, Inc.

Objective: Therapeutic goals for chronic mental disorders like major depressive disorder (MDD) and schizophrenia have evolved in parallel with the growing medical knowledge about the course and treatment of these disorders. Although the knowledge base regarding the clinical course of ADHD, a chronic psychiatric disorder, has evolved beyond symptomatic improvement and short-term treatment response, long-term goals, such as functional remission, have not yet been clearly defined. Method: A PubMed literature search was conducted to investigate the therapeutic goals of pharmacologic treatment referenced in the published literature from January 1998 through February 2010 using the following commonly used ADHD treatments as keywords: amphetamine, methylphenidate, atomoxetine, lisdexamfetamine, guanfacine, and clonidine. This search was then combined with an additional search that included the following outcome keywords: remission, relapse, remit, response, normal, normalization, recovery, and effectiveness. Results: Our search identified 102 publications. The majority (88.2% [90/102]) of these contained predefined criteria for treatment response. Predefined criteria for normalization and remission and/or relapse were presented in 4.9% (5/102), 12.7% (13/102), and 3.9% (4/102) of publications, respectively. There was a lack of consistency between the instruments used to measure outcomes as well as the criteria used to define treatment response, normalization, and remission as well as relapse. Conclusion: The therapeutic goals in treating ADHD should address optimal treatment outcomes that go beyond modest reductions of ADHD symptoms to include syndromatic, symptomatic, and functional remission. Future work should focus on reliable and valid tools to measure these outcomes in the clinical trial setting.