Daniel J. Luckett

The impact of current smoking on outcomes after infrainguinal bypass for claudication

Objective: Although smoking cessation is a benchmark of medical management of intermittent claudication, many patients require further revascularization. Currently, revascularization among smokers is a controversial topic, and practice patterns differ institutionally, regionally, and nationally. Patients who smoke at the time of revascularization are thought to have a poor prognosis, but data on this topic are limited. The purpose of this study was to evaluate the impact of smoking on outcomes after infrainguinal bypass for claudication. Methods: Data from the national Vascular Quality Initiative from 2004 to 2014 were used to identify infrainguinal bypasses performed for claudication. Patients were categorized as former smokers (quit >1 year before intervention) and current smokers (smoking within 1 year of intervention). Demographic and comorbid differences of categorical variables were assessed. Significant predictors were included in adjusted Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) by smoking status for outcomes of major adverse limb event (MALE), amputation‐free survival, limb loss, death, and MALE or death. Cumulative incidence curves were created using competing risks modeling. Results: We identified 2913 patients (25% female, 9% black) undergoing incident infrainguinal bypass grafting for claudication. There were 1437 current smokers and 1476 former smokers in our study. Current smoking status was a significant predictor of MALE (HR, 1.27; 95% CI, 1.00‐1.60; P = .048) and MALE or death (HR, 1.22; 95% CI, 1.03‐1.44; P = .02). Other factors found to be independently associated with poor outcomes in adjusted models included black race, below‐knee bypass grafting, use of prosthetic conduit, and dialysis dependence. Conclusions: Current smokers undergoing an infrainguinal bypass procedure for claudication experienced more MALEs than former smokers did. Future studies with longer term follow‐up should address limitations of this study by identifying a data source with long‐term follow‐up examining the relationship of smoking exposure (pack history and duration) with outcomes.

Multiple-Institution Comparison of Resident and Faculty Perceptions of Burnout and Depression During Surgical Training

Importance Prior studies demonstrate a high prevalence of burnout and depression among surgeons. Limited data exist regarding how these conditions are perceived by the surgical community. Objectives To measure prevalence of burnout and depression among general surgery trainees and to characterize how residents and attendings perceive these conditions. Design, Setting, and Participants This cross-sectional study used unique, anonymous surveys for residents and attendings that were administered via a web-based platform from November 1, 2016, through March 31, 2017. All residents and attendings in the 6 general surgery training programs in North Carolina were invited to participate. Main Outcomes and Measures The prevalence of burnout and depression among residents was assessed using validated tools. Burnout was defined by high emotional exhaustion or depersonalization on the Maslach Burnout Inventory. Depression was defined by a score of 10 or greater on the Patient Health Questionnaire–9. Linear and logistic regression models were used to assess predictive factors for burnout and depression. Residents’ and attendings’ perceptions of these conditions were analyzed for significant similarities and differences. Results In this study, a total of 92 residents and 55 attendings responded. Fifty-eight of 77 residents with complete responses (75%) met criteria for burnout, and 30 of 76 (39%) met criteria for depression. Of those with burnout, 28 of 58 (48%) were at elevated risk of depression (P = .03). Nine of 77 residents (12%) had suicidal ideation in the past 2 weeks. Most residents (40 of 76 [53%]) correctly estimated that more than 50% of residents had burnout, whereas only 13 of 56 attendings (23%) correctly estimated this prevalence (P < .001). Forty-two of 83 residents (51%) and 42 of 56 attendings (75%) underestimated the true prevalence of depression (P = .002). Sixty-six of 73 residents (90%) and 40 of 51 attendings (78%) identified the same top 3 barriers to seeking care for burnout: inability to take time off to seek treatment, avoidance or denial of the problem, and negative stigma toward those seeking care. Conclusions and Relevance The prevalence of burnout and depression was high among general surgery residents in this study. Attendings and residents underestimated the prevalence of these conditions but acknowledged common barriers to seeking care. Discrepancies in actual and perceived levels of burnout and depression may hinder wellness interventions. Increasing understanding of these perceptions offers an opportunity to develop practical solutions.

Survival distributions based on the incomplete gamma function ratio

A method to produce new families of probability distributions is presented based on the incomplete gamma function ratio. The distributions distributions produced also can include a number of popular univariate survival distributions, including the gamma, chi-square, exponential, and half-normal. Examples that demonstrate the generation of new distributions are provided.

A Master Pipeline for Discovery and Validation of Biomarkers

A major challenge in precision medicine is the development of biomarkers which can effectively guide patient treatment in a manner which benefits both the individual and the population. Much of the difficulty is the poor reproducibility of existing approaches as well as the complexity of the problem. Machine learning tools with rigorous statistical inference properties have great potential to move this area forward. In this chapter, we review existing pipelines for biomarker discovery and validation from a statistical perspective and identify a number of key areas where improvements are needed. We then proceed to outline a framework for developing a master pipeline firmly grounded in statistical principles which can yield better reproducibility, leading to improved biomarker development and increasing success in precision medicine.