Fate of the esophagogastric anastomosis

ObjectiveThe study objective was to evaluate histopathology of the esophagogastric anastomosis after esophagectomy, determine time trends of histologic changes, and identify factors influencing those findings.MethodsA total of 231 patients underwent 468 upper gastrointestinal endoscopies with anastomotic biopsy a median of 3.5 years after esophagectomy. Mean age was 59 ± 12 years, 74% (171) were male, and 96% (222) were white. Seventy-eight percent (179) had esophagectomy for cancer, 13% (30) had chemoradiotherapy, and 13% (30) had prior esophageal surgery. The anastomosis was 20 ± 2.0 cm from the incisors. Anti-reflux medications were used in 59% of patients (276/468) at esophagoscopy. Histopathology was graded as normal (0), consistent with reflux (1), cardia mucosa (2), intestinal metaplasia (3), and dysplasia (4). Repeated-measures nonlinear time-trend analysis and multivariable analyses were used.ResultsGrades 0 and 1 were constant, 5% and 92% at 10 years, respectively. Anti-reflux medication, induction therapy, and higher anastomosis were predictive of less grade 1 histopathology. Grades 2 and 3 increased with time: 12% and 33% at 5 years and 4% and 16% at 10 years, respectively. No variable was predictive of grade 2 or 3 (P > .15) except passage of time. No patient’s condition progressed to dysplasia or cancer.ConclusionsThe esophagogastric anastomosis is subject to gastroesophageal reflux. To minimize histopathologic changes of reflux, the anastomosis should be constructed as high as possible (closer to incisors) and anti-reflux medications prescribed. Surveillance endoscopy, if performed, will document a time-related progression of reflux-related histopathologic changes. However, during surveillance, intestinal metaplasia is uncommon and progression to cancer rare

Outcomes of less invasive J-incision approach to aortic valve surgery

ObjectiveLess invasive approaches to aortic valve surgery are increasingly used; however, few studies have investigated their impact on outcome. We sought to compare clinical outcomes after these approaches with full sternotomy using propensity-matching methods.MethodsFrom January 1995 to January 2004, a total of 2689 patients underwent isolated aortic valve surgery, 1193 via upper J-hemisternotomy and 1496 via full sternotomy. Because of important differences in patient characteristics between these groups, a propensity score based on 42 variables was used to obtain 832 well-matched patient pairs (70% of possible cases).ResultsIn-hospital mortality was identical for propensity-matched patients, 0.96% (8 in each). Occurrences of stroke (P > .9), renal failure (P = .8), and myocardial infarction (P = .7) were similar. However, 24-hour mediastinal drainage was a third less after less invasive surgery (median, 250 vs 350 mL; P < .0001), and fewer patients received transfusions (24% vs 34%; P < .0001). More patients undergoing less invasive surgery were extubated in the operating room (12% vs 1.6%; P < .0001), postoperative forced 1-second expiratory volume was higher (P = .009), and fewer had respiratory failure (P = .01). Early after operation, pain scores were lower (P < .0001) after less-invasive surgery and postoperative length of stay shorter (P < .0001).ConclusionsWithin that portion of the spectrum of isolated aortic valve surgery where propensity matching was possible, minimally invasive aortic valve surgery had not only cosmetic advantages, but blood product use, respiratory, pain, and resource utilization advantages over full sternotomy, and no apparent detriments. Less invasive aortic valve surgery should be considered for most aortic valve operations

Persistent risk of subsequent procedures and mortality in patients after interrupted aortic arch repair: A Congenital Heart Surgeons' Society study

ObjectiveMultiple subsequent procedures directed at the arch and/or the left ventricular outflow tract are frequently required after interrupted aortic arch repair. We the investigated patterns and factors associated with these subsequent procedures and mortality.MethodsWe reviewed the data from 447 patients with interrupted aortic arch at 33 institutions enrolled from 1987 to 1997. We classified the subsequent procedures by type (catheter-based or surgical) and focus (arch, left ventricular outflow tract, and “other” cardiovascular lesions). We used competing risks and modulated renewal analysis to explore subsequent procedures.ResultsThere were 158 subsequent arch and 100 left ventricular outflow tract procedures. Freedom from death at 21 years was 60% overall. The risk of additional subsequent arch procedures decreased after the first subsequent arch procedure in the acute phase, but did not significantly change in the chronic phase. The risk of additional subsequent left ventricular outflow tract procedures increased after the first subsequent left ventricular outflow tract procedure in the chronic phase. The risk factors for subsequent arch procedures and mortality, but not for subsequent outflow track procedures, were related in a complex way to previous procedures and their timing.ConclusionsInterrupted aortic arch is a chronic disease in which patients often undergo multiple subsequent procedures with persistent risk for additional intervention and mortality. The risk factors are related to the nature and timing of previous procedures and to the morphology and details of the index procedure. Interrupted aortic arch should be considered a chronic disorder

Some statistical properties of multivariate proper dispersion models, with special reference to a multivariate gamma model

A broad class of error distributions for generalized linear models is provided by the class of dispersion models which was introduced by Jorgensen (1987a, 1997a) and a detailed study on dispersion models was made by Jorgensen (1997b). In this thesis we study multivariate proper dispersion models. Our aim is to do multivariate analysis for non-normal data, particularly data from the multivariate gamma distribution which is an example of a multivariate proper dispersion model, introduced by Jorgensen and Lauritzen (1998). This class provides a multivariate extension of the dispersion model density, following the spirit of the multivariate normal density. We consider the saddlepoint approximation for small dispersion matrices, which, in turn, implies that the multivariate proper dispersion model is approximately multivariate normal for small dispersion matrices. We want to mimic the basic technique of testing in multivariate normal, Hotelling's T². Our version of the T² test applies asymptotically, for either small dispersion or large samples. We also consider estimating the normalizing constant of the bivariate gamma by Monte Carlo simulation and we investigate the marginal density by using numerical integration. We also investigate the distribution of the T²-statistic by Monte Carlo simulation.Science, Faculty ofStatistics, Department ofGraduat

Application of Gradient Boosting in Evaluating Surgical Ablation for Atrial Fibrillation

Atrial fibrillation (AF) represents a condition of irregularities of heartbeats. Timely diagnosis and treatment of AF are crucial to avoid serious consequences such as stroke, heart failure and death. AF is detected from patient’s rhythm data as a binary longitudinal sequence. In this article, we introduce an approach for analyzing the binary longitudinal sequence of AF. Our approach is based on gradient boosting, a machine learning approach. We use multivariate tree as a base learner to model complex relationships between multiple covariates and response. In order to model covariate-time interactions, we use B-spline. Application of our approach to a randomized trial data provides the importance of surgical ablation in the treatment of AF among patients with persistent or long-standing persistent AF. Comparison of prediction performance of our approach with other methods using randomized trial data and simulated data shows that our approach has a better prediction performance. Variable selection using variable importance has identified duration of AF as an important covariate that has strong interaction with post-surgery time. This helps to identify patients’ variability and groups of patients who derived the most benefits from the treatment. Our method can be implemented using the R package boostmtree

Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model

Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network