Biomedical subjects

Sean M O'Brien

Publications and source records attributed to Sean M O'Brien.

4 recordsLinked to original sources

PubMed2005

Clinical predictors of major infections after cardiac surgery.

BACKGROUND: Major infections are infrequent but important complications of cardiac surgery. Predicting their occurrence is essential for future prevention. The objective of the current investigation was to create and validate a bedside scoring system to estimate patient risk for major infection (mediastinitis, thoracotomy or vein harvest site infection, or septicemia) after coronary artery bypass grafting. METHODS AND RESULTS: Using the Society of Thoracic Surgeons National Cardiac Database, we analyzed 331 429 coronary artery bypass grafting cases from January 1, 2002, to December 31, 2003, to identify risk factors for major infection. Using logistic regression, 2 models were generated and validated using split-sample validation: (1) One limited to preoperative characteristics (preop model) and (2) one model including both preoperative and intraoperative characteristics (combined model). Major infection occurred in 11 636 patients (3.51%) (25.1% mediastinitis, 32.6% saphenous harvest site, 35.0% septicemia, 0.5% thoracotomy, 6.8% multiple sites). Patients with major infection had significantly higher mortality (17.3% versus 3.0%, P<0.0001) and postoperative length of stay >14 days (47.0% versus 5.9%, P<0.0001) than patients without major infection. Both the preop model (c-index 0.697) and combined model (c-index: 0.708) successfully discriminated between high- and low-risk patients. A simplified risk scoring system of 12 variables accurately predicted risk for major infection. CONCLUSIONS: We identified and validated a model that can identify patients undergoing cardiac surgery who are at high risk for major infection. These high-risk patients may be targeted for perioperative intervention strategies to reduce rates of major infection.

Aged↗

PubMed2005

Surgical treatment of mitral valve endocarditis in North America.

BACKGROUND: Several single-institution series have suggested the feasibility and effectiveness of mitral valve repair for infective endocarditis (IE). METHODS: We examined 6627 patients with IE undergoing mitral valve surgery at 661 Society of Thoracic Surgeons-participating centers in 1994 to 2003. RESULTS: The diagnosis of IE was assigned to 5.8% (6,627 of 114,934) of patients having mitral valve surgery. The overall frequency of mitral valve repair for IE was 29.7% (1,965 of 6,627). Mitral valve repair was less frequently used for patients with active IE (423 of 2,654; 15.9%) than those with treated IE (1,459 of 3,570; 40.9%). Operative mortality was 3.7% (72 of 1,965) for mitral valve repair and 10.8% (502 of 4,662) for mitral valve replacement. Mortality rates were lower for patients with treated IE compared with active IE. After adjusting for multiple preoperative risk factors, mitral valve repair (odds ratio, 0.67; 95% confidence interval, 0.51 to 0.88) was associated with a significantly lower risk of death. Active (versus treated) IE (odds ratio, 2.12; 95% confidence interval, 1.68 to 2.68) and recent cerebrovascular accident (odds ratio, 1.71; 95% confidence interval, 1.28 to 2.31) were independent predictors of mortality. CONCLUSIONS: Mitral valve repair is less commonly applied for IE compared with other indications for mitral valve surgery. Patients with active IE were less likely to receive repair than those with treated IE. Mitral valve repair was associated with a lower risk of mortality. These results provide support for performing mitral valve repair when technically feasible in the setting of IE.

Aged↗

PubMed2004

Cutpoint selection for categorizing a continuous predictor.

This article presents a new approach for choosing the number of categories and the location of category cutpoints when a continuous exposure variable needs to be categorized to obtain tabular summaries of the exposure effect. The optimum categorization is defined as the partition that minimizes a measure of distance between the true expected value of the outcome for each subject and the estimated average outcome among subjects in the same exposure category. To estimate the optimum partition, an efficient nonparametric estimate of the unknown regression function is substituted into a formula for the asymptotically optimum categorization. This new approach is easy to implement and it outperforms existing cutpoint selection methods.

Age Factors↗

PubMed2004

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic distribution that can be used to construct a likelihood for multivariate logistic regression analysis of binary and categorical data. The model for individual outcomes has a marginal logistic structure, simplifying interpretation. We follow a Bayesian approach to estimation and inference, developing an efficient data augmentation algorithm for posterior computation. The method is illustrated with application to a neurotoxicology study.

Algorithms↗