Exploratory Analysis to Determine Prevalences and Predictors of Central Venous Line Related Thromoboembolism and Infection in Children with Acute Lymphoblastic Leukemia

Abstract

<p>Children with acute lymphoblastic leukemia (ALL) are at high risk for getting thromboembolism (TE), which is a serious complication leading to morbidity and mortality. As treatment protocols have been developed achieving the cure rates as high as 80% [39], study efforts need to turning to evaluating the risk and management of associated TE. Published studies in this field have been mostly exploratory and have had different results in terms of screening TE risk factors predisposing to TE.</p> <p>Based on the records of 150 ALL children treated with central venous line (CVL) from 1995 to 2005 at McMaster Children's Hospital, this study was conducted to evaluate the prevalence ofTE, to explore the association between TE and infection, and to screen TE and Infection risk factors disposing children with ALL for TE and for Infection. The prevalence ofTE was estimated as 15 .07% (9.27%, 20.87%). Logistic regressions, Bayesian approaches, in combination with multiple imputation techniques, were employed to estimate predictors' odds ratios and their 95% confidence (credibility) intervals. The study suggested two significant factors, CVL functionality and ANC category for infection, and no significant factors for TE.</p> <p>As a comparative and supplementary tool to the traditional parametric analyses, we conducted Classification and Regression Trees (CART) modeling, by using three software packages, with intention to visualize predictors of TE and Infection by level of importance. SAS EM 5.0, SPSS 14.0 and S-Plus 6.1 were compared in their tree misclassifications based on our data and their features of tree growth algorithms, validation techniques, missing data handling, model pruning / recovering, output setting, tool tabs transparency, and advantages. SPSS 14.0 and SAS EM 5.0 are recommended based on our experience, though the strengths and weaknesses of each package should be weighted according to the users and the problem natures.</p> <p>The limitations of this exploratory study such as small sample size, missing values, imbalance between data categories, the lack of information about the timing of treatment and the lack of cross-validation techniques in some CART modeling packages led biases to our results. Large prospective cohort studies with few missing values are critical to achieve more accurate results.</p>