HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Mark K. Ferguson Permission Requests Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Ferguson, M. K. Articles by Karrison, T. Search for Related Content PubMed Articles by Ferguson, M. K. Articles by Karrison, T. Related Collections Lung - cancer Lung - other

Modeling major lung resection outcomes using classification trees and multiple imputation techniques

^a Department of Surgery, The University of Chicago, 5841 South Maryland Avenue, MC5035, Chicago, IL USA
^b Department of Health Studies, The University of Chicago, Chicago, IL USA

Received 5 June 2008; received in revised form 14 July 2008; accepted 21 July 2008.

^_* Corresponding author. Tel.: +1 773 702 3551; fax: +1 773 702 2642. (Email: mferguso{at}surgery.bsd.uchicago.edu).

Objective: Modeling of operative risks associated with major lung resection is potentially inaccurate and inefficient because of incomplete observations for predictor variables (covariates). Missing values do not usually occur randomly, potentially introducing an important source of bias in modeling. Deletion of cases with missing data also results in loss of precision. The current study analyzes incomplete variables as potential predictors of outcomes after major lung resection using imputation techniques. Methods: We analyzed major lung resection patients treated from 1980 to 2006 for predictors of pulmonary, cardiovascular, and overall complications, as well as mortality. Predictive variables were initially determined using classification and regression tree (CART) methods. Imputation models were developed and variables with missing values were multiply imputed. We fit a logistic regression model for each outcome using CART variables and any covariates that were of interest clinically. Results: Of 1046 resected patients, serum albumin and diffusing capacity (DLCO%) had a large number of missing values (32% and 13% missing, respectively). Models included 10 covariates for pulmonary complications (p < 0.05 for DLCO% and forced expiratory volume in the first second [FEV1%]), 12 covariates for cardiovascular complications (p < 0.05 for FEV1%, extent of resection, year of operation, and age), 15 covariates for overall complications (p < 0.05 for DLCO%, performance status, serum albumin, and FEV1/FVC ratio), and 12 covariates for death (p < 0.05 for DLCO%, extent of resection, and operation year). Conclusions: We identified serum albumin as a previously under-reported and strong predictor of overall complications. Serum albumin was marginally significantly related to pulmonary and cardiovascular outcomes after major lung surgery. Use of imputation techniques for modeling surgical risks has potential value in identifying important predictive variables that may ordinarily be eliminated from analysis or not identified as predictors because of incomplete observations in clinical databases.

Key Words: Neoplasm • Lung • Surgical risk • Serum albumin • Imputation • Diffusing capacity • Classification and regression tree