Modeling major lung resection outcomes using classification trees and multiple imputation techniques
Modeling major lung resection outcomes using classification trees and multiple imputation techniques
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DOI：
10.1016/j.ejcts.2008.07.037
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发表时间：
2008-11-01
影响因子：
3.4
通讯作者：
Karrison, Theodore
中科院分区：
文献类型：
--
作者：
Ferguson, Mark K.;Siddique, Juned;Karrison, Theodore
目的：由于对预测变量（协变量）的观察不完整，与肺切除术相关的手术风险建模可能不准确且效率低下。缺失值通常不会随机出现，这可能会在建模中引入重要的偏倚来源。删除缺失数据的病例也会导致精度损失。目前的研究分析不完全变量作为潜在的预测结果后，主要肺切除术使用插补技术。研究方法：我们分析了1980年至2006年接受肺切除术的患者的肺部、心血管和总体并发症以及死亡率的预测因素。预测变量最初使用分类和回归树（CART）方法确定。建立了插补模型，对缺失值变量进行多重插补。我们使用CART变量和临床上感兴趣的任何协变量为每个结果拟合逻辑回归模型。结果如下：在1046例切除患者中，血清白蛋白和弥散量（DLCO%）有大量缺失值（分别为32%和13%）。模型包括肺部并发症的10个协变量（DLCO%和第一秒用力呼气量[FEV1%] p < 0.05），心血管并发症的12个协变量（FEV1%、切除范围、手术年份和年龄p < 0.05），总体并发症的15个协变量（DLCO%、体力状态、血清白蛋白和FEV 1/FVC比p < 0.05），以及死亡的12个协变量（DLCO%、切除范围和手术年p < 0.05）。结论：我们确定血清白蛋白是一个以前报道不足的和强有力的预测整体并发症。血清白蛋白与肺大手术后的肺和心血管结局有轻微显著相关。使用插补技术对手术风险进行建模，在识别重要的预测变量方面具有潜在价值，这些变量通常可能从分析中排除，或者由于临床数据库中的观察结果不完整而未被识别为预测因子。(C)2008年欧洲胸外科协会。Elsevier B. V.出版，保留所有权利。
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. (C) 2008 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.