van Walraven Carl
Epidemiology & Community Medicine, University of Ottawa; Ottawa Hospital Research Institute, ASB1-003, 1053 Carling Ave., Ottawa, Ontario K1Y 4E9, Canada; Institute for Clinical Evaluative Sciences.
J Clin Epidemiol. 2017 Apr;84:114-120. doi: 10.1016/j.jclinepi.2017.01.007. Epub 2017 Feb 4.
Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias.
Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates.
Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized.
Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates.
行政数据库中使用的诊断编码因患者疾病状态的错误分类而导致偏差。尚不清楚哪种方法能将这种偏差降至最低。
采用血清肌酐测量值来确定50074名住院患者的严重肾衰竭状态。测量了严重肾衰竭的真实患病率及其与协变量的关联。将这些结果与使用替代测量方法确定肾衰竭状态的结果进行比较，替代测量方法包括：（1）诊断编码；（2）根据先前验证的模型确定的肾衰竭概率估计分类；或（3）使用模型衍生概率估计进行疾病状态的自助法插补。
当使用自助法根据基于模型的概率估计来插补肾衰竭状态时，严重肾衰竭患病率估计及其与协变量关联的偏差最小。相比之下，当使用编码或基于模型的条件概率分类的方法来确定肾衰竭状态时，偏差很大。
使用自助法根据多变量模型衍生的概率估计来插补疾病状态，可以将不准确诊断编码导致的错误分类偏差降至最低。
