BMC Med Inform Decis Mak - Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data?

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Resumo

CKGROUND: Claims data has usually been used in recent studies to identify cases of healthcare-associated infection. However, several studies have indicated that the ICD-9-CM codes might be inappropriate for identifying such cases from claims data; therefore, several researchers developed alternative identification models to correctly identify more cases from claims data. The purpose of this study was to investigate three common approaches to develop alternative models for the identification of cases of coronary artery bypass graft (CABG) surgical site infection, and to compare the performance between these models and the ICD-9-CM model.METHODS: The 2005-2008 National Health Insurance claims data and healthcare-associated infection surveillance data from two medical centers were used in this study for model development and model verification. In addition to the use of ICD-9-CM codes, this study also used classification algorithms, a multivariable regression model, and a decision tree model in the development of alternative identification models. In the classification algorithms, we defined three levels (strict, moderate, and loose) of the criteria in terms of their strictness. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were used to evaluate the performance of each model.RESULTS: The ICD-9-CM-based model showed good specificity and negative predictive value, but sensitivity and positive predictive value were poor. Performances of the other models were varied, except for negative predictive value. Among the models, the performance of the decision tree model was excellent, especially in terms of positive predictive value.CONCLUSION: The accuracy of identification of cases of CABG surgical site infection is an important issue in claims data. Use of the decision tree model to identify such cases can improve the accuracy of patient-level outcome research. This model should be considered when performing future research using claims data.

Resumo Limpo

ckground claim data usual use recent studi identifi case healthcareassoci infect howev sever studi indic icdcm code might inappropri identifi case claim data therefor sever research develop altern identif model correct identifi case claim data purpos studi investig three common approach develop altern model identif case coronari arteri bypass graft cabg surgic site infect compar perform model icdcm modelmethod nation health insur claim data healthcareassoci infect surveil data two medic center use studi model develop model verif addit use icdcm code studi also use classif algorithm multivari regress model decis tree model develop altern identif model classif algorithm defin three level strict moder loos criteria term strict sensit specif posit predict valu negat predict valu accuraci use evalu perform modelresult icdcmbas model show good specif negat predict valu sensit posit predict valu poor perform model vari except negat predict valu among model perform decis tree model excel especi term posit predict valueconclus accuraci identif case cabg surgic site infect import issu claim data use decis tree model identifi case can improv accuraci patientlevel outcom research model consid perform futur research use claim data

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