Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data?
Background: 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 alternati...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 14; no. 1; pp. 42 - 43 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103958100&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103958100 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2014 vid: 14 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 103958100 103958100 NLM24884488 2012614315 10.1186/1472-6947-14-42 NLM24884488 PMC4050397 103958100 ppf: 42 ppct: 1 formats: tig: atl: Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data? aug: au: Yu, Tsung-Hsien Hou, Yu-Chang Lin, Kuan-Chia Chung, Kuo-Piao affil: Institute of Healthcare Policy and Management, National Taiwan University, Taipei, Taiwan. kpchung@ntu.edu.tw. sug: subj: Coronary Artery Bypass Adverse Effects Epidemiology Insurance National Health Programs Surgical Wound Infection Epidemiology Aged Antibiotics Therapeutic Use Female Health Status Indicators Human International Classification of Diseases Male Middle Age Models, Statistical Retrospective Design Sensitivity and Specificity Surgical Wound Infection Drug Therapy Surgical Wound Infection Etiology Taiwan Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Background: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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