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We sought to describe our experience on the management of the imbalanced CIEDI dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Database from two centers of patients undergoing device implantation from 2001 to 2016 were reviewed retrospectively. Re-sampling technique was used to improve the classifier accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>CIEDI was identified in 28 out of 4959 procedures (0.56%); a high imbalance existed in the sizes of the patient profiles. In univariate analyses, replacement procedure and male were significantly associated with an increase in CIEDI: (53.6% vs. 23.4, 0.8% vs. 0.3%,<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.01). Multivariate logistic regression analysis showed that gender (odds ratio, OR\u2009=\u20093.503), age (OR\u2009=\u20091.032), replacement procedure (OR\u2009=\u20093.503), and use of antibiotics (OR\u2009=\u20090.250) remained as independent predictors of CIEDI (all<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05) after adjustment for diabetes, post-operation fever, and device style, device company.<\/jats:p><jats:p>There were 616 under-sampled cases and 123 over-sampled cases in the analyzed cohort after re-sampling. The re-sampling and bootstrap results were robust and largely like the analysis results prior re-sampling method, while use of antibiotics lost the predicting capacity for CIEDI after re-sampling technique (<jats:italic>P<\/jats:italic>\u2009&gt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The application of re-sampling techniques can generate useful synthetic samples for the classification of imbalanced data and improve the accuracy of predicting efficacy of CIEDI. The peri-operative assessment should be intensified in male and aged patients as well as patients receiving replacement procedures for the risk of CIEDI.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-019-0899-4","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T06:51:48Z","timestamp":1568184708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7616-8235","authenticated-orcid":false,"given":"Xiang-Fei","family":"Feng","sequence":"first","affiliation":[]},{"given":"Ling-Chao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Li-Zhuang","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Yi-Gang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,11]]},"reference":[{"issue":"8","key":"899_CR1","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1093\/europace\/euz137","volume":"21","author":"V Traykov","year":"2019","unstructured":"Traykov V, Bongiorni MG, Boriani G, Burri H, Costa R, Dagres N, Deharo JC, Epstein LM, Erba PA, Snygg-Martin U, et al. 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