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Given the higher medical burden and lower susceptibility to azoles in C. glabrata infections, identifying these infections is critical. From 2016 to 2021, patients with deep-seated candidiasis due to C. glabrata and non-glabrata Candida met the criteria to be enrolled in the study. Clinical data were randomly divided into training and validation cohorts. A predictive model and nomogram were constructed using R software based on the stepwise algorithm and logistic regression. The performance of the model was assessed by the area under the receiver operating characteristic curve and decision curve analysis (DCA). A total of 197 patients were included in the study, 134 of them infected with non-glabrata Candida and 63 with C. glabrata. The predictive model for C. glabrata infection consisted of gastrointestinal cancer, co-infected with bacteria, diabetes mellitus, and kidney dysfunction. The specificity was 84.1% and the sensitivity was 61.5% in the validation cohort when the cutoff value was set to the same as the training cohort. Based on the model, treatment for patients with a high-risk threshold was better than \u2018treatment for all\u2019 in DCA, while opting low-risk patients out of treatment was also better than \u2018treatment for none\u2019 in opt-out DCA. The predictive model provides a rapid method for judging the probability of infections due to C. glabrata and will be of benefit to clinicians making decisions about therapy strategies.<\/jats:p>","DOI":"10.1093\/mmy\/myae010","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T08:51:02Z","timestamp":1707209462000},"source":"Crossref","is-referenced-by-count":1,"title":["A novel model for predicting deep-seated candidiasis due to <i>Candida glabrata<\/i> among cancer patients: A 6-year study in a cancer center of China"],"prefix":"10.1093","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6526-7916","authenticated-orcid":false,"given":"Ding","family":"Li","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy , Tianjin , 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