ArticlePloS one2019
An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Knowledge, attitude, and practice toward infection control precautions among medical students in Jordan: A cross-sectional study.Journal of education and health promotion · 2026Article
- Prediction of nosocomial infection incidence in the Department of Critical Care Medicine of Guizhou Province with a time series model.Annals of translational medicine · 2020Article
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12 authors.
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Abstract
objectiveTo develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU).
methodsA multicenter retrospective study was conducted to review 2017 data from six hospitals in Guizhou Province, China. A total of 1,782 ICU inpatients were divided into either a training set (n = 1,189) or a validation set (n = 593). The patients' demographic characteristics, basic clinical features from the previous admission, and their need for bacterial culture during the current admission were extracted from electronic medical records of the hospitals to predict HCAI. Univariate and multivariable analyses were used to identify independent risk factors of HCAI in the training set. The multivariable model's performance was evaluated in both the training set and the validation set, and an interactive nomogram was constructed according to multivariable regression model. Moreover, the interactive nomogram was used to predict the possibility of a patient developing an HCAI based on their prior admission data. Finally, the clinical usefulness of the interactive nomogram was estimated by decision analysis using the entire dataset.
resultsThe nomogram model included factor development (local economic development levels), length of stay (LOS; days of hospital stay), fever (days of persistent fever), diabetes (history of diabetes), cancer (history of cancer) and culture (the need for bacterial culture). The model showed good calibration and discrimination in the training set [area under the curve (AUC), 0.871; 95% confidence interval (CI), 0.848-0.894] and in the validation set (AUC, 0.862; 95% CI, 0.829-0.895). The decision curve demonstrated the clinical usefulness of our interactive nomogram.
conclusionsThe developed interactive nomogram is a simple and practical instrument for quantifying the individual risk of HCAI and promptly identifying high-risk patients.
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