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- W3085228855 abstract "Despite advances in antifungal treatment, the mortality associated with invasive candidiasis (IC) remains high. Early diagnosis of IC can be challenging, leading to the high morbidity and mortality. To assist early detection, the objective of the study was to develop a tool that predicts IC in intensive care units. Data from the Medical Information Mart for Intensive Care III database were used to construct a retrospective cohort of patients with infection. IC was defined if a patient (1) had a positive microorganism test for IC, or (2) had a diagnosis code indicating IC; or (3) had a sepsis diagnosis and received intravenous antifungal treatment. Candidate predictors included patient demographics, comorbidities, primary diagnosis, procedures, composite severity scores (e.g. SOFA, ASP III), laboratory tests, and medications used. An iterative purposeful selection was used to select the most prognostic features. A risk score was calculated for each patient to enumerate the IC risk. Model performance was evaluated using classification, discrimination, and calibration. Internal validation was conducted using an independent subsample of the study cohort. A total of 15,580 eligible patients were included, including 1,368 (8.8%) with IC. The average age of the cohort was 81 years and 46.5% were female. Top prognostic features included total parenteral nutrition, gastrointestinal surgery, central line, APS III, neutrophil count, weight, and preexisting conditions such as cancer, hepatic disorders, and AIDS. The prediction model has a sensitivity of 0.74, a specificity of 0.75, and a c-statistic of 0.82. Calibration results show consistently increased IC rates for patients with higher predicted risk scores. The internal validation showed comparable results. The prediction tool shows good predictive performance in evaluating the risk of IC among patients receiving intensive care. Accurate early detection of IC followed by appropriate treatment is expected to prevent mortality and improve clinical outcomes." @default.
- W3085228855 created "2020-09-21" @default.
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- W3085228855 date "2020-09-01" @default.
- W3085228855 modified "2023-10-03" @default.
- W3085228855 title "PIN46 Predicting Invasive Candidiasis in Intensive Care Units" @default.
- W3085228855 doi "https://doi.org/10.1016/j.vhri.2020.07.293" @default.
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