A Predictive Model for 30-Day Mortality of Fungemia in ICUs

Abstract

Peng Xie,1,2,* Wenqiang Wang,3,* Maolong Dong1,4 1Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Critical Care Medicine, Nanchong Central Hospital, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, People’s Republic of China; 3Department of Nursing, Nanchong Central Hospital, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, People’s Republic of China; 4Department of Burns, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Maolong Dong, No. 1838, Guangzhou Avenue North Road, Guangzhou, 510515, Guangdong, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]: Few predictive models have been established to predict the risk of 30-day mortality from fungemia. This study aims to create a nomogram to predict the 30-day mortality of fungemia in ICUs.Methods: Data of ICU patients with fungemia from both the Medical Information Mart for Intensive Care (MIMIC-III) database and the Grade-III Class-A hospital in China were collected. The data extracted from the MIMIC-III database functioned as the training dataset, which was used to construct a predictive model for 30-day mortality risk in ICU patients with fungemia; the data from the hospital functioned as the validation dataset, which was used to validate the model. A predictive model for 30-day mortality risk in ICU patients with fungemia was then built based on R software. Such indicators as C-index and calibration curve were utilized to evaluate the prediction ability of the model. Data of ICU patients with fungemia from the hospital were used as a validation dataset to validate the model.Results: Predictive models were constructed by age, international normalized ratio (INR), renal failure, liver disease, respiratory rate (RR), glucocorticoid therapy, antifungal therapy, and platelets. The C-index value of the models was 0.838 (95% CI: 0.79096– 0.88504). Attested by external validation results, the model has satisfactory predictive ability.Conclusion: The 30-day mortality risk predictive model for ICU patients with fungemia constructed in this study has good predictive ability and may hopefully provide a 30-day mortality risk screening tool for ICU patients with fungemia.Keywords: fungemia, ICU, mortality, predictive model

Keywords

• fungemia；icu；mortality；predictive model