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

Abstract

Background

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

Data Sharing Statement

The datasets generated during and/or analyzed during the current study are available from https://mimic.physionet.org/tutorials/intro-to-mimic-iii.

Disclosure

All authors report no potential conflicts of interest in this work.

Additional information

Funding

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.