Asian Nurs Res.  2018 Dec;12(4):299-303. 10.1016/j.anr.2018.11.004.

A Risk Prediction Model for Invasive Fungal Disease in Critically Ill Patients in the Intensive Care Unit

• Li F
• Zhou M
• Zou Z
• Li W
• Huang H
• Zhijie H

• KMID: 2431976
• DOI: http://doi.org/10.1016/j.anr.2018.11.004

Abstract

PURPOSE
Developing a risk prediction model for invasive fungal disease based on an analysis of the disease-related risk factors in critically ill patients in the intensive care unit (ICU) to diagnose the invasive fungal disease in the early stages and determine the time of initiating early antifungal treatment.
METHODS
Data were collected retrospectively from 141 critically ill adult patients with at least 4 days of general ICU stay at Sun Yat-sen Memorial Hospital, Sun Yat-sen University during the period from February 2015 to February 2016. Logistic regression was used to develop the risk prediction model. Discriminative power was evaluated by the area under the receiver operating characteristics (ROC) curve (AUC).
RESULTS
Sequential organ failure assessment (SOFA) score, antibiotic treatment period, and positive culture of Candida albicans other than normally sterile sites are the three predictors of invasive fungal disease in critically ill patients in the ICU. The model performs well with an ROC-AUC of .73.
CONCLUSION
The risk prediction model performs well to discriminate between critically ill patients with or without invasive fungal disease. Physicians could use this prediction model for early diagnosis of invasive fungal disease and determination of the time to start early antifungal treatment of critically ill patients in the ICU.

Keyword

critical illness; invasive fungal infections; logistic models; risk; risk factors

MeSH Terms

Adult
Candida albicans
Critical Care*
Critical Illness*
Early Diagnosis
Humans
Intensive Care Units*
Logistic Models
Retrospective Studies
Risk Factors
ROC Curve
Solar System