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Factors Affecting the Risk of Infection

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...

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An Ex vivo Assay to Study Candida albicans Hyphal Morphogenesis in the Gastrointestinal Tract

An Ex vivo Assay to Study Candida albicans Hyphal Morphogenesis in the Gastrointestinal Tract

Published on: July 1, 2020

Risk prediction for invasive candidiasis.

Armin Ahmed¹, Afzal Azim¹, Arvind Kumar Baronia¹

¹Department of Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.

Indian Journal of Critical Care Medicine : Peer-Reviewed, Official Publication of Indian Society of Critical Care Medicine

|October 16, 2014

Summary

This summary is machine-generated.

Risk prediction models for invasive candidiasis (IC) help identify patients unlikely to benefit from empirical antifungal therapy, preventing overuse. These models show good negative but poor positive predictive value.

Keywords:

Critically ill invasive candidiasis risk factors risk prediction models

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Area of Science:

Infectious Diseases
Clinical Mycology
Epidemiology

Background:

Invasive candidiasis (IC) treatment has shifted towards prophylaxis and empirical approaches.
Risk factors for IC are increasingly used to develop predictive models.
Existing models demonstrate limitations in positive predictive value.

Purpose of the Study:

To review and analyze various risk prediction models for invasive candidiasis.
To evaluate the external validation of these risk prediction models.

Main Methods:

Literature review of risk prediction models for invasive candidiasis.
Analysis of external validation studies for identified models.

Main Results:

Risk prediction models for IC generally exhibit strong negative predictive value.
These models show a weaker positive predictive value.
Models are effective in identifying patient groups less likely to require empirical antifungal therapy.

Conclusions:

Risk prediction models are valuable tools for optimizing antifungal stewardship in invasive candidiasis.
Careful consideration of model performance, particularly positive predictive value, is essential for clinical application.
Further research on improving the positive predictive value of IC risk models is warranted.