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Age: 18Years - 90Years
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ID07681583

Developing and Testing the FUNGAL-P Clinical Prediction Score for Early Identification of Fungal Pneumonia in Adults with Pneumonia at Careggi University Hospital

Led by Azienda Ospedaliero-Universitaria Careggi · Updated on 2026-07-02

400

Participants Needed

1

Research Sites

52 weeks

Total Duration

AI-Summary

What this Trial Is About

The study investigates fungal pneumonia, a serious lung infection caused by fungi, which is often hard to diagnose early due to vague symptoms and complex testing. Researchers aim to develop and validate a clinical prediction score called FUNGAL-P to help identify fungal pneumonia or fungal-bacterial coinfections in adult patients presenting with pneumonia. This observational study is conducted at a single center and includes both retrospective and prospective patient groups. The study has two main phases. The retrospective phase reviews adult patients diagnosed with pneumonia between January 2022 and December 2023 who underwent microbiological testing within 48 hours of hospital admission. The prospective phase enrolls consecutive adult patients with pneumonia from January to December 2024 to evaluate the FUNGAL-P score without affecting their clinical care. Data collected include clinical signs, laboratory and radiological findings, microbiology results, treatment details, and outcomes. Participants provide information on their medical history, vital signs, and undergo routine microbiological assessments. Researchers analyze these data to find factors that predict fungal pneumonia, combining them into the FUNGAL-P score. They will measure how well the score identifies fungal pneumonia using statistics like sensitivity, specificity, and calibration. The primary outcome is the scores accuracy assessed by the area under the receiver operating characteristic curve AUROC. The study aims to improve early detection and guide timely treatment for fungal pneumonia.

CONDITIONS

Brief Title

Derivation and Validation of the Fungal Pneumonia Assessment and Likelihood Predictor Score

Research Team

L

Lorenzo Pelagatti, MD, PhDs

P

Peiman Nazerian, MD

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