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Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches.

Panhui Xiong¹, Junliang Chen², Yue Zhang¹

¹Department of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

|February 9, 2024

Summary

Predictive models identify key clinical factors for diagnosing eosinophilic chronic rhinosinusitis (ECRS) without invasive biopsies. This approach aids in early ECRS detection and treatment planning.

Keywords:

Health informatics Health sciences Health technology Medical specialty Medicine

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Area of Science:

Otolaryngology
Medical Imaging
Computational Biology

Background:

Eosinophilic chronic rhinosinusitis (ECRS) presents significant challenges due to eosinophilic infiltration and treatment resistance.
Current diagnostic methods for ECRS often rely on invasive histological assessments.
There is a need for non-invasive diagnostic tools to improve ECRS identification.

Purpose of the Study:

To develop and validate predictive models for ECRS diagnosis using readily available clinical data.
To eliminate the necessity of histological examination for ECRS diagnosis.
To identify key clinical predictors associated with ECRS.

Main Methods:

Utilized logistic regression with lasso, random forest, gradient-boosted decision tree, and deep neural network models.
Trained models on clinical parameters from a cohort of 437 patients.
Evaluated model performance using AUC, decision curves, and feature ranking.

Main Results:

Peripheral blood eosinophil ratio and absolute peripheral blood eosinophil counts were identified as significant predictors.
Ethmoidal/maxillary sinus density ratio (E/M) on CT scans emerged as a crucial diagnostic indicator.
The developed models demonstrated effective predictive capabilities for ECRS.

Conclusions:

Non-invasive predictive models can accurately identify ECRS, reducing the need for biopsies.
Clinical parameters including blood eosinophil levels and sinus CT density ratios are valuable for ECRS diagnosis.
This approach enhances clinical decision-making and patient management for ECRS.