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. 2024 Nov;49(6):776-784.
doi: 10.1111/coa.14208. Epub 2024 Aug 7.

Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps

Anda Gata  1 , Lajos Raduly  2 , Liviuța Budișan  2 , Adél Bajcsi  3 , Teodora-Maria Ursu  3 , Camelia Chira  3 , Laura Dioșan  3 , Ioana Berindan-Neagoe  2 , Silviu Albu  1
Affiliations

Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps

Anda Gata et al. Clin Otolaryngol. 2024 Nov.

Abstract

Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.

Methods: We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. Preoperative (demographic data, blood eosinophiles, endoscopy, Lund-Mackay, SNOT-22 and depression PHQ scores) and follow-up data was standardly collected. Outcome measures included SNOT-22, PHQ-9 and endoscopy perioperative sinus endoscopy (POSE) scores and two different microRNAs (miR-125b, miR-203a-3p) from polyp tissue. Based on POSE score, three labels were created (controlled: 0-7; partial control: 8-15; or relapse: 16-32). Patients were divided into train and test groups and using Random Forest, we developed algorithms for predicting ESS related outcomes.

Results: Based on data collected from 85 patients, the proposed Machine Learning-approach predicted whether the patient would present control, partial control or relapse of nasal polyposis at 18 months following ESS. The algorithm predicted ESS outcomes with an accuracy between 69.23% (for non-invasive input parameters) and 84.62% (when microRNAs were also included). Additionally, miR-125b significantly improved the algorithm's accuracy and ranked as one of the most important algorithm variables.

Conclusion: We propose a Machine Learning algorithm which could change the prediction of disease course in CRSwNP.

Keywords: artificial intelligence; chronic rhinosinusitis with nasal polyps; endoscopic sinus surgery; machine learning; outcomes; prediction.

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References

    1. X. Tao, F. Chen, Y. Sun, et al., “Prediction Models for Postoperative Uncontrolled Chronic Rhinosinusitis in Daily Practice,” Laryngoscope 128, no. 12 (2018): 2673–2680.
    1. N. Bhattacharyya, “Ambulatory Sinus and Nasal Surgery in the United States: Demographics and Perioperative Outcomes,” Laryngoscope 120, no. 3 (2010): 635–638.
    1. C. Hopkins, R. Slack, V. Lund, P. Brown, L. Copley, and J. Browne, “Long‐Term Outcomes From the English National Comparative Audit of Surgery for Nasal Polyposis and Chronic Rhinosinusitis,” Laryngoscope 119, no. 12 (2009): 2459–2465.
    1. K. A. Smith, R. R. Orlandi, G. Oakley, H. Meeks, K. Curtin, and J. A. Alt, “Long‐Term Revision Rates for Endoscopic Sinus Surgery,” International Forum of Allergy & Rhinology 9, no. 4 (2019): 402–408.
    1. A. S. DeConde, J. C. Mace, J. M. Levy, L. Rudmik, J. A. Alt, and T. L. Smith, “Prevalence of Polyp Recurrence After Endoscopic Sinus Surgery for Chronic Rhinosinusitis With Nasal Polyposis,” Laryngoscope 127, no. 3 (2017): 550–555.

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