Recurrence of Chronic Rhinosinusitis with Nasal Polyps After Surgery: Risk Factors, Predictive Models, and Treatment Approaches with a Focus on Western and Asian Differences
Abstract
1. Introduction
2. Materials and Methods
2.1. Literature Search Strategy
2.2. Eligibility Criteria
- Studies involving adult patients (aged ≥ 18 years) undergoing endoscopic sinus surgery (ESS) for chronic rhinosinusitis (CRS)
- Reporting recurrence rates, recurrence risk factors, prediction models, or postoperative treatment strategies
- Conducted in or reporting data from Asian or Western populations
- Published in English with full text available
- Pediatric studies (involving patients < 18 years)
- Case reports or case series with <30 patients
- Narrative reviews without systematic methodology, unless cited for background
- Conference abstracts, editorials, and letters
2.3. Study Screening and Selection
3. Recurrence Rates of CRS After Surgery
3.1. Definitions of Recurrence
3.2. Recurrence Rates in Western vs. Asia
4. Risk Factors of CRS Recurrence
4.1. Patient Factors
4.2. Disease Characteristics
4.3. Surgical Factors
5. Predictive Models for Recurrence
5.1. Overview of Predictive Modeling Approaches
5.2. Clinical Predictors and Symptom-Based Models
5.3. Serologic and Immunologic Biomarkers
5.4. Histologic Markers of Inflammation
5.5. Radiologic Predictors
5.6. Role of Microbiota in Recurrence Risk
5.7. Integrated and Machine Learning–Based Prediction Models
6. Management of Recurrent CRS
6.1. Medical Treatments
6.2. Surgical Revision Strategies
7. Discussion
7.1. Methodological Limitations
7.2. Regional Differences in Predictors and Models
7.3. Therapeutic Implications
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CRS | Chronic rhinosinusitis |
CRSsNP | Chronic rhinosinusitis without nasal polyp |
CRSwNP | Chronic rhinosinusitis with nasal polyp |
ESS | Endoscopic sinus surgery |
SNOT-22 | Sino-Nasal Outcome Test |
VAS | Visual analog scale |
AERD | Aspirin-exacerbated respiratory disease |
AR | Allergic rhinitis |
CF | Cystic Fibrosis |
T2DM | Type 2 diabetes mellitus |
BMI | Body mass index |
JESREC | Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis |
IgE | Immunoglobulin E |
ECP | Eosinophil cationic protein |
CLC | Charcot-Leyden crystals |
NERD | NSAID-exacerbated respiratory disease |
CCAD | Central Compartment Atopic Disease |
SES | Steroid-eluting stents |
ML | Machine learning |
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Risk Factor/Description | |
---|---|
Patient Factors | Asthma and AERD are common and strong predictors for CRS recurrence [3,4,18,19,20,21] |
Allergic rhinitis (AR), often combined with asthma, increases recurrence risk via mucosal edema and eosinophilia [18,22,23,24] | |
Smoking increases risk, especially among asthmatic patients; also predicts postoperative inflammation [25,26,27,28,29] | |
Environmental and occupational exposures (e.g., dust, chemicals) independently raise recurrence and revision surgery risk [31] | |
Demographics: female sex and age (either younger or older, region-dependent) may influence recurrence risk [32,33,34,35,36] | |
Cystic fibrosis (CF) is a strong predictor of revision, especially in Western cohorts [26] | |
Metabolic comorbidities (obesity, metabolic syndrome, T2DM): higher BMI, MetS components, and T2DM linked to recurrence [28,37,38,39,40,41] | |
Serum uric acid (hyperuricemia) identified as an independent risk biomarker for recurrence [22,42] | |
Disease Characteristics | CRSwNP carries a higher recurrence risk than CRSsNP; pan-European study shows 3× higher revision with polyps [20,21,32,43] |
Eosinophilic inflammation (tissue/blood eosinophilia, high ECP, high IgE, high IL-5) is a consistent predictor of relapse [1,4,44,45] | |
Inflammatory endotype (type 2 inflammation, high IL-5/IL-13) is associated with more severe, recurrent disease [24,35,46,47,48,49,50] | |
Non-type 2 or mixed inflammation (eosinophilic-neutrophilic) is more prevalent in Asian cohorts, with variable prognosis [8,11,51] | |
Surgical Factors | Incomplete/opening of key sinuses and residual disease (missed cells, structures) increase recurrence risk [21,32,33,52,53,54] |
Surgeon’s experience: high-volume surgeons have lower recurrence rates post-ESS [40,55] | |
Insufficient postoperative management and inflammation control (poor healing, inadequate steroids) raise recurrence rates [7,22,47,48] | |
High postoperative endoscopy scores (e.g., Lund-Mackay) within 3–6 months signal higher likelihood of relapse [7,9,57] |
Model | Parameters | Highlights | Limitations |
---|---|---|---|
Clinical Predictors and Symptom-Based Models | Comorbidities: Asthma, allergic rhinitis, NSAID-exacerbated respiratory disease (NERD), previous surgery Symptom Trajectory: SNOT-22 change over 3–12 months Surgery Interval: Shorter interval between surgeries | Strong, consistent clinical predictors for recurrence and revision surgery Dynamic symptom tracking (e.g., SNOT-22) offers added prognostic value | Often region and tool- dependent; symptoms alone have moderate accuracy [7,30,58,59,60,61,62,63,64,65,66,67] |
Serologic & Immunologic Biomarkers | Peripheral eosinophil, basophil counts, ELR, NLR, serum ECP Cytokines: Interleukins (e.g., IL-5, IL-13), eotaxin, complement proteins Immune cell subsets: Regulatory T cells, innate lymphoid cells | Eosinophil counts (peripheral and tissue) reproducibly linked with recurrence Cytokines/immune markers refine endo- typing and risk assessment | Cut-offs not standardized; moderate predictive accuracy [48,68,69,70,71,72,73,74,75,76,77,78] |
Histologic Markers | Tissue eosinophilia (e.g., >55/HPF or ≥27%) Charcot-Leyden crystals IL-5, IL-13 expression, dense eosinophil/mast cell infiltration Eosinophilic mucin | Robust predictor in Asian cohorts Mast cell burden linked to early recurrence | Thresholds debated; invasive sampling often required [9,59,70,79,80,81,82,83,84] |
Radiologic Predictors | Lund-Mackay CT score (LM) Ethmoid-to-maxillary opacification ratio Radiologic endotypes (e.g., CCAD, AFRS, AERD forms) | Higher LM/ethmoid dominance: poorer prognosis CCAD: lower recurrence despite ethmoid- predominance | Severity interpretation must consider disease endotype [85,86,87,88,89,90] |
Microbiota | Staphylococcus aureus colonization Specific nasal/rectal microbial signatures | S. aureus carriage linked to higher recurrence risk Microbial diversity as possible risk modifier | Research emerging; not yet in routine use [91,92,93] |
Integrated and Machine Learning (ML)-Based Models | Multivariable models combining: Symptoms (VAS, SNOT-22), radiology (LM), comorbidities, eosinophils, cytokines ML using miRNAs, cytokines, medication history | Superior predictive accuracy (e.g., >80%) Asia: logistic regression, nomograms Western: multivariable and ML models | No single biomarker suffices; integration improves accuracy [7,18,94,95] |
Feature | Western Populations | Asian Populations | Clinical Implications |
---|---|---|---|
Dominant Inflammatory Endotype | Predominantly type 2 (eosinophilic) inflammation | More heterogeneous; higher non-type 2 or mixed eosinophilic-neutrophilic | Endotype-driven treatment essential; differences affect biologic response and prediction models [5,6,24,46] |
Asthma Prevalence | High, strong association with recurrence | High, especially in combination with allergic rhinitis | Asthma co-management improves outcomes; must be factored into risk models [18,24,35,40,46,47,48,50] |
AERD Prevalence | Higher prevalence; linked to severe disease and >50% recurrence | Less frequently reported; role still relevant | Predicts severe, treatment-resistant CRS; influences surgery and biologic choice [3,21,47,94] |
Recurrence Rates | 12–76.6%, often >30% in high-risk groups | 13.6–65.3%, most between 25–45% | Regional data must be interpreted in context of definitions and follow-up [11,12,13,14,15,16] |
Risk Factors | Asthma, AERD, eosinophilia, CF, female sex, smoking | Tissue/blood eosinophils, uric acid, metabolic syndrome, BMI, younger age | Tailored risk stratification and monitoring required for comorbidities [3,18,20,22,28,37,39,40,41] |
Predictive Models | Integrated models: radiologic scores, symptoms, history, biomarkers; ML-based models emerging | Emphasis on inflammatory biomarkers (e.g., eosinophils, IL-6/IL-8); logistic regression and nomograms | Region-specific models needed; integrated data improves prediction [7,8,18,94,95] |
Biologic Use | Widely used; early integration with surgery and topical steroids | Limited use; reserved for type 2 patients due to cost/regulation | Access disparity impacts treatment outcomes and equity [5,6,25,101,115] |
Surgical Revision Patterns | More frequent; Guide-lines support earlier revision for T2 inflammation | More conservative; lower revision rates and delayed intervention | Careful assessment of candidacy and timing critical; cultural/systemic constraints matter [20,55,105,106,107,115] |
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© 2025 by the authors. Published by MDPI on behalf of the Lithuanian University of Health Sciences. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Chen, Y.-S.; Feng, C.-Y.; Su, S.-H.; Wang, Y.-H.; Yang, T.-H.; Lin, C.-F. Recurrence of Chronic Rhinosinusitis with Nasal Polyps After Surgery: Risk Factors, Predictive Models, and Treatment Approaches with a Focus on Western and Asian Differences. Medicina 2025, 61, 1620. https://doi.org/10.3390/medicina61091620
Chen Y-S, Feng C-Y, Su S-H, Wang Y-H, Yang T-H, Lin C-F. Recurrence of Chronic Rhinosinusitis with Nasal Polyps After Surgery: Risk Factors, Predictive Models, and Treatment Approaches with a Focus on Western and Asian Differences. Medicina. 2025; 61(9):1620. https://doi.org/10.3390/medicina61091620
Chicago/Turabian StyleChen, Yi-Shyue, Chi-Yu Feng, Shih-Hao Su, Yu-Han Wang, Ting-Hua Yang, and Chih-Feng Lin. 2025. "Recurrence of Chronic Rhinosinusitis with Nasal Polyps After Surgery: Risk Factors, Predictive Models, and Treatment Approaches with a Focus on Western and Asian Differences" Medicina 61, no. 9: 1620. https://doi.org/10.3390/medicina61091620
APA StyleChen, Y.-S., Feng, C.-Y., Su, S.-H., Wang, Y.-H., Yang, T.-H., & Lin, C.-F. (2025). Recurrence of Chronic Rhinosinusitis with Nasal Polyps After Surgery: Risk Factors, Predictive Models, and Treatment Approaches with a Focus on Western and Asian Differences. Medicina, 61(9), 1620. https://doi.org/10.3390/medicina61091620