A novel risk score for disease control prediction of chronic rhinosinusitis

Abstract Objectives To assess the impact of risk factors on the disease control among chronic rhinosinusitis (CRS) patients, following 1 year of functional endoscopic sinus surgery (FESS), and combining the risk factors to formulate a convenient, visualised prediction model. Design A retrospective and nonconcurrent cohort study. Setting and Participants A total of 325 patients with CRS from June 2018 to July 2020 at the First Affiliated Hospital of Sun Yat‐sen University, the Third Affliated Hospital of Sun Yat‐sen University, the Seventh Affiliated Hospital of Sun Yat‐sen University. Main Outcomes Measures Outcomes were time to event measures: the disease control of CRS after surgery 1 year. The presence of nasal polyps, smoking habits, allergic rhinitis (AR), the ratio of tissue eosinophil (TER) and peripheral blood eosinophil count (PBEC) and asthma was assessed. The logistic regression models were used to conduct multivariate and univariate analyses. Asthma, TER, AR, PBEC were also included in the nomogram. The calibration curve and area under curve (AUC) were used to evaluate the forecast performance of the model. Results In univariate analyses, most of the covariates had significant associations with the endpoints, except for age, gender and smoking. The nomogram showed the highest accuracy with an AUC of 0.760 (95% CI, 0.688–0.830) in the training cohort. Conclusions In this cohort study that included the asthma, AR, TER, PBEC, which had significantly affected the disease control of CRS after surgery. The model provided relatively accurate prediction in the disease control of CRS after FESS and served as a visualised reference for daily diagnosis and treatment.


| INTRODUCTION
Chronic rhinosinusitis (CRS) is a multifactorial heterogeneous disease, although its pathogenesis and precise mechanism remains largely unclear. Due to the poor understanding of the pathophysiology of CRS, it affects the quality of life of patients and increases the cost burden as compared to people without CRS. It is estimated to affect 8% of the adult population in China. 1 According to the EPOS2020, the current treatment for CRS includes medical therapy and FESS with the final target to achieve cure or clinical control. 2 Although, the disease state of more than 30% of patients with nasal polyps, remains uncontrolled despite the current medical therapy (AMT) and FESS. 3 DeConde et al. also reported the disease relapse in 40% of patients with nasal polyps after 18 months. 4 The latest evidence has further indicated that the underlying diversity of endotypes might be a crucial reason for the unconformity in clinical phenotype and disease prognosis. 5 Therefore, it is essential to find relevant clinical markers and to make a convenient model to predict the poor disease control in CRS.
Emerging evidence has proven that eosinophil (EOS) inflammation is a dominant factor associated with CRS recurrence and poor disease control. 6 In addition to the local eosinophils, peripheral blood eosinophils are also associated with CRS and can be a reliable marker for predicting the prognosis of CRS. Some studies have demonstrated the peripheral blood eosinophil as a marker for the EOS CRS. 6 In a recent study, Guiherme et al., suggested that asthma was a dominant factor for the recurrence of CRS. 7  Items recorded from the enrolled patients were as following: • Nasal symptoms; • Lund and Kennedy score recorded by nasal endoscopy findings; • Comorbidities: smoking habit, asthma (based on the spirometry and clinical parameters); • Respiratory allergens; • Peripheral blood eosinophil count before the initiation of oral corticosteroids. More than 0.3 Â 10 9 /L was considered as high blood eosinophilia in CRS.

| Data collection
Patients were divided into two groups of controlled (included partly controlled) and uncontrolled CRS, based on the disease control criteria of EPOS2020. Patients were followed up for 1 year after surgery, until the end of the study period (30th December 2020). Time-to-event was defined as the time starting from surgery till the 12th month post-operatively. According to the EPOS2020, the control criteria of the CRS can be divided into symptoms, nasal endoscopy, the need for recuse treatment. Symptom substituted by 'VAS (Visual Analogue Scale) < 5', and 'present/impaired' by 'VAS ≥5'. Furthermore, the detailed symptoms related to CRS are included in supplement Table   S1. The evaluation endpoint was 12th month post-operatively.

| Nomogram development
The nomogram model was formulated by the results of multivariate analysis. Univariate analysis with a significant difference at p-value (<.05) between all variables was included in the multivariate analysis.
The p-value <.05 in multivariate analysis was also included as the prog- Cox proportional hazard model was used to produce nomograms for predicting the risk of the uncontrolled incident after the surgery. A score based on regression coefficients was assigned to these factors.

| Model evaluation
The nomogram's forecast performance was evaluated by the receiver

| Statistical analysis
We compared the patient pathologic characteristics and demographic profile between training and validation cohort by using Fisher's exact  The baseline characteristics of the CRS patients between the training cohort and validation cohort are shown in

| Nomogram development
After the initial univariate analyses with extensive review of the medical literature, we included all the covariates in the subsequent multivariate logistic regression models, except for age, gender, smoking, tissue eosinophil counts, preoperative Lund Kennedy score and Lund Mackay score.
Based on these factors, the nomogram was constructed for calculating the risk of recurrence of the CRS after operation 1 year ( Figure 1A).
A case demonstrating our nomogram usage is shown in Figure 1B.
For example, if the patient had tissue eosinophil ratio ≥10%, low blood eosinophilia, no AR and asthma, then the total points would be 196 with the corresponding risk of recurrence at 46.11%.

| DISCUSSION
CRS is a group of multifactorial diseases, associated with asthma, allergy, high tissue eosinophil ratio and blood eosinophil counts. CRS is generally treated by pharmacotherapy or by FESS. 9 In this study,

| Asthma
In 2012, a multicentre study conducted by the Global Allergy and Asthma Network of Excellence (GA 2 LEN) showed that asthma was associated with CRS in all age groups, irrespective of gender and smoking behaviour. 10 Our group previously reported that extensive endoscopic sinus surgery (EESS) improved the surgery outcomes in CRS with asthma. 11 In a 12-year study, asthma was identified as the only factor that increased the chance of recurrence in patients with either CRSwNP or CRSsNP (chronic rhinosinusitis without nasal polyps). 7,12 Our current study also showed that asthma was the important factor for disease control after surgery, as demonstrated in univariate and multivariate analysis. In the training cohort, the AUC of the asthma models was 0.665 (0.593-0.737).
However, CRS with or without asthma is an indisputable element affecting its prognosis.

| Allergy
The causal relationship between allergy and CRS is still debatable, however, the risks of CRSwNP are higher in patients with coexisting allergy and asthma conditions 10 A population-based study reported the AR higher prevalence, before the diagnosis of CRSsNP or CRSwNP in comparison with patients without CRS. 13

| System and local eosinophil
The EPOS2020 and several studies reported the cut-off points for EOS in blood and tissue. We classified the cohort subjects by using 0.3 Â 10 9 /L as a cut-off value for blood EOS counts and 10% for polyp tissue EOS percentages. 2 The cut-off point of 10% tissue EOS has been extensively used for differentiating the eosinophilic CRS. 18 Lou et al. and Nakayama et al. have also demonstrated a strong correlation between polyp recurrence and tissue EOS numbers. 19,20 Blood EOS can also reflect the prognosis of chronic sinusitis, but its sensitivity is low as compared to the tissue EOS. 21,22 Our group has reported that the tissue and blood eosinophilia has an additive effect in predicting the risk of poor disease control after at least 1 year of FESS. 23 This study further demonstrated using multivariate analysis, that the tissue eosinophilia ratio was an independent factor, affecting the disease control after surgery. The analysis revealed that the number of eosinophils in tissues had no significant effect on CRS disease control. However, EPOS 2020 suggests that tissue eosinophils can be considered as nasal polyps eosinophils in case the tissue eosinophils count was more than 10. 24 In many pieces of literature, tissue eosinophils ratio was still higher than 10% as the cut-off value to predict the prognosis of chronic sinusitis nasal polyps. 22 Therefore, we only included tissue eosinophil ratio (TER) in our Nomogram prediction model.
So far, few studies have focused on the various combination factors among asthma (AS), PBEC, TER, AR and disease control. Interestingly, in our study, the combination of AS, AR, TER, PBEC significantly increased the odds ratio for predicting the possibility of uncontrolled and partly controlled disease. To the best of our knowledge, this observation has not been reported in the literature. Therefore, as the potential predictors, we included allergy, asthma, TER and blood EOS counts, among the various demographic factors in our nomograms.
For a long, these factors have been recognised to have a significant impact on the disease control of CRS.
This study also had some limitations due to the small cohort size.
In addition, childhood-onset or adult-onset asthma in CRSwNP were not confirmed. Further, we could not evaluate the relationship between the prognosis of disease the childhood or adult-onset asthma.

| CONCLUSIONS
We found that TER and AS were the independent factors affecting the prognosis of CRSwNP. In combination with AR, PBEC, TER and AS, the nomogram model exhibited higher accuracy than with tissue eosinophil ratio and asthma alone. The nomogram model provided relatively accurate and visually prediction for disease control in CRS after FESS and served as a reference for the daily diagnosis and treatment.