Allergic rhinitis (AR) and laryngopharyngeal reflux (LPR) are common chronic diseases in otolaryngology, with global prevalence rates of 10%–40% and 10%–20%, respectively. The prevalence of AR among adults in China is 17.6, and 30%–50% of them are complicated with LPR. The co-occurrence of AR and LPR creates a substantial disease burden that significantly impacts quality of life, with overlapping symptoms including nasal congestion, throat irritation, chronic cough, and voice changes that often lead to misdiagnosis and delayed treatment (1). This comorbidity results in increased healthcare utilization, prolonged symptom duration, and substantial economic costs due to frequent medical visits, multiple diagnostic procedures, and polypharmacy approaches.

Currently, there are key challenges in clinical treatment: Approximately 30%–50% of AR patients complicated with LPR respond poorly to standard therapies (such as nasal corticosteroids combined with proton pump inhibitors), and there is significant heterogeneity in therapeutic efficacy. This difference may be related to factors such as BMI, allergy burden, and immune status. Therefore, it is urgent to construct a quantitative model to predict individualized treatment responses. The latest mechanism research reveals that the two share a Th2-type immune microenvironment. An increase in serum total IgE not only indicates AR but also enhances reflux sensitivity through the mast cell-esophageal mucosa axis (2, 3).

There are also major challenges in clinical diagnosis: The misdiagnosis rate of existing questionnaires and single indicators (such as serum IgE and esophageal pH monitoring) reaches 40%, and there is a lack of multi-dimensional assessment tools. This study intends to construct a Nomogram prediction model for treatment efficacy based on AR cohort by integrating clinical symptoms and immune indicators (IgE, eosinophil count) (3, 4). This visual tool can quantify individual differences in treatment efficacy, achieve one-stop clinical assessment, and reveal key driving factors through weight analysis, providing a basis for stratified intervention. This study may contribute to addressing knowledge gaps in predicting AR complicated with LPR and could potentially inform the development of more precise diagnostic and therapeutic approaches for allergic diseases.

Current clinical diagnosis for the concurrent presentation of allergic rhinitis (AR) and laryngopharyngeal reflux (LPR) faces several dilemmas. Traditional assessments rely heavily on subjective symptom questionnaires, and reliance on single indicators (e.g., serum IgE, esophageal pH monitoring) is associated with a misdiagnosis rate as high as 40%, highlighting a lack of integrated, multi-dimensional assessment tools. While the precise synergistic mechanisms between AR and LPR remain to be fully elucidated, existing evidence suggests interactive pathways, including: (1) Nasal congestion caused by AR forces patients to breathe through the mouth for a long time, weakening the function of the upper esophageal sphincter and increasing the risk of reflux; (2) Inflammatory mediators such as histamine and leukotrienes from post-nasal drip directly stimulate the pharyngeal mucosa, inducing local inflammation; (3) Excessive activation of the vagus nerve can simultaneously lead to hyperreactivity of the nasal mucosa and relaxation of the lower esophageal sphincter, forming an inflammation-reflux vicious cycle. The latest research on immune mechanisms has found that AR and LPR share a Th2-type immune microenvironment. An increase in total serum IgE is not only a marker of AR but also can enhance reflux sensitivity through the mast cell-esophageal mucosa axis (7). Therefore, constructing a prediction model for the treatment efficacy of patients with AR complicated with LPR can not only fill the gap in current diagnostic tools but also achieve the goal of precision medicine of early identification and early intervention by quantifying individual efficacy differences, providing a scientific basis for clinical stratified management.

In this study, based on a cohort of 106 AR patients, a Nomogram prediction model for the treatment efficacy of patients with AR complicated with LPR was constructed and validated by randomly dividing the training set (73 cases) and the validation set (33 cases) at a ratio of 7:3. The model construction process followed strict statistical procedures: First, 11 potentially relevant factors (BMI, smoking history, AR course, etc.) were screened out through univariate Logistic regression, and further multivariate analysis determined that BMI, AR course, the number of allergens and total serum IgE were independent influencing factors (p < 0.05). This screening process excluded the interference of confounding factors, ensuring the scientific nature of the model variables. The AUC in the training set and the validation set was 0.809 and 0.823 respectively, and the lower limits of the 95% confidence intervals were both > 0.6, indicating that the model had strong stability in different samples. The calibration curve showed that the predicted values fitted well with the actual observed values. The mean absolute errors in the training set and the validation set were 0.168 and 0.182 respectively, and the p values of the Hosmer–Lemeshow test were both > 0.05, further supporting the reliability of the model. Decision curve analysis showed that when the threshold probability was between 0.08 and 0.78, the net benefit of the model was significantly better than the “treat all” or “treat none” strategies, suggesting its clinical practicability. The visual feature of the Nomogram model is its significant advantage. By assigning weights to each independent influencing factor, clinicians can quickly calculate the probability of individual efficacy differences, achieving one-stop assessment. For example, for a patient with a BMI of 25 kg/m², an AR course of 10 years, 4 types of allergens, and a total serum IgE of 400 IU/mL, the predicted probability of ineffective treatment is about 75%, suggesting that early anti-reflux treatment (such as proton pump inhibitors) should be considered and the AR management plan should be re-evaluated (in combination with validated symptom scores such as TNSS, disease control status, and current allergen exposure) (8). It should be emphasized that the model is a supportive tool for clinical decision-making, and adjustments to nasal corticosteroid doses should be guided by comprehensive clinical assessment rather than the model alone. This quantitative assessment mode helps to break the limitations of traditional empirical medicine and promote the precision diagnosis and treatment of AR-LPR comorbidity.

This study showed that for every 1-unit increase in BMI, the risk of ineffective treatment increased by 30% (OR = 1.300, 95% CI: 1.061–1.594). This result is consistent with the classic theory that obesity causes reflux: an increase in intra-abdominal pressure in obese individuals can directly lead to an imbalance in the pressure of the lower esophageal sphincter, and inflammatory factors (such as TNF-α and IL-6) secreted by adipose tissue may exacerbate the sensitivity of the esophageal mucosa (9). In addition, obese patients often have comorbid sleep apnea, and the proportion of supine sleep increases (in this study, the proportion of supine sleep in the ineffective group was 78.92%), further promoting nocturnal reflux events (10). In clinical practice, weight control should be used as a basic intervention measure for AR patients to prevent LPR (11). For every 1-year extension of the AR course, the risk of ineffective treatment increased by 30.6% (OR = 1.306). Long-term nasal mucosal inflammation can continuously damage the upper esophageal mucosa through the post-nasal drip-pharyngeal stimulation pathway, and the chronic inflammatory state may induce vagus nerve remodeling, reducing the anti - reflux barrier function of the esophagus (12). In this study, the average AR course in the ineffective group was 8.52 years, significantly longer than that in the effective group (6.82 years), suggesting that early intervention in AR to shorten the course may block its pathological progression to LPR (13). For every 1-type increase in the number of allergens, the risk of ineffective treatment increased by 1.629 times (OR = 2.629), which reflects a positive correlation between the allergic load and reflux susceptibility (14). Exposure to multiple allergens can activate a stronger Th2-type immune response, leading to an increase in serum IgE and eosinophil infiltration, and then promoting LPR through the following pathways: (1) Inflammatory mediators (such as tryptase) directly damage the esophageal mucosal barrier; (2) Cytokines (such as IL - 13) released by immune cells upregulate the expression of TRPV1 ion channels in esophageal epithelial cells, enhancing the sensitivity to acid reflux (15). Clinically, for AR patients sensitized to multiple allergens, potential differences in LPR treatment efficacy should be vigilant (16). For every 1-IU/mL increase in total serum IgE, the risk of ineffective treatment increased by 0.6% (OR = 1.006), suggesting a potential role for IgE in the allergy-reflux interaction (17). IgE may indirectly influence esophageal mucosal sensitivity through mast cell activation and release of inflammatory mediators such as histamine (18). Furthermore, some studies suggest that immune mediators may affect esophageal smooth muscle function, though the precise mechanisms in reflux require further investigation (19). In this study, the mean serum IgE in the ineffective group was 320.32 IU/mL, 22.8% higher than that in the effective group, suggesting that monitoring IgE levels can be used as an important indicator for efficacy difference stratification. Acidic reflux substances directly damage the pharyngeal mucosa, triggering an inflammatory response and squamous metaplasia, and the pharyngeal mucosa of AR patients is in a hypersensitive state due to long-term inflammation, and its tolerance to acid stimulation is significantly reduced (20).

This study has the following limitations: First, the sample size was small (a total of 106 cases) and the data were from a single center, leading to moderate specificity (training set: 0.520; validation set: 0.625) and wide 95% confidence intervals for AUC (e.g., validation set 95% CI: 0.630–1.000). These factors may limit the external validity of the model, and external validation in multi-center, larger independent cohorts is required to confirm its generalizability across different geographical and ethnic populations; Second, non-acidic reflux monitoring (such as impedance-pH monitoring) was not included, which may have missed some LPR patients with non-acid reflux as the main symptom; In addition, long-term follow-up was not conducted, and the ability of the model to predict long-term efficacy differences in LPR is not clear. The main reason for not conducting external validation was that the sample collection time was short within the research period (from January 2023 to November 2024), and the amount of single-center data was limited, making it difficult to obtain an independent external cohort. In the future, multi-center, large-sample studies are needed to integrate multi-modal data such as impedance-pH monitoring and esophageal manometry, and include lifestyle questionnaires (such as diet structure and exercise habits) to further optimize the model efficacy. At the same time, a risk warning APP can be constructed based on the model to achieve dynamic risk assessment through real-time data collection (such as sleep posture recorded by a smart bracelet), promoting the intelligent and personalized management of AR-LPR comorbidity. This study did not include detailed treatment compliance data, which may have influenced the observed efficacy and the model’s predictions. Future studies should aim to collect such data to further refine predictive models. Additionally, we did not perform endoscopic or histopathological examinations (the gold standard for EoE diagnosis) to systematically rule out EoE, which may introduce residual confounding due to overlapping symptoms between EoE and LPR. Future studies should include endoscopic/histopathological data to further improve the accuracy of patient enrollment. Finally, the model predicts efficacy based on the improvement in LPR symptoms (RSI). Future research aiming to predict the global response in patients with concurrent AR and LPR should consider integrating validated, disease-specific outcome measures for both conditions.

In conclusion, the Nomogram model constructed in this study successfully built a visual prediction tool for the treatment efficacy of patients with AR complicated with LPR by integrating multi-dimensional indicators such as BMI, AR course, the number of allergens, total serum IgE, and esophageal pH monitoring. The model not only has good discrimination and calibration but also its visual features are more convenient for clinical application. The analysis of each independent factor reveals the pathological chain of obesity-inflammation-reflux, suggesting that patients with high BMI, long-term disease, multiple allergies, and high IgE levels should be strengthened in reflux monitoring and early intervention. Despite the limitations of sample size and external validation, this study still provides a new paradigm for the interdisciplinary management of naso-pharyngeal-esophageal axis diseases and is expected to promote the development of a precision diagnosis and treatment system for allergic diseases.