Comparative safety of monoclonal antibodies in chronic inflammatory airway diseases (chronic sinusitis with nasal polyposis and asthma): A network meta-analysis.

OBJECTIVE: Several monoclonal antibodies (MoAbs) targeting specific type 2 immune reactions have been developed as innovative therapeutic approaches for chronic inflammatory airway diseases, such as chronic sinusitis with nasal polyps (CRSwNP) and asthma. However, the clinical safety of these MoAbs and how to choose them are not clear. Therefore, we aimed to assess the systemic drug- and dose-based safety of MoAbs in chronic airway inflammation using network meta-analysis (NMA). METHODS: Electronic databases were systematically searched for relevant studies published in English between January 2009 and December 2022. Eligible studies must have clearly reported adverse events (AEs) among the MoAbs' safety data. RESULTS: 1). Regarding serious AEs, mepolizumab was significantly safer than placebo; in terms of permanent treatment discontinuation, reslizumab and dupilumab were significantly safer than benralizumab. 2). Regarding asthma worsening, dupilumab was associated with the best safety profile; was safer than dupilumab/300 mg/q2-4w. 3). In terms of injection-site reactions, dupilumab posed a higher risk than placebo; dupilumab/300 mg/qw posed a higher risk than dupilumab/300 mg/q2w and dupilumab/300 mg/q2-4w; lebrikizumab/250 mg/q4w posed a higher risk than lebrikizumab/37.5 mg/q4w; mepolizumab/100 mg/q4w posed a higher risk than mepolizumab/75 mg/q4w; benralizumab/30 mg/q4-8w posed a higher risk than benralizumab/20 mg/q4-8w. 4) In CRSwNP patients combined with asthma, the risks of experiencing AEs were not increased. CONCLUSION: Overall, biologics are safe and well tolerated in chronic inflammatory airway disease. This drug- and dose-based NMA provides further evidence on the different safety profiles of different emerging MoAbs. This information may help guide rational drug use and provide clinical recommendations for choosing MoAbs. TRIAL REGISTRATION: SYSTEMATIC REVIEW REGISTRATION (PROSPERO #CRD42023387610).

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

Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making.