Prescription patterns and clinical rationales for corticosteroid use in respiratory infections: A cross sectional survey of medical specializations

INTRODUCTION The use of corticosteroids in respiratory infections remains a topic of clinical debate, particularly regarding indications, frequency, and alignment with patient phenotypes. We aimed to analyze prescribing patterns, decision-making processes, and perceived efficacy of corticosteroids across a diverse group of physicians. METHODS Within a cross-sectional survey, we analyzed responses from 203 physicians across various medical specializations, with a majority representing pulmonology (73.89%). The survey was conducted in Italy between March and June 2024 using an anonymous, structured online questionnaire. Eligible participants were practicing physicians with at least one year of experience in managing respiratory infections; trainees and non-clinical professionals were excluded. An anonymous, structured questionnaire explored prescribing rationales, frequencies, inflammatory phenotype considerations, and biomarker utilization. Data were analyzed using descriptive statistics and chi-squared tests, with statistical significance set at p<0.05. RESULTS Key findings revealed that inflammation reduction (60.59%) was the predominant reason for corticosteroid use, with eosinophilic inflammation guiding prescriptions in 39.90% of cases. Inhaled corticosteroids were generally preferred over oral formulations (60.10%). Despite a reliance on clinical judgment (44.33%), biomarker-driven approaches remain underutilized. Statistically significant differences were observed in phenotype-based prescribing and biomarker monitoring, indicating variability in decision-making. CONCLUSIONS These findings underscore the need for standardized guidelines and personalized therapeutic strategies. The observed gaps in biomarker utilization and inflammatory phenotyping highlight areas for improvement. Future research should focus on integrating standardized diagnostic approaches to enhance treatment precision and optimizing patient outcomes.