Prediction Score of Pseudomonas Aeruginosa Isolation in Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Thotsaporn Morasert,1,2 Pichaya Tantiyavarong1 1Department of Clinical Epidemiology, Faculty of Medicine, Thammasat University, Pathum Thani, 12120, Thailand; 2Pulmonary and Critical Care Medicine, Department of Medicine, Suratthani Hospital, Surat Thani, 84000, ThailandCorrespondence: Pichaya Tantiyavarong, Department of Clinical Epidemiology, Faculty of Medicine, Thammasat University, 99/209 Moo 18, Phaholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand, Tel +66-89-7711576, Email [email protected]: Pseudomonas aeruginosa (PA) is an important pathogen in acute exacerbations of chronic obstructive pulmonary disease (AECOPD), yet empirical anti-PA coverage is often prescribed inappropriately. Accurate prediction tools are needed to guide targeted therapy.Methods: We conducted a retrospective cohort study of hospitalized AECOPD patients at a regional referral hospital in Thailand (2015– 2018). Clinical, radiographic, and laboratory data were analyzed to identify independent predictors of PA isolation. Multivariable logistic regression was used to develop a prediction score, which was internally validated using bootstrap resampling. Model performance was assessed by the area under the receiver operating characteristic curve (AuROC).Results: Among 1,348 admissions (771 patients), P. aeruginosa was isolated in 167 cases (12.4%). A simplified prediction score was developed based on four independent predictors: home nebulizer use, prior anti-PA antibiotic exposure within 1 year, radiographic consolidation, and elevated serum bicarbonate. The prediction score achieved an AuROC of 0.66 (95% CI 0.62– 0.70) with good calibration after internal validation. Risk groups were defined as low (0– 1 points, < 12% risk), intermediate (2– 3 points, 16– 23% risk), and high (4– 6 points, ≥ 32% risk).Conclusion: This simplified prediction score, developed in hospitalized AECOPD patients, may aid clinicians in guiding empirical antibiotic selection by reducing unnecessary anti-PA coverage in low-risk cases while ensuring timely treatment for those at high risk.Keywords: AECOPD, acute exacerbations, chronic obstructive pulmonary disease, pseudomonas aeruginosa, predictive model, prediction score