Prediction Score of Pseudomonas Aeruginosa Isolation in Acute Exacerbations of Chronic Obstructive Pulmonary Disease
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Thotsaporn Morasert1,2, Pichaya Tantiyavarong1
1Department of Clinical Epidemiology, Faculty of Medicine, Thammasat University, Pathum Thani, 12120, Thailand.
Background:
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.
Insights
A new prediction score helps identify Pseudomonas aeruginosa in COPD exacerbations. This tool guides antibiotic use, reducing unnecessary treatments for low-risk patients and ensuring timely care for high-risk individuals.
Area of Science:
- Infectious Diseases
- Pulmonology
- Clinical Microbiology
Background:
- Pseudomonas aeruginosa (PA) is a significant pathogen in acute exacerbations of chronic obstructive pulmonary disease (AECOPD).
- Inappropriate empirical anti-PA antibiotic coverage is common in AECOPD management.
- There is a need for accurate tools to predict PA isolation and guide targeted therapy.
Purpose of the Study:
- To develop and validate a prediction tool for identifying PA in hospitalized AECOPD patients.
- To guide empirical antibiotic selection in AECOPD management.
Main Methods:
- Retrospective cohort study of hospitalized AECOPD patients (2015-2018).
- Analysis of clinical, radiographic, and laboratory data to identify PA predictors.
- Development and internal validation of a multivariable logistic regression prediction score.
Main Results:
- Pseudomonas aeruginosa was isolated in 12.4% of AECOPD admissions.
- A simplified score based on home nebulizer use, prior anti-PA antibiotic exposure, consolidation, and elevated bicarbonate was developed.
- The score achieved an AuROC of 0.66 and defined low, intermediate, and high-risk groups.
Conclusions:
- A simplified prediction score can aid clinicians in AECOPD management.
- The score helps optimize empirical antibiotic selection, reducing unnecessary anti-PA coverage.
- Timely treatment for high-risk patients can be ensured while avoiding overtreatment in low-risk cases.
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