Abstract
OBJECTIVE: Derive and externally validate a prediction model for pneumococcal urinary antigen test (pUAT) positivity. METHODS: Retrospective cohort study of adults admitted with community-acquired pneumonia (CAP) to 177 U.S. hospitals in the Premier Database (derivation and internal validation samples) or 12 Cleveland Clinic hospitals (external validation sample). We utilized multivariable logistic regression to predict pUAT positivity in the derivation dataset, followed by model performance evaluation in both validation datasets. Potential predictors included demographics, comorbidities, clinical findings, and markers of disease severity. RESULTS: Of 198,130 Premier patients admitted with CAP, 27,970 (14.1%) underwent pUAT; 1962 (7.0%) tested positive. The strongest predictors of pUAT positivity were history of pneumococcal infection in the previous year (OR 6.99, 95% CI 4.27-11.46), severe CAP on admission (OR 1.76, 95% CI 1.56-1.98), substance abuse (OR 1.57, 95% CI 1.27-1.93), smoking (OR 1.23, 95% CI 1.09-1.39), and hyponatremia (OR 1.35, 95% CI 1.17-1.55). Negative predictors included IV antibiotic use in past year (OR 0.65, 95% CI 0.52-0.82), congestive heart failure (OR 0.72, 95% CI 0.63-0.83), obesity (OR 0.71, 95% CI 0.60-0.85), and admission from skilled nursing facility (OR 0.60, 95% CI 0.45-0.78). Model c-statistics were 0.60 and 0.67 in the internal and external validation cohorts, respectively. Compared to guideline-recommended testing of severe CAP patients, our model would have detected 23% more cases with 5% fewer tests. CONCLUSION: Readily available data can identify patients most likely to have a positive pUAT. Our model could be incorporated into automated clinical decision support to improve test efficiency and antimicrobial stewardship.