OBJECTIVE: To evaluate the predictive validity and calibration of the pneumonia severity-of-illness index (PSI) in patients with community-acquired pneumonia (CAP). PATIENTS: Randomly selected patients (n = 1,024) admitted with CAP to 22 community hospitals. MEASUREMENTS AND MAIN RESULTS: Medical records were abstracted to obtain prognostic information used in the PSI. The discriminatory ability of the PSI to identify patients who died and the calibration of the PSI across deciles of risk were determined. The PSI discriminates well between patients with high risk of death and those with a lower risk. In contrast, calibration of the PSI was poor, and the PSI predicted about 2.4 times more deaths than actually occurred in our population of patients with CAP. CONCLUSIONS: We found that the PSI had good discriminatory ability. The original PSI overestimated absolute risk of death in our population. We describe a simple approach to recalibration, which corrected the overestimation in our population. Recalibration may be needed when transporting this prediction rule across populations.
RCT Entities:
OBJECTIVE: To evaluate the predictive validity and calibration of the pneumonia severity-of-illness index (PSI) in patients with community-acquired pneumonia (CAP). PATIENTS: Randomly selected patients (n = 1,024) admitted with CAP to 22 community hospitals. MEASUREMENTS AND MAIN RESULTS: Medical records were abstracted to obtain prognostic information used in the PSI. The discriminatory ability of the PSI to identify patients who died and the calibration of the PSI across deciles of risk were determined. The PSI discriminates well between patients with high risk of death and those with a lower risk. In contrast, calibration of the PSI was poor, and the PSI predicted about 2.4 times more deaths than actually occurred in our population of patients with CAP. CONCLUSIONS: We found that the PSI had good discriminatory ability. The original PSI overestimated absolute risk of death in our population. We describe a simple approach to recalibration, which corrected the overestimation in our population. Recalibration may be needed when transporting this prediction rule across populations.
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