M S Abers1, D M Musher2. 1. From the Department of Medicine, Baylor College of Medicine and Medical Care Line, Infectious Diseases Section, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA abers@bcm.edu. 2. From the Department of Medicine, Baylor College of Medicine and Medical Care Line, Infectious Diseases Section, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USAFrom the Department of Medicine, Baylor College of Medicine and Medical Care Line, Infectious Diseases Section, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
Abstract
A variety of prediction scores have been developed to identify at the time of presentation patients with community-acquired pneumonia at risk for intensive care unit (ICU) admission or death within 30 days. The effectiveness of each scoring score is typically assessed by calculation of the area under the receiver-operator characteristic curve (AUROC). Although this statistical parameter is helpful in determining the discriminatory value of a score, it assumes equal importance of false negatives and false positives in the tradeoff between sensitivity and specificity. Because patient safety takes precedence over cost, the balance between limiting false negatives (unnecessarily strict ICU admission policy) and false positives (unnecessarily liberal ICU admission policy) should favor the reduction of false negatives. Instead of using AUROC as the primary measure to evaluate prediction rules, we propose the use of sensitivity as a more appropriate alternative.
A variety of prediction scores have been developed to identify at the time of presentation patients with community-acquired pneumonia at risk for intensive care unit (ICU) admission or death within 30 days. The effectiveness of each scoring score is typically assessed by calculation of the area under the receiver-operator characteristic curve (AUROC). Although this statistical parameter is helpful in determining the discriminatory value of a score, it assumes equal importance of false negatives and false positives in the tradeoff between sensitivity and specificity. Because patient safety takes precedence over cost, the balance between limiting false negatives (unnecessarily strict ICU admission policy) and false positives (unnecessarily liberal ICU admission policy) should favor the reduction of false negatives. Instead of using AUROC as the primary measure to evaluate prediction rules, we propose the use of sensitivity as a more appropriate alternative.