• H A Conte, Y T Chen, W Mehal, J D Scinto, and V J Quagliarello.
• Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
• Am. J. Med. 1999 Jan 1;106(1):20-8.

PurposeWe sought to identify admission characteristics predicting mortality in elderly patients hospitalized with community-acquired pneumonia and to develop a prognostic staging system and discriminant rule.Patients And MethodsWe retrospectively analyzed data from 2,356 patients aged > or = 65 years admitted with community-acquired pneumonia. Multivariable analyses of a derivation cohort (n = 1,000) identified characteristics associated with hospital mortality. A staging system and discriminant rule based on these characteristics were tested in a validation cohort (n = 1,356). Our discriminant rule was compared with a rule formulated from a heterogeneous adult population with community-acquired pneumonia.ResultsHospital mortality rates were 9% (derivation cohort) and 12% (validation cohort). We identified five independent predictors of mortality: age > or = 85 years [odds ratio 1.8 (95% confidence interval 1.1-3.1)], comorbid disease [odds ratio 4.1 (2.1-8.1)], impaired motor response [odds ratio 2.3 (1.4-3.7)], vital sign abnormality [odds ratio 3.4 (2.1-5.4)], and creatinine level > or = 1.5 mg/dL [odds ratio 2.5 (1.5-4.2)]. These variables stratified patients into four distinct stages with increasing mortality in the derivation cohort (Stage 1, 2%; Stage 2, 7%; Stage 3, 22%; Stage 4, 45%; P = 0.001) as well as in the validation cohort (Stage 1, 4%; Stage 2, 11%; Stage 3, 23%; Stage 4, 41%; P = 0.001). The discriminant rule developed from the derivation cohort had greater overall accuracy (77.1%) in the validation cohort than a rule formulated from a heterogeneous adult population (68.0%, P = 0.001).ConclusionElderly patients with community-acquired pneumonia have characteristics at admission that can predict mortality. Our staging system and discriminant rule improve prognostic stratification of these patients.

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