A Prediction Rule to Identify Low-Risk Patients with Community-Acquired Pneumonia
- 23 January 1997
- journal article
- research article
- Published by Massachusetts Medical Society in New England Journal of Medicine
- Vol. 336 (4) , 243-250
- https://doi.org/10.1056/nejm199701233360402
Abstract
There is considerable variability in rates of hospitalization of patients with community-acquired pneumonia, in part because of physicians' uncertainty in assessing the severity of illness at presentation. From our analysis of data on 14,199 adult inpatients with community-acquired pneumonia, we derived a prediction rule that stratifies patients into five classes with respect to the risk of death within 30 days. The rule was validated with 1991 data on 38,039 inpatients and with data on 2287 inpatients and outpatients in the Pneumonia Patient Outcomes Research Team (PORT) cohort study. The prediction rule assigns points based on age and the presence of coexisting disease, abnormal physical findings (such as a respiratory rate of >30 per minute or a temperature of >40°C), and abnormal laboratory findings (such as a pH 30 mg per deciliter [11 mmol per liter] or a sodium concentration <130 mmol per liter) at presentation. There were no significant differences in mortality in each of the five risk classes among the three cohorts. Mortality ranged from 0.1 to 0.4 percent for class I patients (P = 0.22), from 0.6 to 0.7 percent for class II (P = 0.67), and from 0.9 to 2.8 percent for class III (P = 0.12). Among the 1575 patients in the three lowest risk classes in the Pneumonia PORT cohort, there were only seven deaths, of which only four were pneumonia-related. The risk class was significantly associated with the risk of subsequent hospitalization among those treated as outpatients and with the use of intensive care and the number of days in the hospital among inpatients. The prediction rule we describe accurately identifies the patients with community-acquired pneumonia who are at low risk for death and other adverse outcomes. This prediction rule may help physicians make more rational decisions about hospitalization for patients with pneumonia.Keywords
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