Objectives: This study was designed to develop a clinical prediction r
ule that identifies adult patients with acute respiratory illness who
would demonstrate a pneumonic infiltrate on a chest roentgenogram. Mat
erials and methods: The demographic, clinical, and chest radiologic da
ta of 557 adult patients (derivation set) with acute respiratory illne
ss were used to construct and to cross-validate a model using stepwise
logistic regression procedure. A prospective sample of 100 patients (
validation set) was used to validate the model. Results: The prevalenc
e of pulmonary infiltrates among the derivation and the validation set
s was 17% and 18%, respectively. The stepwise model identified documen
ted fever (>38 degrees C), rales, colored sputum, dullness to percussi
on, absence of asthma, and bronchial breathing as significant factors
that independently predict pneumonic infiltrates. The area under the r
eceiver operating characteristics was 0.81 (95% CI, 0.77 to 0.85%). Th
us, the probability that the logistic rule would correctly identify a
patient with pneumonic infiltrates was 81% and the overall sensitivity
and specificity of the rule were 78% and 94%, respectively with a tot
al error rate of 8.4%. Both cross-validation and the prospective valid
ation reaffirmed the robustness of the clinical prediction rule. Concl
usion: We concluded that the clinical prediction rule proved useful in
identifying adult patients who are having pulmonary infiltrates. The
model would be beneficial to assist physicians in deciding whether to
order a chest roentgenogram.