Graffelman A W, le Cessie S, Knuistingh Neven A, Wilemssen F E J A, Zonderland H M, van den Broek P J
Leiden University Medical Center, Leiden, The Netherlands.
J Fam Pract. 2007 Jun;56(6):465-70.
Prediction rules based on clinical information have been developed to support the diagnosis of pneumonia and help limit the use of expensive diagnostic tests. However, these prediction rules need to be validated in the primary care setting.
Adults who met our definition of lower respiratory tract infection (LRTI) were recruited for a prospective study on the causes of LRTI, between November 15, 1998 and June 1, 2001 in the Leiden region of The Netherlands. Clinical information was collected and chest radiography was performed. A literature search was also done to find prediction rules for pneumonia.
129 patients--26 with pneumonia and 103 without--were included, and 6 prediction rules were applied. Only the model with the addition of a test for C-reactive protein had a significant area under the curve of 0.69 (95% confidence interval [CI], 0.58-0.80), with a positive predictive value of 47% (95% CI, 23-71) and a negative predictive value of 84% (95% CI, 77-91). The pretest probabilities for the presence and absence of pneumonia were 20% and 80%, respectively.
Models based only on clinical information do not reliably predict the presence of pneumonia. The addition of an elevated C-reactive protein level seems of little value.
基于临床信息的预测规则已被开发出来,以支持肺炎的诊断并有助于限制昂贵诊断测试的使用。然而,这些预测规则需要在初级保健环境中进行验证。
1998年11月15日至2001年6月1日期间,在荷兰莱顿地区,招募符合我们下呼吸道感染(LRTI)定义的成年人,进行关于LRTI病因的前瞻性研究。收集临床信息并进行胸部X光检查。还进行了文献检索以寻找肺炎的预测规则。
纳入了129名患者,其中26名患有肺炎,103名未患肺炎,并应用了6种预测规则。只有添加了C反应蛋白检测的模型的曲线下面积有显著意义,为0.69(95%置信区间[CI],0.58 - 0.80),阳性预测值为47%(95% CI,23 - 71),阴性预测值为84%(95% CI,77 - 91)。肺炎存在和不存在的预测试概率分别为20%和80%。
仅基于临床信息的模型不能可靠地预测肺炎的存在。添加升高的C反应蛋白水平似乎价值不大。