Le Gal G, Righini M, Perrier A
EA 3878 (groupe d'étude de la thrombose de Bretagne Occidentale), département de médecine interne et de pneumologie, CHU de la Cavale-Blanche, boulevard Tanguy-Prigent, 29609 Brest cedex, France.
Rev Pneumol Clin. 2008 Dec;64(6):269-75. doi: 10.1016/j.pneumo.2008.09.002. Epub 2008 Nov 18.
The determination of the clinical pretest probability using clinical prediction models is an important step in the assessment of patients with suspected pulmonary embolism (PE). It helps establish which test or sequence of tests can effectively corroborate or safely rule out PE. For example, it has been demonstrated that it is safe to withhold anticoagulant therapy in patients with negative d-dimer results and low pretest probability at initial presentation. Clinical probability will also increase the diagnostic yield of ventilation perfusion lung scan. Compared with clinical gestalt, clinical prediction rules provide a standardized and more reproducible estimate of a patient's probability of having a PE. Clinical prediction models combine aspects of the history and physical examination to categorize a patient's probability of having a disease. The models classify patients as having a low, moderate, or high likelihood of having PE. Clinical prediction models have been validated and are well established for the diagnosis of PE in symptomatic patients. They allow all physicians, whatever their expertise, to reliably determine the clinical pretest probability of PE, and thus safely manage their patients using diagnostic and therapeutic algorithms.
使用临床预测模型确定临床预测试概率是评估疑似肺栓塞（PE）患者的重要一步。它有助于确定哪种检查或检查序列能够有效地证实或安全地排除PE。例如，已经证明，对于初始就诊时D-二聚体结果为阴性且预测试概率低的患者，停用抗凝治疗是安全的。临床概率也会提高通气灌注肺扫描的诊断率。与临床整体判断相比，临床预测规则对患者患PE的概率提供了标准化且更具可重复性的估计。临床预测模型结合病史和体格检查的各个方面来对患者患疾病的概率进行分类。这些模型将患者分类为患PE的可能性低、中或高。临床预测模型已经过验证，在有症状患者的PE诊断中已得到充分确立。它们使所有医生，无论其专业水平如何，都能可靠地确定PE的临床预测试概率，从而使用诊断和治疗算法安全地管理他们的患者。
