Department of General Practice, PMaastricht University, School of Public Health and Primary Care (CAPHRI), O Box 616, 6200 MD Maastricht, the Netherlands.
BMC Fam Pract. 2010 Feb 16;11:13. doi: 10.1186/1471-2296-11-13.
Abnormal results of diagnostic laboratory tests can be difficult to interpret when disease probability is very low. Although most physicians generally do not use Bayesian calculations to interpret abnormal results, their estimates of pretest disease probability and reasons for ordering diagnostic tests may--in a more implicit manner--influence test interpretation and further management. A better understanding of this influence may help to improve test interpretation and management. Therefore, the objective of this study was to examine the influence of physicians' pretest disease probability estimates, and their reasons for ordering diagnostic tests, on test result interpretation, posttest probability estimates and further management.
Prospective study among 87 primary care physicians in the Netherlands who each ordered laboratory tests for 25 patients. They recorded their reasons for ordering the tests (to exclude or confirm disease or to reassure patients) and their pretest disease probability estimates. Upon receiving the results they recorded how they interpreted the tests, their posttest probability estimates and further management. Logistic regression was used to analyse whether the pretest probability and the reasons for ordering tests influenced the interpretation, the posttest probability estimates and the decisions on further management.
The physicians ordered tests for diagnostic purposes for 1253 patients; 742 patients had an abnormal result (64%). Physicians' pretest probability estimates and their reasons for ordering diagnostic tests influenced test interpretation, posttest probability estimates and further management. Abnormal results of tests ordered for reasons of reassurance were significantly more likely to be interpreted as normal (65.8%) compared to tests ordered to confirm a diagnosis or exclude a disease (27.7% and 50.9%, respectively). The odds for abnormal results to be interpreted as normal were much lower when the physician estimated a high pretest disease probability, compared to a low pretest probability estimate (OR = 0.18, 95% CI = 0.07-0.52, p < 0.001).
Interpretation and management of abnormal test results were strongly influenced by physicians' estimation of pretest disease probability and by the reason for ordering the test. By relating abnormal laboratory results to their pretest expectations, physicians may seek a balance between over- and under-reacting to laboratory test results.
当疾病概率非常低时,诊断实验室检查的异常结果可能难以解释。尽管大多数医生通常不使用贝叶斯计算来解释异常结果,但他们对检测前疾病概率的估计和进行诊断检测的原因可能会——以更隐晦的方式——影响检测解释和进一步的管理。更好地了解这种影响可能有助于改善检测解释和管理。因此,本研究的目的是检查医生检测前疾病概率估计和进行诊断检测的原因对检测结果解释、检测后概率估计和进一步管理的影响。
在荷兰的 87 名初级保健医生中进行前瞻性研究,每位医生为 25 名患者开了实验室检查。他们记录了他们开这些检查的原因(排除或确认疾病或让患者安心)和他们的检测前疾病概率估计。在收到结果后,他们记录了他们如何解释这些检查、他们的检测后概率估计和进一步的管理。使用逻辑回归分析检测前概率和进行诊断检测的原因是否影响检测结果解释、检测后概率估计和进一步的管理决策。
医生为 1253 名患者开具了诊断性检查;742 名患者的检查结果异常(64%)。医生的检测前概率估计和进行诊断检测的原因影响了检测结果解释、检测后概率估计和进一步的管理。出于安心目的开具的检查的异常结果更有可能被解释为正常(65.8%),而不是为了确认诊断或排除疾病而开具的检查(分别为 27.7%和 50.9%)。当医生估计高检测前疾病概率时,异常结果被解释为正常的几率要低得多,而不是低检测前概率估计(OR=0.18,95%CI=0.07-0.52,p<0.001)。
检测异常结果的解释和管理受到医生对检测前疾病概率的估计和进行检测的原因的强烈影响。通过将异常实验室结果与他们的检测前预期联系起来,医生可以在过度和不足对实验室检测结果的反应之间寻求平衡。