Ben-Shlomo Yoav, Collin Simon M, Quekett James, Sterne Jonathan A C, Whiting Penny
School of Social & Community Medicine, University of Bristol, Canynge Hall, Bristol, United Kingdom.
School of Social & Community Medicine, University of Bristol, Canynge Hall, Bristol, United Kingdom; Centre for Child & Adolescent Health, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom.
PLoS One. 2015 Jul 6;10(7):e0128637. doi: 10.1371/journal.pone.0128637. eCollection 2015.
There is little evidence on how best to present diagnostic information to doctors and whether this makes any difference to clinical management. We undertook a randomised controlled trial to see if different data presentations altered clinicians' decision to further investigate or treat a patient with a fictitious disorder ("Green syndrome") and their ability to determine post-test probability.
We recruited doctors registered with the United Kingdom's largest online network for medical doctors between 10 July and 6" November 2012. Participants were randomised to one of four arms: (a) text summary of sensitivity and specificity, (b) Fagan's nomogram, (c) probability-modifying plot (PMP), (d) natural frequency tree (NFT). The main outcome measure was the decision whether to treat, not treat or undertake a brain biopsy on the hypothetical patient and the correct post-test probability. Secondary outcome measures included knowledge of diagnostic tests.
917 participants attempted the survey and complete data were available from 874 (95.3%). Doctors randomized to the PMP and NFT arms were more likely to treat the patient than those randomized to the text-only arm. (ORs 1.49, 95% CI 1.02, 2.16) and 1.43, 95% CI 0.98, 2.08 respectively). More patients randomized to the PMP (87/218-39.9%) and NFT (73/207-35.3%) arms than the nomogram (50/194-25.8%) or text only (30/255-11.8%) arms reported the correct post-test probability (p <0.001). Younger age, postgraduate training and higher self-rated confidence all predicted better knowledge performance. Doctors with better knowledge were more likely to view an optional learning tutorial (OR per correct answer 1.18, 95% CI 1.06, 1.31).
Presenting diagnostic data using a probability-modifying plot or natural frequency tree influences the threshold for treatment and improves interpretation of tests results compared to text summary of sensitivity and specificity or Fagan's nomogram.
关于如何以最佳方式向医生呈现诊断信息以及这是否会对临床管理产生影响，几乎没有证据。我们进行了一项随机对照试验，以观察不同的数据呈现方式是否会改变临床医生对患有虚构疾病（“格林综合征”）的患者进行进一步检查或治疗的决定，以及他们确定检验后概率的能力。
我们招募了在2012年7月10日至11月6日期间注册于英国最大的在线医生网络的医生。参与者被随机分配到四个组之一：（a）敏感性和特异性的文本摘要，（b）费根氏诺模图，（c）概率修正图（PMP），（d）自然频率树（NFT）。主要结局指标是对假设患者进行治疗、不治疗或进行脑活检的决定以及正确的检验后概率。次要结局指标包括对诊断测试的了解。
917名参与者尝试了该调查，874名（95.3%）有完整数据。随机分配到PMP组和NFT组的医生比随机分配到仅文本组的医生更倾向于治疗患者（优势比分别为1.49，95%置信区间1.02，2.16和1.43，95%置信区间0.98，2.08）。与诺模图组（50/194 - 25.8%）或仅文本组（30/255 - 11.8%）相比，随机分配到PMP组（87/218 - 39.9%）和NFT组（73/207 - 35.3%）的更多患者报告了正确的检验后概率（p <0.001）。年龄较小、接受过研究生培训和自我评定的信心较高都预示着知识表现更好。知识较好的医生更有可能查看一个可选的学习教程（每个正确答案的优势比为1.18，95%置信区间1.06，1.31）。
与敏感性和特异性的文本摘要或费根氏诺模图相比，使用概率修正图或自然频率树呈现诊断数据会影响治疗阈值并改善对测试结果的解读。
Zotero 插件