Moreira Juan, Bisoffi Zeno, Narváez Alberto, Van den Ende Jef
Centro de Epidemiología Comunitaria y Medicina Tropical (CECOMET), Esmeraldas, Ecuador.
J Eval Clin Pract. 2008 Oct;14(5):934-40. doi: 10.1111/j.1365-2753.2008.01003.x.
Bedside use of Bayes' theorem for estimating probabilities of diseases is cumbersome. An alternative approach based on five categories of powers of tests from 'useless' to 'very strong' has been proposed. The performance of clinicians using it was assessed.
Fifty clinicians attending a course of tropical medicine estimated powers of tests and post-test probabilities using the classical vs. the categorical Bayesian approach. The estimation of post-test probability was assessed for real and dummy diseases in order to avoid the bias of previous knowledge. Accuracy of answers was measured by the difference with reference values obtained from an expert system (Kabisa).
Clinicians estimated positive likelihood ratios (LRs) a median of -1.07 log(10) lower than Kabisa [interquartile range (IQR): -1.47; -0.80] when derived classically and -0.17 (IQR: -0.42; +0.04) when estimated categorically (P < 0.001). For negative LRs the median was +0.39 log(10) higher (IQR: +0.71; +0.08) when derived classically and -0.18 log(10) lower (IQR: +0.03; -0.36) when estimated categorically (P < 0.001). Twenty (40%) disclosed not being able to calculate post-test probabilities using sensitivities and specificities. Regardless the approach post-test probabilities were overestimated both for real and dummy diseases [respectively +1.23 log(10) (IQR: +0,67; +2.08) and +2.03 log(10) (IQR: +0.49; +2.42)] (P = 0277), but the range was wider for the latter (P = 0.001).
Participants were more accurate in estimating powers with a categorical approach than with sensitivities and specificities. Post-test probabilities were overestimated with both approaches. Knowledge of the disease did not influence the estimation of post-test probabilities. A categorical approach might be an interesting instructional tool, but the effect of training with this approach needs assessment.
在床边使用贝叶斯定理来估计疾病概率很麻烦。有人提出了一种基于从“无用”到“非常强”的五类检验效能的替代方法。对使用该方法的临床医生的表现进行了评估。
五十名参加热带医学课程的临床医生使用经典方法与分类贝叶斯方法估计检验效能和检验后概率。为了避免先前知识的偏差，对真实疾病和虚拟疾病的检验后概率估计进行了评估。答案的准确性通过与从专家系统（Kabisa）获得的参考值的差异来衡量。
当采用经典方法推导时，临床医生估计的阳性似然比（LRs）中位数比Kabisa低-1.07 log(10) [四分位间距（IQR）：-1.47；-0.80]，而采用分类方法估计时为-0.17（IQR：-0.42；+0.04）（P < 0.001）。对于阴性似然比，采用经典方法推导时中位数高+0.39 log(10)（IQR：+0.71；+0.08），采用分类方法估计时低-0.18 log(10)（IQR：+0.03；-0.36）（P < 0.001）。二十名（40%）临床医生表示无法使用敏感度和特异度来计算检验后概率。无论采用哪种方法，真实疾病和虚拟疾病的检验后概率都被高估了[分别为+1.23 log(10)（IQR：+0.67；+2.08）和+2.03 log(10)（IQR：+0.49；+2.42）]（P = 0.277），但后者的范围更广（P = 0.001）。
参与者采用分类方法估计效能比使用敏感度和特异度更准确。两种方法都会高估检验后概率。对疾病的了解并未影响检验后概率的估计。分类方法可能是一种有趣的教学工具，但需要评估使用该方法进行培训的效果。
