Competence Centre for Clinical Research, Skåne University Hospital, Barngatan 2, Lund, Sweden.
J Clin Epidemiol. 2012 Mar;65(3):335-42. doi: 10.1016/j.jclinepi.2011.06.019. Epub 2011 Nov 22.
The interface of a computerized decision support system is crucial for its acceptance among end users. We demonstrate how combined bar-line charts can be used to visualize predictions for individual patients from logistic regression models.
Data from a previous diagnostic study aiming at predicting the immediate risk of acute coronary syndrome (ACS) among 634 patients presenting to an emergency department with chest pain were used. Risk predictions from the logistic regression model were presented for four hypothetical patients in bar-line charts with bars representing empirical Bayes adjusted likelihood ratios (LRs) and the line representing the estimated probability of ACS, sequentially updated from left to right after assessment of each risk factor.
Two patients had similar low risk for ACS but quite different risk profiles according to the bar-line charts. Such differences in risk profiles could not be detected from the estimated ACS risk alone. The bar-line charts also highlighted important but counteracted risk factors in cases where the overall LR was less informative (close to one).
The proposed graphical technique conveys additional information from the logistic model that can be important for correct diagnosis and classification of patients and appropriate medical management.
计算机决策支持系统的界面对于其在终端用户中的接受程度至关重要。我们展示了如何结合条形线图来可视化来自逻辑回归模型的个体患者预测。
使用了来自之前一项旨在预测 634 名因胸痛就诊急诊的患者发生急性冠状动脉综合征 (ACS) 即刻风险的诊断研究的数据。在条形线图中为 4 个假设患者呈现逻辑回归模型的风险预测，每个风险因素评估后，从左到右依次更新条形表示经验贝叶斯调整后的似然比 (LR)，线表示 ACS 的估计概率。
2 名患者的 ACS 风险相似，但根据条形线图，他们的风险特征却大不相同。仅从估计的 ACS 风险来看，无法发现这些风险特征的差异。条形线图还突出了在整体 LR 不太有信息量（接近 1）的情况下重要但相互抵消的风险因素。
所提出的图形技术传达了逻辑模型的额外信息，对于正确诊断和分类患者以及进行适当的医疗管理非常重要。
