Jonas Björk, Ulf Ekelund and Mattias Ohlsson
Risk predictions for individual patients from logistic regression can be visualized with bar-line charts
LU TP 11-10
Abstract:
Objective 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.
Study Design and Setting Data from a previous diagnostic study aiming at predicting the immediate risk of acute coronary syndromes (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.
Results 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).
Conclusion 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.
Objective 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.
Study Design and Setting Data from a previous diagnostic study aiming at predicting the immediate risk of acute coronary syndromes (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.
Results 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).
Conclusion 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.
LU TP 11-10