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Risk predictions for individual patients from logistic regression were visualized with bar-line charts.

Björk, Jonas LU ; Ekelund, Ulf LU and Ohlsson, Mattias LU (2012) In Journal of Clinical Epidemiology 65. p.335-342
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 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... (More)
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 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. 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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Clinical Epidemiology
volume
65
pages
335 - 342
publisher
Elsevier
external identifiers
  • wos:000299754800017
  • pmid:22112994
  • scopus:84856012082
ISSN
1878-5921
DOI
10.1016/j.jclinepi.2011.06.019
language
English
LU publication?
yes
id
d075197c-fa9e-48d1-a507-e7381e24641e (old id 2220367)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/22112994?dopt=Abstract
date added to LUP
2011-12-03 10:04:38
date last changed
2017-05-17 10:39:13
@article{d075197c-fa9e-48d1-a507-e7381e24641e,
  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 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. 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.},
  author       = {Björk, Jonas and Ekelund, Ulf and Ohlsson, Mattias},
  issn         = {1878-5921},
  language     = {eng},
  pages        = {335--342},
  publisher    = {Elsevier},
  series       = {Journal of Clinical Epidemiology},
  title        = {Risk predictions for individual patients from logistic regression were visualized with bar-line charts.},
  url          = {http://dx.doi.org/10.1016/j.jclinepi.2011.06.019},
  volume       = {65},
  year         = {2012},
}