Automatic covariate selection in logistic models for chest pain diagnosis: a new approach.
A newly established method for optimizing logistic models via a minorization-majorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via 10-fold cross-validation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task. The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall. The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside.
Harrison
Robert F
RF
Department of Automatic Control & Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK. r.f.harrison@sheffield.ac.uk
Kennedy
R Lee
RL
D054058:Acute Coronary Syndrome / Q000175:diagnosis*; D000465:Algorithms; D001499:Bayes Theorem; D002637:Chest Pain / Q000175:diagnosis*; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical