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. 2018 Feb:10577:105770J.
doi: 10.1117/12.2293954. Epub 2018 Mar 7.

Quantifying predictive capability of electronic health records for the most harmful breast cancer

Yirong Wu  1 , Jun Fan  1 , Peggy Peissig  2 , Richard Berg  2 , Ahmad Pahlavan Tafti  2 , Jie Yin  3   4 , Ming Yuan  1 , David Page  1 , Jennifer Cox  1 , Elizabeth S Burnside  1
Affiliations

Quantifying predictive capability of electronic health records for the most harmful breast cancer

Yirong Wu et al. Proc SPIE Int Soc Opt Eng. 2018 Feb.

Abstract

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and Lasso-LR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

Keywords: breast cancer; electronic health records (EHRs); least absolute shrinkage and selection operator (Lasso); regularized prediction model.

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Figures

Figure 1
Figure 1
Specification of censor date. EHR data in the red oval were used to predict the “most harmful” breast cancer.
Figure 2
Figure 2
ROC curves for Ridge-LR models.
Figure 3
Figure 3
ROC curves for Lasso-LR models.
See this image and copyright information in PMC

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