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Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository

Mehrdad Rezaee, Arsia Takeh, Igor Putrenko, Andrea Ganna, Erik Ingelsson
doi: https://doi.org/10.1101/648956
Mehrdad Rezaee
1Precision Wellness Inc., 1901 Embarcadero Rd #102, Palo Alto, CA, USA
2Cardiac and Vascular Care, Inc. 2030 Forest Ave, San Jose, CA, USA
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  • For correspondence: mrezaee@precisionwellness.com
Arsia Takeh
1Precision Wellness Inc., 1901 Embarcadero Rd #102, Palo Alto, CA, USA
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Igor Putrenko
1Precision Wellness Inc., 1901 Embarcadero Rd #102, Palo Alto, CA, USA
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Andrea Ganna
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
4Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
5Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
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Erik Ingelsson
6Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
7Stanford Cardiovascular Institute, Stanford, CA 94305, USA
8Stanford Diabetes Research Center, Stanford, CA 94305, USA
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Abstract

Background Stratification of individuals for their risk to develop cardiovascular diseases can be used for effective prevention and intervention. A significant amount of information for risk assessment can be obtained through repurposing electronic health records (EHR). The objective of this study is to derive and assess the performance of prediction models for cardiovascular outcomes by using EHR-derived data.

Methods We used the Stanford Medicine Research Data Repository (STARR) data from 2000-2017, containing over 2.1 million patients. A subset of 762,372 individuals with complete International Classification of Diseases (ICD) data was used to fit Cox proportional hazard models for prediction of six cardiovascular-related diseases and type 2 diabetes.

Results The derived prediction models indicated consistent high discrimination performance (C-index) for all diseases examined: coronary artery disease (0.85), hypertension (0.82), type 2 diabetes (0.77), stroke (0.76), atrial fibrillation (0.82) and abdominal aortic aneurysm (0.77). Lower prediction abilities were observed for deep vein thrombosis (0.67). These results were consistent across age groups and maintained good prediction abilities among individuals with pre-existing diabetes or hypertension. Assessment of model calibration is ongoing.

Conclusions We proposed new prediction models for the seven diseases using ICD codes derived from EHR data. EHR data can be used for health risk assessment, but challenges related to data quality and model generalizability and calibration remain to be solved.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 27, 2019.
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Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository
Mehrdad Rezaee, Arsia Takeh, Igor Putrenko, Andrea Ganna, Erik Ingelsson
bioRxiv 648956; doi: https://doi.org/10.1101/648956
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Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository
Mehrdad Rezaee, Arsia Takeh, Igor Putrenko, Andrea Ganna, Erik Ingelsson
bioRxiv 648956; doi: https://doi.org/10.1101/648956

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