Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 73,413 bioRxiv papers from 319,549 authors.

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

By Mehrdad Rezaee, Arsia Takeh, Igor Putrenko, Andrea Ganna, Erik Ingelsson

Posted 27 May 2019
bioRxiv DOI: 10.1101/648956

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. 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. 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. 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.

Download data

  • Downloaded 196 times
  • Download rankings, all-time:
    • Site-wide: 55,116 out of 73,424
    • In bioinformatics: 5,937 out of 7,156
  • Year to date:
    • Site-wide: 25,314 out of 73,424
  • Since beginning of last month:
    • Site-wide: 25,314 out of 73,424

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News