Stanford Cardiovascular Data Integration Lab

Overview

The Stanford Cardiovascular Data Integration Lab (SCDIL) brings together a group of cardiologists, cardiovascular surgeons, radiologists, and information technology and computer science experts with the overall objective to facilitate cardiovascular data science research.

Rationale for the group

Over the last few years, data science has evolved where significant clinical insights can be gained by integrating clinical, laboratory, imaging, and outcome data. To make this possible, the group combines expertise from the working group.

History

The group has been working together for several years, first contributing to the transcatheter aortic valve replacement (TAVR) database, surgical aortic disease dataset, and several heart failure and pulmonary disease datasets. In addition, cardiovascular investigators have developed several datasets over the years, including the inherited cardiovascular disease databases, the heart transplant and LVAD databases, the South Asian Initiative datasets, the Insulin resistance network datasets, among others.

Objectives

To facilitate data integration from different data sources

To develop a data dictionary for facilitating data integration from the STAnford Research Repository, or STARR
To develop a script for the Xcelera. Xcelera database established in 1999 is available for analysis
To develop a script or data structure for Xray, computed tomography (CT), and magnetic resonance imaging
To identify cardiovascular science leads for key datasets for collaborations
To consolidate outcome measures based on mortality dataset, STS surgical outcome measure, Social security death index, hospital analytics, specific outcome measures
To combine datasets with other high yield datasets available
Operational: to identify best database structure according to needs

To make accessible for researchers a data science analytic pipeline

To facilitate and prioritize clinical or data science research projects

These projects may include quality of care work, resource utilization, outcome’s research, data science structure research, or artificial intelligence analytics research.

First project priorities:

Echo Database

Acute heart failure admission

Heart transplant early outcome project

Surgical risk models

Type B Dissection

Mortality associated with re-intervention following endovascular aortic repair

Long-term outcomes of endovascular aortic repair in uncomplicated type B dissection

To facilitate imaging and clinical Artificial Intelligence research projects

Funding

Support has been provided to the IT group by the Department of Cardiothoracic Surgery with other support for personnel from the Stanford Cardiovascular Institute.

Additional aspirational objectives for future consideration

To integrate these datasets with genomic data
To integrate these datasets with biosamples and biorepositories
To integrate these datasets with pathology samples and associated data

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