CARDIOLOGY
Data-Driven Discovery
How can adopting big data strategies from the business world help the medical field advance research ?
The business world has used big data to drive decisions for years . Now , doctors are bringing this approach from the board room to the lab bench , using data to inform research questions . Investigators in Children ’ s Hospital Colorado ’ s Heart Institute are using big data and multiomics profiling as a launchpad to uncover new information and hypotheses about pediatric heart disease . This technique can point researchers toward new areas of study and accelerate the research process to impact patients faster . As Ben Frank , MD , puts it , “ Sometimes you don ’ t know what you don ’ t know until you find it .”
Jesse Davidson , MD , and his team started exploring this inductive research approach at Children ’ s Colorado in 2018 with a metabolic profiling study of infants under 4 months old who underwent cardiothoracic surgery . At the time , Dr . Davidson says , this was one of the first major pediatric medical studies to use the data-driven approach . The team ’ s work showed significant dysregulation in many of the metabolic pathways , pointing them to areas of further research ( 1 ).
The big data process looks slightly different each time , but the research typically starts with blood tests . If Drs . Davidson and Frank are using a metabolomics approach , that means measuring anywhere from 200 to 250 biomarkers . If they are employing a proteomics approach , that involves measuring closer to 1,500 markers in each patient . Then , the researchers plug that data into a machine-learning algorithm that sorts through what might be significant depending on how the researchers are dividing up the population they are studying , such as kids with heart disease compared to kids who don ’ t have heart disease . After that , they are able to ask questions and test hypotheses .
“ Zooming out to see the truth can help you see things you weren ’ t expecting ,” Dr . Davidson says . “ Science has traditionally been a hypothesis-driven product . You make a hypothesis about a next experience , test that hypothesis , come up with more data , make your hypothesis , test that , etc . It has blinders on to just about everything else that ’ s going on . It is extraordinarily challenging to work down a very linear path and expect that you ’ ll know a lot about the rest of the system .”
While Dr . Davidson says this more traditional , hypothesis-driven approach is still extremely important in the research process , using data to drive the hypotheses can account for more complexity .
As Dr . Davidson , Dr . Frank and their colleagues started seeing clinical research yield more complex and intricate data , they realized this type of big data approach would be ideal , especially for their work with patients with single ventricle heart disease . The team treats children with this condition regularly , so there ’ s a trove of clinical data to analyze .
“ There ’ s a lot of room to do better , and there ’ s a lot of room to help their lives , to help their survival and to help their day-to-day experience ,” Dr . Frank says . “ We are at a unique and powerful moment to be at the forefront of trying to figure out how to leverage this new data , and how to use it to help kids .”
Dr . Davidson agrees : “ Cardiology and cardiac surgery are ripe for this reverse strategy . If you start with an inductive approach , are there ways to find smarter ways forward and also to speed up the discovery process ?”
TEAM SCIENCE
Big data analytics would not be possible without a team of experts working together across the University of Colorado ( CU ) Anschutz Medical Campus . For example , as part of this work , it was necessary to engage with statisticians , and CU is home to the Center for Innovative Design and Analysis — an invaluable tool for researchers partnering with biostatisticians and data scientists on complex data sets . Drs . Davidson and Frank also work closely with a team of research coordinators , research nurses and regulatory support teams .
“ Sometimes I think Dr . Davidson and I are conductors of a big orchestra . Without all the instruments , though , you wouldn ’ t get any music ,” Dr . Frank says . “ That kind of collaborative , team-based approach has been essential to our success .”
This collaborative style of research allows the team to follow the path laid out by the data . Dr . Davidson and his team are taking this work one step further , by not just generating new hypotheses after
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