large datasets in cardiology

Large Datasets and Their Ability to Advance Cardiology

December 12, 2016

Large-scale data collection has been an increasingly adopted practice in healthcare and clinical trials. The speed at which data is collected via electronic mediums over recent years has facilitated more precise and efficient data storing, retrieval, and analysis of pertinent health-related outcomes. Today’s electronic data collection tools also allow for easy access of large datasets that can potentially assist in the design of future clinical trials, especially in cardiology. Additionally, large datasets have aided the robust development of current trials involving large groups of participants, further supporting the swift evolution of heart disease knowledge and treatment options.

Large Data in Current Science

One such example of a large dataset is the Framingham study, a clinical trial involving thousands of people from several generations in Framingham, MA, since the late 1940s. This large-scale study has provided much-needed insight into cardiovascular disease patterns and risk factors and has even led to the discovery of important key elements in the development of heart disease. Similar studies across the world with big datasets have contributed to the development of science and medicine, helping to evolve the current model of healthcare.

Big Data’s Ability to Progress Treatment and Patient Care

Large datasets used in cardiology research can help provide further insights into the applicability of various therapies across the population, including the utility of certain cardiovascular treatments and their effects within specific demographic cohorts. The Algorithm for Comorbidities, Associations, Length of Stay, and Mortality (ACALM) study unit has more than a million participants and has been useful for evaluating common risk factors affecting mortality as they relate to cardiovascular disease among different genders, races, ages, and lifestyles.

The utilization of big data in research like the Framingham and ACALM study units can also help researchers discover health-related correlations and develop hypotheses that may lead to further clinical and scientific discovery within the field of cardiology. According to a 2016 article published in the European Medical Journal, big data (in addition to complex modelling and algorithms) have aided in the prediction of septic shock onset, the risk of myocardial infarction, and have even helped optimize individualized anticoagulation treatments.1

The Limitations and Advantages of Large Data Collection

Initiating a large dataset-type study is no easy task as it requires a substantial amount of resources to make it worthwhile and effective. Regardless of the initial costs, the information benefits obtained from big datasets often exceed limitations that time, money, and energy present. In an effort to improve patient care, the field of cardiology may benefit from more focused, large-scale datasets, analyses, and studies specifically designed to assess cardiovascular risk factors, preventative components, key clinical endpoints, and intervention outcomes.

Reference:

  1. Potluri R, Drozdov I, Carter P, Sarma J. Big Data and Cardiology: Time for Mass Analytics? European Medical Journal. 2016:15-17.

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