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Big Data and Kidney Transplantation: Basic Concepts and Initial Experiences

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Technological Advances in Organ Transplantation

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Abstract

We live in a data-rich world that is ever expanding, and the field of medicine has become particularly enriched with data from the electronic health record (EHR) and from sensors such as EKG monitors, glucometers, and pacemakers. Big Data is a term that is now frequently encountered in both the lay press and the technical literature and is best defined by the extreme volume, variety, or velocity of data. Large relational databases alone do not equate to Big Data (Table 13.2 and see discussion that follows). The magnitude of the data explosion that we live in consciously or unconsciously is underscored, which is outlined throughout this chapter. As a specific example this ever-growing field can have, we will use our recent inquiry into predicting kidney transplant outcomes using a big data approach and discuss the applicability of big data techniques in clinical transplantation.

This chapter is an in-depth review of content published in the American Journal of Transplantation. Srinivas et al. Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept. AJT. 17:3;March 2017. 671–681.

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Abbreviations

AUC-ROC:

Area Under the Curve-Receiver Operating Characteristic Curve

BK:

BK Virus

BMI:

Body Mass Index

BP:

Blood Pressure

CI:

Confidence Interval

CMV:

Cytomegalovirus

DGF:

Delayed Graft Function

eGFR:

Estimated Glomerular Filtration Rate

EHR:

Electronic Health Record

GL:

Graft Loss

HGB:

Hemoglobin

ICD-9:

International Classification of Diseases

KDRI:

Kidney Donor Risk Index

Max:

Maximum

MI:

Myocardial Infarction

NLP:

Natural Language Processing

OR:

Odds Ratio

PCR:

Polymerase Chain Reaction

SBP:

Systolic Blood Pressure

SRTR:

Scientific Registry of Transplant Recipients

Tx Database:

Transplant Database

UNOS:

United Network for Organ Sharing

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Author information

Authors and Affiliations

  1. Medical University of South Carolina, Division of Transplant Surgery, Charleston, SC, USA

    David J. Taber PharmD, MS, BCPS

  2. Mayo Clinic, Division of Transplant Surgery, Scottsdale, AZ, USA

    Amit K. Mathur MD

  3. Transplant Nephrology, Intermountain Medical Center, Transplant Services, 5169 S Cottonwood St. Suite 320 Eccles Outpt. Bldg, Murray, UT, 84107, UK

    Titte R. Srinivas MD, FAST

Authors
  1. David J. Taber PharmD, MS, BCPS
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  2. Amit K. Mathur MD
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  3. Titte R. Srinivas MD, FAST
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Corresponding author

Correspondence to Titte R. Srinivas MD, FAST .

Editor information

Editors and Affiliations

  1. Medical University of South Carolina, Charleston, South Carolina, USA

    Satish N. Nadig

  2. Northwestern University, Chicago, Illinois, USA

    Jason A. Wertheim

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Taber, D.J., Mathur, A.K., Srinivas, T.R. (2017). Big Data and Kidney Transplantation: Basic Concepts and Initial Experiences. In: Nadig, S., Wertheim, J. (eds) Technological Advances in Organ Transplantation. Springer, Cham. https://doi.org/10.1007/978-3-319-62142-5_13

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