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Big Data and Kidney Transplantation: Basic Concepts and Initial Experiences

  • David J. Taber
  • Amit K. Mathur
  • Titte R. Srinivas
Chapter

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.

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

References

  1. 1.
    Kaplan, B., Schold, J., & Meier-Kriesche, H. U. (2003). Overview of large database analysis in renal transplantation. American Journal of Transplantation, 3, 1052–1056.CrossRefPubMedGoogle Scholar
  2. 2.
    Taber, D. J., Palanisamy, A. P., Srinivas, T. R., et al. (2015). Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation. Transplantation, 99, 324–330.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    McAdams-Demarco, M. A., Grams, M. E., King, E., Desai, N. M., & Segev, D. L. (2014). Sequelae of early hospital readmission after kidney transplantation. American Journal of Transplantation, 14, 397–403.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    IBM White Paper. (2016). 5 Steps to becoming a data-driven healthcare organization. In (pp. 1–7). Somers, NY: IBM Corporation.https://assets.sourcemedia.com/00/f5/b8f107cf478296eaa937a413581c/imw14682usen.PDFGoogle Scholar
  5. 5.
    Racusen, L. C., Solez, K., Colvin, R. B., et al. (1999). The Banff 97 working classification of renal allograft pathology. Kidney International, 55, 713–723.CrossRefPubMedGoogle Scholar
  6. 6.
    Quan, H., Sundararajan, V., Halfon, P., et al. (2005). Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care, 43, 1130–1139.CrossRefPubMedGoogle Scholar
  7. 7.
    Charlson, M. E., & Feinstein, A. R. (1974). The auxometric dimension. A new method for using rate of growth in prognostic staging of breast cancer. JAMA, 228, 180–185.CrossRefPubMedGoogle Scholar
  8. 8.
    Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80, 27–38.CrossRefGoogle Scholar
  9. 9.
    Heinze, G. A. (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine, 21, 2409–2419.CrossRefPubMedGoogle Scholar
  10. 10.
    Heinze, G. A. (2006). A comparative investigation of methods for logistic regression with separated or nearly separated data. Statistics in Medicine, 25, 4216–4226.CrossRefPubMedGoogle Scholar
  11. 11.
    Harrell, F. E., Jr., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15, 361–387.CrossRefPubMedGoogle Scholar
  12. 12.
    SRTR Risk Adjustment Model Documentationr Waiting List and Post-Transplant Outcomes. (2016). http://www.srtr.org/csr/current/modtabs.aspx. Accessed 16 June 2016.
  13. 13.
    Rao, P. S., Schaubel, D. E., Guidinger, M. K., et al. (2009). A comprehensive risk quantification score for deceased donor kidneys: The kidney donor risk index. Transplantation, 88, 231–236.CrossRefPubMedGoogle Scholar
  14. 14.
    Amann, K., Wanner, C., & Ritz, E. (2006). Cross-talk between the kidney and the cardiovascular system. Journals of the American Society of Nephrology, 17, 2112–2119.CrossRefGoogle Scholar
  15. 15.
    Chang, T. I., Tabada, G. H., Yang, J., Tan, T. C., & Go, A. S. (2016). Visit-to-visit variability of blood pressure and death, end-stage renal disease, and cardiovascular events in patients with chronic kidney disease. Journal of Hypertension, 34, 244–252.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Johnson, R. J., Rodriguez-Iturbe, B., Kang, D. H., Feig, D. I., & Herrera-Acosta, J. (2005). A unifying pathway for essential hypertension. American Journal of Hypertension, 18, 431–440.CrossRefPubMedGoogle Scholar
  17. 17.
    Meier-Kriesche, H. U., Schold, J. D., Srinivas, T. R., Reed, A., & Kaplan, B. (2004). Kidney transplantation halts cardiovascular disease progression in patients with end-stage renal disease. American Journal of Transplantation, 4, 1662–1668.CrossRefPubMedGoogle Scholar
  18. 18.
    Wan, S. S., Cantarovich, M., Mucsi, I., Baran, D., Paraskevas, S., & Tchervenkov, J. (2016). Early renal function recovery and long-term graft survival in kidney transplantation. Transplant International, 29, 619–626.CrossRefPubMedGoogle Scholar
  19. 19.
    Elfadawy, N., Flechner, S. M., Liu, X., et al. (2013). CMV Viremia is associated with a decreased incidence of BKV reactivation after kidney and kidney-pancreas transplantation. Transplantation, 96, 1097–1103.CrossRefPubMedGoogle Scholar
  20. 20.
    Gonzales, M. M., Bentall, A., Kremers, W. K., Stegall, M. D., & Borrows, R. (2016). Predicting individual renal allograft outcomes using risk models with 1-year surveillance biopsy and alloantibody data. Journals of the American Society of Nephrology, 27(10), 3165–3174.CrossRefGoogle Scholar
  21. 21.
    Goldfarb-Rumyantzev, A. S., Rout, P., Sandhu, G. S., Khattak, M., Tang, H., & Barenbaum, A. (2010). Association between social adaptability index and survival of patients with chronic kidney disease. Nephrology, Dialysis, Transplantation, 25, 3672–3681.CrossRefPubMedGoogle Scholar
  22. 22.
    Taber, D. J., Hamedi, M., Rodrigue, J. R., et al. (2016). Quantifying the race stratified impact of socioeconomics on graft outcomes in kidney transplant recipients. Transplantation, 100(7), 1550–1557.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Evans, R. S., Benuzillo, J., Horne, B. D., et al. (2016). Automated identification and predictive tools to help identify high-risk heart failure patients: Pilot evaluation. Journal of the American Medical Informatics Association, 23(5), 872–878.CrossRefPubMedGoogle Scholar
  24. 24.
    Srinivas, T. R., Taber, D. J., Su, Z., et al. (2017). Big data, predictive analytics and quality improvement in kidney transplantation: A proof of concept. American Journal of Transplantation, 17, 671–681.CrossRefPubMedGoogle Scholar
  25. 25.
    Hurwitz JS, Kaufman M, Bowles A in Cognitive Computing and Big Data Analytics. Wiley (Indianapolis) 2015.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • David J. Taber
    • 1
  • Amit K. Mathur
    • 2
  • Titte R. Srinivas
    • 3
  1. 1.Medical University of South Carolina, Division of Transplant SurgeryCharlestonUSA
  2. 2.Mayo Clinic, Division of Transplant SurgeryScottsdaleUSA
  3. 3.Transplant Nephrology, Intermountain Medical Center, Transplant ServicesMurrayUK

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