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  • Review Article
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Big Data in Nephrology

Nature Reviews Nephrology volume 17, pages 676–687 (2021)Cite this article

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Abstract

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Key points

  • Big data in nephrology can provide essential information about kidney disease burden, molecular mechanisms, novel risk factors and therapeutic targets.

  • Artificial intelligence and machine-learning approaches that utilize big data could be used for a variety of applications in nephrology, including early diagnosis and prognosis, as well as clinical decision-support systems for personalized selection of therapy.

  • Data curation and standardization enable interoperability, facilitate consolidation and exchange of high-quality data from different sources, create independence from manufacturers and ease competition as comparable products are offered by all market players.

  • Sources of big data in nephrology include patient registries, population surveys, electronic health records, open-access clinical trials, mobile health devices and molecular data repositories.

  • Large-scale acquisition of annotated molecular and clinical data, together with advances in machine learning approaches, open-source computational packages, affordable computation power and cloud storage, will all facilitate more novel data-driven approaches in nephrology.

  • Challenges for the utilization of big data in nephrology include issues relating to data governance and protection, siloed datasets, data heterogeneity, small sample sizes and a lack of consistent research funding.

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Fig. 1: Characteristics of big data.
Fig. 2: Workflow for use of data for clinical decision support systems in nephrology.
Fig. 3: Timeline of the availability of big data in nephrology.

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Acknowledgements

The authors would like to acknowledge Flavio Vincenti, Sri Lekha Tummalapalli, Vivek Rudrapatna, Douglas Arneson and Zicheng Hu (all University of California, San Francisco) for their valuable suggestions for this manuscript. The authors’ work was supported by the National Institute of Allergy and Infectious Diseases (Bioinformatics Support Contract HHSN316201200036W), the UCSF Bakar Computational Health Sciences Institute and the UCSF Clinical and Translational Sciences Institute, supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1 TR001872. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA

    Navchetan Kaur, Sanchita Bhattacharya & Atul J. Butte

  2. Department of Pediatrics, University of California, San Francisco, CA, USA

    Navchetan Kaur, Sanchita Bhattacharya & Atul J. Butte

  3. Center for Data-Driven Insights and Innovation, University of California Health, Oakland, CA, USA

    Atul J. Butte

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  1. Navchetan Kaur
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N.K. researched data for the article and wrote the manuscript, S.B. contributed to data research and edited the manuscript. A.J.B. conceived the manuscript framework and reviewed and edited it before submission.

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Correspondence to Atul J. Butte.

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Competing interests

A.T.B. is a co-founder and consultant to Personalis and NuMedii; consultant to Samsung, Mango Tree Corporation, and in the recent past, 10x Genomics, Helix, Pathway Genomics, and Verinata (Illumina); has served on paid advisory panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, Merck and Roche; is a shareholder in Personalis and NuMedii; is a minor shareholder in Apple, Facebook, Alphabet (Google), Microsoft, Amazon, Snap, 10x Genomics, Illumina, CVS, Nuna Health, Assay Depot, Vet24seven, Regeneron, Sanofi, Royalty Pharma, AstraZeneca, Moderna, Biogen, Paraxel and Sutro, and several other non-health-related companies and mutual funds; and has received honoraria and travel reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer, Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, Westat, and many academic institutions, medical or disease-specific foundations and associations, and health systems. A.T.B. receives royalty payments through Stanford University, for several patents and other disclosures licensed to NuMedii and Personalis. His research has been funded by NIH, Northrup Grumman (as the prime on an NIH contract), Genentech, Johnson and Johnson, FDA, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, the Barbara and Gerson Bakar Foundation, and in the recent past, the March of Dimes, Juvenile Diabetes Research Foundation, California Governor’s Office of Planning and Research, California Institute for Regenerative Medicine, L’Oreal, and Progenity.

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Nature Reviews Nephrology thanks Luxia Zhang, who co-reviewed with Chao Yang, William Herrington, Min Jun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Autosomal dominant polycystic kidney disease mutation database: https://pkdb.mayo.edu/

ClinicalStudyDataRequest.com: https://clinicalstudydatarequest.com/Default.aspx

Clinicaltrials.gov: https://clinicaltrials.gov/

Immune Tolerance Network: https://www.immunetolerance.org/

National Kidney Foundation Patient Network: https://www.kidney.org/nkfpatientnetwork

Nephroseq: https://www.nephroseq.org/resource/login.html

PatientsLikeMe: https://www.patientslikeme.com/

Registry Of Kidney Diseases: https://gardn.org.au/registries/registry-of-kidney-diseases/

RenDER data extraction and referencing system: https://render.usrds.org/render/xrender.phtml

Sentinel and Patient-Centered Outcomes Research Network: https://www.sentinelinitiative.org/sentinel/data/distributed-database-common-data-model

The HCUP National Inpatient Sample (NIS): https://www.hcup-us.ahrq.gov/nisoverview.jsp

The NephCure Kidney Network Patient Registry: https://nephcure.org/2015/12/the-nephcure-kidney-network-patient-registry-nkn/

Think Kidneys: https://www.thinkkidneys.nhs.uk/

WHO International Clinical Trials Registry Platform: https://www.who.int/ictrp/en/

Supplementary information

Glossary

Deep learning

A type of machine learning that uses multiple layers to progressively extract higher level features from the input layer of the model. Common deep learning algorithms include convolutional neural networks, recurrent neural networks, general adversarial networks and autoencoders.

k-anonymity

A data anonymization technique that protects the identities of individuals using methods such as suppression and generalization. A dataset is said to have k-anonymity if the information for each individual cannot be distinguished from that of at least k-1 individuals.

l-diversity

A data anonymization approach that relies on introducing further entropy or diversity to the dataset. This model uses generalization and promotes diversity for sensitive values within a group. l-diversity is an extension of the k-anonymity model.

t-closeness

This model is a further refinement of the k-anonymity and l-diversity models. t-closeness is the maximum of the distances between the distribution of values of a sensitive attribute and that of the entire database table. An equivalence class will have t-closeness if the distance between the attribute in the class and whole table is no more than threshold t.

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Kaur, N., Bhattacharya, S. & Butte, A.J. Big Data in Nephrology. Nat Rev Nephrol 17, 676–687 (2021). https://doi.org/10.1038/s41581-021-00439-x

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