• 1.

    Vazquez MA, et al.; ICD-Pieces Study Group. Pragmatic trial of hospitalization rate in chronic kidney disease. N Engl J Med 2024; 390:11961206. doi: 10.1056/NEJMoa2311708

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Jefferies JL, et al. A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm. Orphanet J Rare Dis 2021; 16:518. doi: 10.1186/s13023-021-02150-3

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Solanki KV, et al. The phenotypic spectrum of COL4A3 heterozygotes. Kidney Int Rep 2023; 8:20882099. doi: 10.1016/j.ekir.2023.07.010

  • 4.

    Puurunen M, et al. Twenty-four-hour urine oxalate and risk of chronic kidney disease. Nephrol Dial Transplant 2024; 39:788794. doi: 10.1093/ndt/gfad221

  • 5.

    AWAK Technologies Pte Ltd. AWAK Technologies announces FDA breakthrough device designation for their artificial intelligence enabled kidney disease prediction tool. PR Newswire. November 27, 2023. Accessed June 24, 2024. https://www.prnewswire.com/news-releases/awak-technologies-announces-fda-breakthrough-device-designation-for-their-artificial-intelligence-enabled-kidney-disease-prediction-tool-301997423.html

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Gudelunas MK, et al. Low perfusion and missed diagnosis of hypoxemia by pulse oximetry in darkly pigmented skin: A prospective study. Anesth Analg 2024; 138:552561. doi: 10.1213/ANE.0000000000006755

    • PubMed
    • Search Google Scholar
    • Export Citation

Leveraging Real-World Patient Data Is Key to Kidney Care Innovation

Bridget M. Kuehn
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