A Free Algorithm Program to Scan Big Data for Chronic Kidney Disease

A Free Algorithm Program to Scan Big Data for Chronic Kidney Disease Posted By:
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A team at Columbia University Medical Center in New York has developed and validated a free software algorithm that can scan an electronic medical record (EMR) and identify patients with chronic kidney disease (CKD) with a positive predictive value of 95% to 97% and a negative predictive value of 89%. They reported their findings in a partner journal to Nature.

The algorithm relies on multiple kidney-related metrics typically found in a patient's EMR. Two of the metrics included are serum creatinine levels, which are used to calculate estimated glomerular filtration rate (eGFR), and urine proteinuria using either an albumin-to-creatinine ratio, a dipstick protein result, or a 24-hour urine specimen.

By applying the algorithm to Columbia University Medical Center's entire EMR, they evaluated more than 1.3 million patients. Of those, approximately 673,000 had data allowing for staging assessment. Of those assessed, approximately 240,000 had some stage of CKD. Of these, only about 45,000 had been previously diagnosed with CKD. This allowed the identification of nearly 195,000 patients with previously undiagnosed CKD—allowing for intervention and treatment.

To assure transferability of their algorithm across EMRs, the researchers validated their application across datasets from the University of Minnesota, Vanderbilt University, and the Mayo Clinic. They have provided free, open-source access of their algorithm through the publicly accessible Phenotype KnowledgeBase, available at: phekb.org/phenotype/chronic-kidney-disease.

CKD is frequently unrecognized and commonly undertreated, despite the known fact that it is especially important to diagnose CKD in the early stages when treatment can often slow advancement and reduce complications. Mainstay treatments are relatively straightforward and involve dietary interventions, statins for dyslipidemia, control of hypertension, glycemic control, use of angiotensin-converting enzyme-inhibitors or angiotensin II receptor blockers, and/or use of sodium-glucose cotransporter 2 inhibitors.

This is but one example of how "big data" can be utilized to improve population health and provide a backstop to regular and ongoing health screenings.

References
  • Shang N, et al. Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies. npj Digital Medicine. 2021;4.

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Filed under: Preventive Medicine , Public Health

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