For patients with type 2 diabetes or the APOL1-HR genotype, a machine learning test integrating biomarkers and electronic health record data demonstrated improved prediction of kidney failure compared with commonly used clinical models.
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According to Kinsuk Chauhan , MD, MPH, of Icahn School of Medicine at Mount Sinai, and colleagues, diabetic kidney disease from type 2 diabetes accounts for 44% of all patients with end-stage kidney disease, with the APOL1 high-risk genotypes also associated with increased risk for chronic kidney disease progression and eGFR decline that may ultimately result in kidney failure. Source: Adobe Stock “Even though these populations are on average higher risk than the general population, accurate prediction of who will have rapid kidney function decline (RKFD) and worse kidney outcomes is lacking,” the researchers wrote, noting that the current standard of using the kidney failure risk equation to predict ESKD has only been validated in patients who already have kidney disease and not in those with preserved kidney function at baseline.
“Widespread electronic health records (EHR) usage provides the potential to leverage thousands of clinical features,” the researchers added. “Standard statistical approaches are inadequate to leverage this data due to feature volume, unaligned nature of data and correlation structure.”
Collecting plasma samples from the Mount Sinai BioMe Biobank, the researchers measured plasma tumor necrosis factors 1/2 (TNFR 1/2) and kidney injury molecule-1 (KIM-1) in two separate cohorts (871 patients with diabetes; 498 patients with APOL1-HR). Data related to these biomarkers were subsequently integrated, along with EHR data, into the KidneyIntelX prognostic test, which utilizes machine-learning algorithms to predict kidney outcomes. More specifically, the test generates a risk score for a composite outcome of rapid kidney function decline (defined as eGFR decline >5 mL/min/1.73m 2 /year), 40% sustained eGFR decline or kidney failure, stratifying patients into risk categories.
Patients with type […]
For patients with type 2 diabetes or the APOL1-HR genotype, a machine learning test integrating biomarkers and electronic health record data demonstrated improved prediction of kidney failure compared with commonly used clinical models.
read more – copyright by www.healio.com
“Widespread electronic health records (EHR) usage provides the potential to leverage thousands of clinical features,” the researchers added. “Standard statistical approaches are inadequate to leverage this data due to feature volume, unaligned nature of data and correlation structure.”
Collecting plasma samples from the Mount Sinai BioMe Biobank, the researchers measured plasma tumor necrosis factors 1/2 (TNFR 1/2) and kidney injury molecule-1 (KIM-1) in two separate cohorts (871 patients with diabetes; 498 patients with APOL1-HR). Data related to these biomarkers were subsequently integrated, along with EHR data, into the KidneyIntelX prognostic test, which utilizes machine-learning algorithms to predict kidney outcomes. More specifically, the test generates a risk score for a composite outcome of rapid kidney function decline (defined as eGFR decline >5 mL/min/1.73m 2 /year), 40% sustained eGFR decline or kidney failure, stratifying patients into risk categories.
Patients with type […]
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