A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

ABSTRACT:

Background

Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI).

Hypothesis

Clinical and biomarker data can predict AKI more accurately.

Methods

Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection ope

SUBMITTER: Ibrahim NE 

PROVIDER: S-EPMC6712314 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature