A clinical, proteomics, and artificial intelligence‐driven model to predict acute kidney injury in patients undergoing coronary angiography. Issue 2 (8th January 2019)

• Record Type: Journal Article
• Authors: Ibrahim, Nasrien E.; McCarthy, Cian P.; Shrestha, Shreya; Gaggin, Hanna K.; Mukai, Renata; Magaret, Craig A.; Rhyne, Rhonda F.; Januzzi, James L.
• Is Part Of: Clinical cardiology. Volume 42:Issue 2(2019)
• Page Start: 292
• Page End: 298
• Publication Date: 2019-01-08
• Subjects: coronary angiography -- kidney injury -- risk prediction -- risk score; Cardiology -- Periodicals; 616.12005
• DOI: 10.1002/clc.23143
• Languages: English
• ISSNs: 0160-9289

Abstract

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 operator (LASSO). Results: Forty‐three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C‐reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen‐like and Factor VII) had a negative association with AKI risk. The final model had a cross‐validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in‐sample AUC of 0.82 ( P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001). Conclusions: We describe a clinical and proteomics‐supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.