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

Clin Cardiol. 2019 Feb;42(2):292-298. doi: 10.1002/clc.23143. Epub 2019 Jan 8.


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.

Keywords: coronary angiography; kidney injury; risk prediction; risk score.

MeSH terms

  • Acute Kidney Injury / chemically induced
  • Acute Kidney Injury / diagnosis*
  • Acute Kidney Injury / epidemiology
  • Aged
  • Artificial Intelligence*
  • Biomarkers / blood
  • Contrast Media / adverse effects*
  • Coronary Angiography / methods*
  • Coronary Artery Disease / diagnosis
  • Creatinine / blood
  • Female
  • Humans
  • Incidence
  • Male
  • Predictive Value of Tests
  • Prospective Studies
  • Proteomics / methods*
  • ROC Curve
  • Risk Assessment / methods*


  • Biomarkers
  • Contrast Media
  • Creatinine

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