A web-based tool to predict acute kidney injury in patients with ST-elevation myocardial infarction: Development, internal validation and comparison

• Published in: PloS one 2017-07, Vol.12 (7), p.e0181658-e0181658
• Main Authors: Zambetti, Benjamin R, Thomas, Fridtjof, Hwang, Inyong, Brown, Allen C, Chumpia, Mason, Ellis, Robert T, Naik, Darshan, Khouzam, Rami N, Ibebuogu, Uzoma N, Reed, Guy L
• Format: Article
• Language: English
• Subjects: Acute coronary syndromes; Acute kidney failure; Acute Kidney Injury - diagnosis; Aged; Algorithms; Aorta; Area Under Curve; Balloon treatment; Biology and Life Sciences; Biomarkers - blood; Cardiology; Cardiovascular disease; Classification; Computer applications; Coronary vessels; Critical care; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnostic software; Diagnostic systems; Emergency medical care; Family medical history; Female; Heart; Heart attacks; Hospitals; Humans; Hypotension; Internet; Kidney diseases; Kidneys; Laboratories; Male; Medical diagnosis; Medical imaging; Medical personnel; Medicine and Health Sciences; Middle Aged; Morbidity; Mortality; Myocardial infarction; Patient outcomes; Patients; People and Places; Percutaneous Coronary Intervention; Physicians; Predictions; Preventive medicine; Reproducibility of Results; Research and Analysis Methods; Retrospective Studies; Risk; Science; Sensitivity and Specificity; Severity of Illness Index; ST Elevation Myocardial Infarction - diagnosis; Studies; Systole; Treatment Outcome; Ventricle; Web applications; World Wide Web
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0181658

In ST-elevation myocardial infarction (STEMI), acute kidney injury (AKI) may increase subsequent morbidity and mortality. Still, it remains difficult to predict AKI risk in these patients. We sought to 1) determine the frequency and clinical outcomes of AKI and, 2) develop, validate and compare a web-based tool for predicting AKI. In a racially diverse series of 1144 consecutive STEMI patients, Stage 1 or greater AKI occurred in 12.9% and was severe (Stage 2-3) in 2.9%. AKI was associated with increased mortality (5.7-fold, unadjusted) and hospital stay (2.5-fold). AKI was associated with systolic dysfunction, increased left ventricular end-diastolic pressures, hypotension and intra-aortic balloon counterpulsation. A computational algorithm (UT-AKI) was derived and internally validated. It showed higher sensitivity and improved overall prediction for AKI (area under the curve 0.76) vs. other published indices. Higher UT-AKI scores were associated with more severe AKI, longer hospital stay and greater hospital mortality. In a large, racially diverse cohort of STEMI patients, Stage 1 or greater AKI was relatively common and was associated with significant morbidity and mortality. A web-accessible, internally validated tool was developed with improved overall value for predicting AKI. By identifying patients at increased risk, this tool may help physicians tailor post-procedural diagnostic and therapeutic strategies after STEMI to reduce AKI and its associated morbidity and mortality.