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Comparative Study
. 2017 Jul 31;12(7):e0181658.
doi: 10.1371/journal.pone.0181658. eCollection 2017.

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

Benjamin R Zambetti  1 , Fridtjof Thomas  2 , Inyong Hwang  1 , Allen C Brown  1 , Mason Chumpia  1 , Robert T Ellis  1 , Darshan Naik  1 , Rami N Khouzam  1 , Uzoma N Ibebuogu  1 , Guy L Reed  1
Affiliations
Comparative Study

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

Benjamin R Zambetti et al. PLoS One. .

Abstract

Background: 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.

Methods & findings: 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.

Conclusions: 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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Mortality, length of stay and severity of AKI.
A) Mean hospital stay according to the severity of AKI as indicated by stage. B) In-hospital mortality by Stage of AKI. C) Frequency of AKI stage in patients with diagnostic coronary angiography (diagnostic) alone or with PCI. AKI = contrast-induced acute kidney injury, PCI = percutaneous coronary intervention. ***p<0.001 vs. patients with no AKI (none).
Fig 2
Fig 2. Classification of patients at risk for AKI.
ROC curves for A) derivation and B) validation datasets. AUC = area under the curver, ROC = receiver operator characteristic.
See this image and copyright information in PMC

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