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

Abstract

BackgroundIn 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 & findingsIn 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.ConclusionsIn 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.

Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients

“…A burst of immunological and inflammatory activation were the potential causes of further renal injury [12]. Several studies proposed certain risk factors for AKI secondary to AMI, including advanced age [6, 13, 14], admission hyperglycemia [15, 16], impaired renal function at presentation [5, 6, 13], and prolonged duration to coronary reperfusion [17]. There were some prediction scores of AKI after the percutaneous coronary intervention (PCI) for AMI [18–21].…”

Clinical Scoring for Prediction of Acute Kidney Injury in Patients with Acute ST-Segment Elevation Myocardial Infarction after Emergency Primary Percutaneous Coronary Intervention

“…The proportion of patients who underwent elective pPCI in these studies would affect the generalizability of their results to the emergency pPCI population, as the incidence of AKI largely differed between emergency and elective pPCI populations [ 26 ]. To date, only four clinical prediction scores were developed entirely from a sample of STEMI patients who underwent emergency pPCI [ 14 , 19 , 27 , 29 ], including the CCIT score. Most studies defined AKI based on a traditional criterion of contrast-induced nephropathy (i.e., an increase in serum creatinine ≥0.5 mg/dL or ≥25% from baseline value 48 or 72 h after pPCI [ 14 , 19 , 24 , 25 , 26 , 27 ].…”

“…Most studies defined AKI based on a traditional criterion of contrast-induced nephropathy (i.e., an increase in serum creatinine ≥0.5 mg/dL or ≥25% from baseline value 48 or 72 h after pPCI [ 14 , 19 , 24 , 25 , 26 , 27 ]. Some studies defined AKI according to RIFLE and AKIN criteria [ 28 , 29 ]. Our study defined AKI according to the KDIGO criteria, which is a current standard criterion.…”

“…Our study defined AKI according to the KDIGO criteria, which is a current standard criterion. Regardless of methodological differences, most of the clinical prediction scores, including the newly-derived PPCI-AKI score, showed consistent performance at an acceptable level (0.7 ≤ AuROC < 0.8) [ 14 , 19 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

“…Although the definite intervention for AKI after a coronary intervention is not yet settled and a general preventive strategy is usually applied in all patients, individual clinical risk assessment is still essential for risk communication. Several clinical prediction rules for AKI prediction were developed over the years, such as Mehran’s risk score (MRS) [ 24 ], Marenzi risk score [ 14 ], Ghani and Tohamy, (2009) [ 25 ], Tziakas et al, (2013) [ 26 ], AGEF score [ 27 ], Abusaada, Yuan, Sabzwari, Butt, and Maqsood (2017) [ 28 ], and UT-AKI [ 29 ]. As several clinical and methodological heterogeneity were identified among the reported scoring systems, the selection of the most appropriate scoring system to be applied in real world clinical settings could not be based entirely on the reported apparent discriminative ability.…”