A Prediction Model for Severe AKI in Critically Ill Adults That Incorporates Clinical and Biomarker Data

Abstract

Background and objectivesCritically ill patients with worsening AKI are at high risk for poor outcomes. Predicting which patients will experience progression of AKI remains elusive. We sought to develop and validate a risk model for predicting severe AKI within 72 hours after intensive care unit admission.Design, setting, participants, & measurementsWe applied least absolute shrinkage and selection operator regression methodology to two prospectively enrolled, critically ill cohorts of patients who met criteria for the systemic inflammatory response syndrome, enrolled within 24–48 hours after hospital admission. The risk models were derived and internally validated in 1075 patients and externally validated in 262 patients. Demographics and laboratory and plasma biomarkers of inflammation or endothelial dysfunction were used in the prediction models. Severe AKI was defined as Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3.ResultsSevere AKI developed in 62 (8%) patients in the derivation, 26 (8%) patients in the internal validation, and 15 (6%) patients in the external validation cohorts. In the derivation cohort, a three-variable model (age, cirrhosis, and soluble TNF receptor-1 concentrations [ACT]) had a c-statistic of 0.95 (95% confidence interval [95% CI], 0.91 to 0.97). The ACT model performed well in the internal (c-statistic, 0.90; 95% CI, 0.82 to 0.96) and external (c-statistic, 0.93; 95% CI, 0.89 to 0.97) validation cohorts. The ACT model had moderate positive predictive values (0.50–0.95) and high negative predictive values (0.94–0.95) for severe AKI in all three cohorts.ConclusionsACT is a simple, robust model that could be applied to improve risk prognostication and better target clinical trial enrollment in critically ill patients with AKI.

Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference

“…Newer developments related to monitoring and evaluating risk progression include e-alert systems, machine-learning algorithms and artificial intelligence for AKI recognition and monitoring, 20,[33][34][35][36] as well as models based upon the renal angina index, 37,38 furosemide stress test (FST), 39 or biomarkers. [40][41][42][43] In revisiting the guideline for AKI, severity of AKI should be based not only upon serum creatinine elevation and urine output, but also upon duration, possibly with the inclusion of biomarkers. The need to increase attention for persistent (>48 hours) AKI should also be considered.…”

A long journey for acute kidney injury biomarkers

“…Although the study had limitations regarding AKI risk prediction, as their main goal was to incorporate new urinary markers as risk factors, the study showed that the inclusion of novel biomarkers could improve the robustness of a prediction model in early periods of critical care. Although there is no consensus regarding using such biomarkers to predict AKI, several have shown promising results [37,53,[78][79][80]. Therefore, a novel biomarker that directly reflects kidney injury may further improve prediction in the future.…”

Clinical examination findings as predictors of acute kidney injury in critically ill patients

“…Recently, Bhatraju et al developed a three variable model to predict severe AKI which performed excellent in a group of 1075 SIRS patients . This model yielded a much higher AUROC compared to our model, possibly explained by differences in AKI stage, and outcome definition.…”

“…This model yielded a much higher AUROC compared to our model, possibly explained by differences in AKI stage, and outcome definition. Moreover, the model was developed and validated internally in a highly selected population and includes a biomarker, which may not be available everywhere …”

“…Incidences up to 60% have been reported, depending on definitions and populations studied . Many studies focus on advanced measures, such as biomarkers or imaging for predicting AKI. These techniques are frequently time‐consuming, costly, and are not available on a global scale .…”