Clinical review: Volume of fluid resuscitation and the incidence of acute kidney injury - a systematic review

Intravenous fluids are widely administered to maintain renal perfusion and prevent acute kidney injury (AKI). However, fluid overload is of concern during AKI. Using the Pubmed database (up to October 2011) we identified all randomised controlled studies of goal-directed therapy (GDT)-based fluid resuscitation (FR) reporting renal outcomes and documenting fluid given during perioperative care. In 24 perioperative studies, GDT was associated with decreased risk of postoperative AKI (odds ratio (OR) = 0.59, 95% confidence interval (CI) = 0.39 to 0.89) but additional fluid given was limited (median: 555 ml). Moreover, the decrease in AKI was greatest (OR = 0.47, 95% CI = 0.29 to 0.76) in the 10 studies where FR was the same between GDT and control groups. Inotropic drug use in GDT patients was associated with decreased AKI (OR = 0.52, 95% CI = 0.34 to 0.80, P = 0.003), whereas studies not involving inotropic drugs found no effect (OR = 0.75, 95% CI = 0.37 to 1.53, P = 0.43). The greatest protection from AKI occurred in patients with no difference in total fluid delivery and use of inotropes (OR = 0.46, 95% CI = 0.27 to 0.76, P = 0.0036). GDT-based FR may decrease AKI in surgical patients; however, this effect requires little overall FR and appears most effective when supported by inotropic drugs

Long-term risk of mortality after acute kidney injury in patients with sepsis: a contemporary analysis

<p>Abstract</p> <p>Background</p> <p>Acute kidney injury (AKI) is associated with increased short-term mortality of septic patients; however, the exact influence of AKI on long-term mortality in such patients has not yet been determined.</p> <p>Methods</p> <p>We retrospectively evaluated the impact of AKI, defined by the "Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease" (RIFLE) classification based on creatinine criteria, on 2-year mortality in a cohort of 234 hospital surviving septic patients who had been hospitalized at the Infectious Disease Intensive Care Unit of our Hospital.</p> <p>Results</p> <p>Mean-follow-up was 21 ± 6.4 months. During this period, 32 patients (13.7%) died. At 6 months, 1 and 2 years of follow-up, the cumulative probability of death of patients with previous AKI was 8.3, 16.9 and 34.2%, respectively, as compared with 2.2, 6 and 8.9% in patients without previous AKI (log-rank, P < 0.0001). In the univariate analysis, age (hazard ratio 1.4, 95% CI 1.2-1.7, P < 0.0001), as well as pre-existing cardiovascular disease (hazard ratio 3.6, 95% CI 1.4-9.4, P = 0.009), illness severity as evaluated by nonrenal APACHE II (hazard ratio 1.3, 95% CI 1.1-1.6, P = 0.002), and previous AKI (hazard ratio 4.2, 95% CI 2.1-8.5, P < 0.0001) were associated with increased 2-year mortality, while gender, race, pre-existing hypertension, cirrhosis, HIV infection, neoplasm, and baseline glomerular filtration rate did not. In the multivariate analysis, however, only previous AKI (hazard ratio 3.2, 95% CI 1.6-6.5, P = 0.001) and age (hazard ratio 1.4, 95% CI 1.2-1.6, P < 0.0001) emerged as independent predictors of 2-year mortality.</p> <p>Conclusions</p> <p>Acute kidney injury had a negative impact on long-term mortality of patients with sepsis.</p

Acute kidney injury in the era of big data: The 15^th Consensus Conference of the Acute Dialysis Quality Initiative (ADQI)

The world is immersed in "big data". Big data has brought about radical innovations in the methods used to capture, transfer, store and analyze the vast quantities of data generated every minute of every day. At the same time; however, it has also become far easier and relatively inexpensive to do so. Rapidly transforming, integrating and applying this large volume and variety of data are what underlie the future of big data. The application of big data and predictive analytics in healthcare holds great promise to drive innovation, reduce cost and improve patient outcomes, health services operations and value. Acute kidney injury (AKI) may be an ideal syndrome from which various dimensions and applications built within the context of big data may influence the structure of services delivery, care processes and outcomes for patients. The use of innovative forms of "information technology" was originally identified by the Acute Dialysis Quality Initiative (ADQI) in 2002 as a core concept in need of attention to improve the care and outcomes for patients with AKI. For this 15th ADQI consensus meeting held on September 6-8, 2015 in Banff, Canada, five topics focused on AKI and acute renal replacement therapy were developed where extensive applications for use of big data were recognized and/or foreseen. In this series of articles in the Canadian Journal of Kidney Health and Disease, we describe the output from these discussions

Utilizing electronic health records to predict acute kidney injury risk and outcomes: Workgroup statements from the 15^th ADQI Consensus Conference

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display