Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: Development and multi-centric, multi-national external validation of a machine-learning model.

Background: Acute Kidney Injury (AKI) is a major complication in patients admitted to Intensive Care Units (ICU), causing both clinical and economic burden on the healthcare system. This study develops a novel machine-learning (ML) model to predict, with several hours in advance, the AKI episodes of stage 2 and 3 (according to KDIGO definition) acquired in ICU.

Methods: A total of 16'760 ICU adult patients from 145 different ICU centers and 3 different countries (US, Netherland, Italy) are retrospectively enrolled for the study.

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

Objectives: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients.

Methods: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the "AmsterdamUMC database") admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods.

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

Background: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions.

Methods: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development.

[Acute Kidney Injury: the nephrology plus value and competence and a good organization can ameliorate the prognosis].

Epidemiology of Acute Kidney Injury (AKI) has changed radically in the past 15 years: we have observed an exponential increase of cases with high mortality and residual disability, particularly in those patients who need dialysis treatment. Those who survive AKI have an increased risk of requiring dialysis after hospital discharge over the short term as well as long term. They have an increased risk of deteriorating residual kidney function and cardiovascular events as well as a shorter life expectancy.

Outcome in noncritically ill patients with acute kidney injury requiring dialysis: Effects of differing medical staffs and organizations.

Acute kidney injury requiring dialysis (AKI-D) treatment has significantly increased in incidence over the years, with more than 400 new cases per million population/y, 2/3 of which concern noncritically ill patients. In these patients, there are little data on mortality or on information of care organization and its impact on outcome. Specialty training and integrated teams, as well as a high volume of activity, seem to be linked to better hospital outcome.