10 tips on how to use dynamic risk assessment and alerts for AKI.

Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing the length of hospitalization and minimizing resource utilization.

Efficacy and safety of therapeutic alpha-1-microglobulin RMC-035 in reducing kidney injury after cardiac surgery: a multicentre, randomised, double-blind, parallel group, phase 2a trial.

Background: Cardiac surgery invariably triggers acute kidney stress causing adverse renal outcomes. The AKITA study evaluated the efficacy and safety of RMC-035, a novel analogue of alpha-1-microglobulin, for reducing cardiac surgery-associated kidney injury.

Methods: In this randomised double-blind placebo-controlled phase 2a study, we randomly assigned (1:1) adult hospitalised patients undergoing open-chest cardiac surgery at high risk for acute kidney injury (AKI) at 21 sites in North America and Europe to receive either RMC-035 (1.

Recommendations for clinical trial design in acute kidney injury from the 31st acute disease quality initiative consensus conference. A consensus statement.

Purpose: Novel interventions for the prevention or treatment of acute kidney injury (AKI) are currently lacking. To facilitate the evaluation and adoption of new treatments, the use of the most appropriate design and endpoints for clinical trials in AKI is critical and yet there is little consensus regarding these issues. We aimed to develop recommendations on endpoints and trial design for studies of AKI prevention and treatment interventions based on existing data and expert consensus.

Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis.

Background: Acute kidney injury (AKI) is associated with increased morbidity/mortality. With artificial intelligence (AI), more dynamic models for mortality prediction in AKI patients have been developed using machine learning (ML) algorithms. The performance of various ML models was reviewed in terms of their ability to predict in-hospital mortality for AKI patients.

The Road to Precision Medicine for Acute Kidney Injury.

Objectives: Acute kidney injury (AKI) is a common form of organ dysfunction in the ICU. AKI is associated with adverse short- and long-term outcomes, including high mortality rates, which have not measurably improved over the past decade. This review summarizes the available literature examining the evidence of the need for precision medicine in AKI in critical illness, highlights the current evidence for heterogeneity in the field of AKI, discusses the progress made in advancing precision in AKI, and provides a roadmap for studying precision-guided care in AKI.

Development and external validation of deep learning clinical prediction models using variable-length time series data.

Objectives: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected infection).

Materials And Methods: This multicenter retrospective study included admissions at 2 medical centers that spanned 2007-2022. Distinct datasets were created for each clinical task, with 1 site used for training and the other for testing.

CCL14 testing to guide clinical practice in patients with AKI: Results from an international expert panel.

Purpose: Urinary C-C motif chemokine ligand 14 (CCL14) is a strong predictor of persistent stage 3 acute kidney injury (AKI). Multiple clinical actions are recommended for AKI but how these are applied in individual patients and how the CCL14 test results may impact their application is unknown.

Methods: We assembled an international panel of 12 experts and conducted a modified Delphi process to evaluate patients at risk for persistent stage 3 AKI (lasting 72 hours or longer).

Assessing the role of Chemokine (C-C motif) ligand 14 in AKI: a European consensus meeting.

Background: Urinary Chemokine (C-C motif) ligand 14 (CCL14) is a biomarker associated with persistent severe acute kidney injury (AKI). There is limited data to support the implementation of this AKI biomarker to guide therapeutic actions.

Methods: Sixteen AKI experts with clinical CCL14 experience participated in a Delphi-based method to reach consensus on when and how to potentially use CCL14.

CCL14 Predicts Oliguria and Dialysis Requirement in Patients with Moderate to Severe Acute Kidney Injury.

Introduction: AKI is a frequent complication of critical illness and portends poor outcome. CCL14 is a validated predictor of persistent severe AKI in critically ill patients. We examined the association of CCL14 with urine output within 48 h.

Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials.

Acute kidney injury (AKI) often complicates sepsis and is associated with high morbidity and mortality. In recent years, several important clinical trials have improved our understanding of sepsis-associated AKI (SA-AKI) and impacted clinical care. Advances in sub-phenotyping of sepsis and AKI and clinical trial design offer unprecedented opportunities to fill gaps in knowledge and generate better evidence for improving the outcome of critically ill patients with SA-AKI.

Initial renal replacement therapy (RRT) modality associates with 90-day postdischarge RRT dependence in critically ill AKI survivors.

Purpose: Real-world comparison of RRT modality on RRT dependence at 90 days postdischarge among ICU patients discharged alive after RRT for acute kidney injury (AKI).

Methods: Using claims-linked to US hospital discharge data (Premier PINC AI Healthcare Database [PHD]), we compared continuous renal replacement therapy (CRRT) vs. intermittent hemodialysis (IHD) for AKI in adult ICU patients discharged alive from January 1, 2018 to June 30, 2021.

Exploring the Cost-Utility of a Biomarker Predicting Persistent Severe Acute Kidney Injury: The Case of C-C Motif Chemokine Ligand 14 (CCL14).

Background: Approximately 24% of hospitalized stage 2-3 acute kidney injury (AKI) patients will develop persistent severe AKI (PS-AKI), defined as KDIGO stage 3 AKI lasting ≥3 days or with death in ≤3 days or stage 2 or 3 AKI with dialysis in ≤3 days, leading to worse outcomes and higher costs. There is currently no consensus on an intervention that effectively reverts the course of AKI and prevents PS-AKI in the population with stage 2-3 AKI. This study explores the cost-utility of biomarkers predicting PS-AKI, under the assumption that such intervention exists by comparing C-C motif chemokine ligand 14 (CCL14) to hospital standard of care (SOC) alone.

Development and external validation of multimodal postoperative acute kidney injury risk machine learning models.

Objectives: To develop and externally validate machine learning models using structured and unstructured electronic health record data to predict postoperative acute kidney injury (AKI) across inpatient settings.

Materials And Methods: Data for adult postoperative admissions to the Loyola University Medical Center (2009-2017) were used for model development and admissions to the University of Wisconsin-Madison (2009-2020) were used for validation. Structured features included demographics, vital signs, laboratory results, and nurse-documented scores.

Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients.

Digital health utilities in acute kidney injury management.

Purpose Of Review: Acute kidney injury (AKI) is a highly prevalent clinical syndrome that substantially impacts patient outcomes. It is accepted by the clinical communities that the management of AKI is time-sensitive. Unfortunately, despite growing proof of its preventability, AKI management remains suboptimal in community, acute care, and postacute care settings.

Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit.

Background: Sepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output.

New biomarkers in acute kidney injury.

Acute kidney injury (AKI) is a commonly encountered clinical syndrome. Although it often complicates community acquired illness, it is more common in hospitalized patients, particularly those who are critically ill or who have undergone major surgery. Approximately 20% of hospitalized adult patients develop an AKI during their hospital care, and this rises to nearly 60% in the critically ill, depending on the population being considered.

Intraoperative Renal Replacement Therapy in Orthotopic Liver Transplantation.

Acute kidney injury in patients admitted to the hospital for liver transplantation is common, with up to 80% of pretransplant patients having some form of acute kidney injury. Many of these patients start on dialysis prior to their transplant and have it continued intraoperatively during their surgery. This review discusses the limited existing literature and expert opinion around the indications and outcomes around intraoperative dialysis (intraoperative renal replacement therapy) during liver transplantation.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes.

Sepsis-associated acute kidney injury-treatment standard.

Sepsis is a host's deleterious response to infection, which could lead to life-threatening organ dysfunction. Sepsis-associated acute kidney injury (SA-AKI) is the most frequent organ dysfunction and is associated with increased morbidity and mortality. Sepsis contributes to ≈50% of all AKI in critically ill adult patients.

Real-World Use of AKI Biomarkers: A Quality Improvement Project Using Urinary Tissue Inhibitor Metalloprotease-2 and Insulin-Like Growth Factor Binding Protein 7 ([TIMP-2]*[IGFBP7]).

Introduction: Novel urinary biomarkers, including tissue inhibitor metalloprotease-2 and insulin-like growth factor binding protein 7 ([TIMP-2]*[IGFBP7]), have been developed to identify patients at risk for acute kidney injury (AKI). We investigated the "real-world" clinical utility of [TIMP-2]*[IGFBP7] in preventing AKI.

Methods: We performed a before and after single-center quality improvement study of intensive care unit (ICU) patients at risk for severe (KDIGO stage 2 or 3) AKI.

Study protocol of a phase 2, randomised, placebo-controlled, double-blind, adaptive, parallel group clinical study to evaluate the efficacy and safety of recombinant alpha-1-microglobulin in subjects at high risk for acute kidney injury following open-chest cardiac surgery (AKITA trial).

Introduction: Acute kidney injury (AKI) is a common complication after cardiac surgery (CS) and is associated with adverse short-term and long-term outcomes. Alpha-1-microglobulin (A1M) is a circulating glycoprotein with antioxidant, heme binding and mitochondrial-protective mechanisms. RMC-035 is a modified, more soluble, variant of A1M and has been proposed as a novel targeted therapeutic protein to prevent CS-associated AKI (CS-AKI).

Health Care Resource Utilization and Costs of Persistent Severe Acute Kidney Injury (PS-AKI) Among Hospitalized Stage 2/3 AKI Patients.

Key Points: Among hospitalized patients with stage 2/3 AKI, persistent severe acute kidney injury (PS-AKI) is associated with significantly longer length of stay (LOS) and higher costs during index hospitalization and 30 days postdischarge. Relative differences in LOS and costs for PS-AKI versus NPS-AKI were similar for intensive care (ICU) and non-ICU patients. Preventing PS-AKI among patients with stage 2/3 AKI may reduce hospital LOS and costs.