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. METHODS: At the 31st Acute Disease Quality Initiative (ADQI) meeting, international experts in critical care, nephrology, involving adults and pediatrics, biostatistics and people with lived experience (PWLE) were assembled. We focused on four main areas: (1) patient enrichment strategies, (2) prevention and attenuation studies, (3) treatment studies, and (4) innovative trial designs of studies other than traditional (parallel arm or cluster) randomized controlled trials. Using a modified Delphi process, recommendations and consensus statements were developed based on existing data, with > 90% agreement among panel members required for final adoption. RESULTS: The panel developed 12 consensus statements for clinical trial endpoints, application of enrichment strategies where appropriate, and inclusion of PWLE to inform trial designs. Innovative trial designs were also considered. CONCLUSION: The current lack of specific therapy for prevention or treatment of AKI demands refinement of future clinical trial design. Here we report the consensus findings of the 31st ADQI group meeting which has attempted to address these issues including the use of predictive and prognostic enrichment strategies to enable appropriate patient selection.

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. Methods: A literature search was conducted through PubMed, Embase and Web of Science databases. Included studies contained variables regarding the efficacy of the AI model [the AUC, accuracy, sensitivity, specificity, negative predictive value and positive predictive value]. Only original studies that consisted of cross-sectional studies, prospective and retrospective studies were included, while reviews and self-reported outcomes were excluded. There was no restriction on time and geographic location. Results: Eight studies with 37 032 AKI patients were included, with a mean age of 65.3 years. The in-hospital mortality was 18.0% in the derivation and 15.8% in the validation cohorts. The pooled [95% confidence interval (CI)] AUC was observed to be highest for the broad learning system (BLS) model [0.852 (0.820-0.883)] and elastic net final (ENF) model [0.852 (0.813-0.891)], and lowest for proposed clinical model (PCM) [0.765 (0.716-0.814)]. The pooled (95% CI) AUC of BLS and ENF did not differ significantly from other models except PCM [Delong's test P = .022]. PCM exhibited the highest negative predictive value, which supports this model's use as a possible rule-out tool. Conclusion: Our results show that BLS and ENF models are equally effective as other ML models in predicting in-hospital mortality, with variability across all models. Additional studies are needed.

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. DATA SOURCES: Medical literature regarding topics relevant to precision medicine in AKI, including AKI definitions, epidemiology, and outcomes, novel AKI biomarkers, studies of electronic health records (EHRs), clinical trial design, and observational studies of kidney biopsies in patients with AKI. STUDY SELECTION: English language observational studies, randomized clinical trials, reviews, professional society recommendations, and guidelines on areas related to precision medicine in AKI. DATA EXTRACTION: Relevant study results, statements, and guidelines were qualitatively assessed and narratively synthesized. DATA SYNTHESIS: We synthesized relevant study results, professional society recommendations, and guidelines in this discussion. CONCLUSIONS: AKI is a syndrome that encompasses a wide range of underlying pathologies, and this heterogeneity has hindered the development of novel therapeutics for AKI. Wide-ranging efforts to improve precision in AKI have included the validation of novel biomarkers of AKI, leveraging EHRs for disease classification, and phenotyping of tubular secretory clearance. Ongoing efforts such as the Kidney Precision Medicine Project, identifying subphenotypes in AKI, and optimizing clinical trials and endpoints all have great promise in advancing precision medicine in AKI.

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. Consensus was defined as ≥ 80% agreement (participants answered with 'Yes', or three to four points on a five-point Likert Scale). RESULTS: Key consensus areas for CCL14 test implementation were: identifying challenges and mitigations, developing a comprehensive protocol and pairing it with a treatment plan, and defining the target population. The majority agreed that CCL14 results can help to prioritize AKI management decisions. CCL14 levels above the high cutoff (> 13 ng/mL) significantly changed the level of concern for modifying the AKI treatment plan (p < 0.001). The highest level of concern to modify the treatment plan was for discussions on renal replacement therapy (RRT) initiation for CCL14 levels > 13 ng/mL. The level of concern for discussion on RRT initiation between High and Low, and between Medium and Low CCL14 levels, showed significant differences. CONCLUSION: Real world urinary CCL14 use appears to provide improved care options to patients at risk for persistent severe AKI. Experts believe there is a role for CCL14 in AKI management and it may potentially reduce AKI-disease burden. There is, however, an urgent need for evidence on treatment decisions and adjustments based on CCL14 results.

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). Using a Likert scale, we rated 11 clinical actions based on international guidelines applied to each case before and after CCL14 testing and analyzed the association between the strength and direction of recommendations and CCL14 results. RESULTS: The strength and direction of clinical recommendations were strongly influenced by CCL14 results (P < 0.001 for the interaction). Nine (82%) recommendations for clinical actions were significantly impacted by CCL14 results (P < 0.001 comparing low to highest CCL14 risk category). CONCLUSIONS: Most recommendations for care of patients with stage 2-3 by an international panel of experts were strongly modified by CCL14 test results. This work should set the stage for clinical practice protocols and studies to determine the effects of recommended actions informed by 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. METHODS: In pooled data from 2 studies of critically ill patients with KDIGO stage 2-3 AKI, CCL14 was measured by NEPHROCLEAR™ CCL14 Test on the Astute 140® Meter (low, intermediate, and high categories [1.3 and 13 ng/mL]). Average hourly urine output over 48 h, stage 3 AKI per urine output criterion on day 2, and composite of dialysis or death within 7 days were examined using multivariable mixed and logistic regression models. RESULTS: Of the 497 subjects with median age of 65 (56-74) years, 49% (242/497) were on diuretics. CCL14 concentration was low in 219 (44%), intermediate in 217 (44%), and high in 61 (12%) patients. In mixed regression analysis, hourly urine output over time was different within each CCL14 risk category based on diuretic use due to significant three-way interaction (p < 0.001). In logistic regression analysis, CCL14 risk category was independently associated with low urine output on day 2 per KDIGO stage 3 (adjusted for diuretic use and baseline clinical variables), and composite of dialysis or death within 7 days (adjusted for urine output within 48 h of CCL14 measurement). CONCLUSIONS: CCL14 measured in patients with moderate to severe AKI is associated with urine output trajectory within 48 h, oliguria on day 2, and dialysis within 7 days.

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. Three feature engineering methods (normalization, standardization, and piece-wise linear encoding with decision trees [PLE-DTs]) and 3 architectures (long short-term memory/gated recurrent unit [LSTM/GRU], temporal convolutional network, and time-distributed wrapper with convolutional neural network [TDW-CNN]) were compared in each clinical task. Model discrimination was evaluated using the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). RESULTS: The study comprised 373 825 admissions for training and 256 128 admissions for testing. LSTM/GRU models tied with TDW-CNN models with both obtaining the highest mean AUPRC in 2 tasks, and LSTM/GRU had the highest mean AUROC across all tasks (deterioration: 0.81, AKI: 0.92, infection: 0.87). PLE-DT with LSTM/GRU achieved the highest AUPRC in all tasks. DISCUSSION: When externally validated in 3 clinical tasks, the LSTM/GRU model architecture with PLE-DT transformed data demonstrated the highest AUPRC in all tasks. Multiple models achieved similar performance when evaluated using AUROC. CONCLUSION: The LSTM architecture performs as well or better than some newer architectures, and PLE-DT may enhance the AUPRC in variable-length time series data for predicting clinical outcomes during external validation.

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. RRT dependence at 90 days postdischarge was defined as ≥2 RRT treatments in the last 8 days. Between-group differences were balanced using inverse probability treatment weighting (IPTW). RESULTS: Of 34,804 patients, 3804 patients (from 382 hospitals) had claims coverage for days 83-90 postdischarge. Compared to IHD-treated patients (n = 2740), CRRT-treated patients (n = 1064) were younger; had more admission to large teaching hospitals, surgery, sepsis, shock, mechanical ventilation, but lower prevalence of comorbidities (p < 0.05 for all). Compared to IHD-treated patients, CRRT-treated patients had lower RRT dependence at hospital discharge (26.5% vs. 29.8%, p = 0.04) and lower RRT dependence at 90 days postdischarge (4.9% vs. 7.4% p = 0.006) with weighted adjusted OR (95% CI): 0.68 (0.47-0.97), p = 0.03. Results persisted in sensitivity analyses including patients who died during days 1-90 postdischarge (n = 112) or excluding patients from hospitals with IHD patients only (n = 335), or when excluding patients who switched RRT modalities (n = 451). CONCLUSIONS: Adjusted for potential confounders, the odds of RRT dependence at 90 days postdischarge among survivors of RRT for AKI was 30% lower for those treated first with CRRT vs. IHD, overall and in several sensitivity analyses. SUMMARY: Critically ill patients in intensive care units (ICU) may develop acute kidney injury (AKI) that requires renal replacement therapy (RRT) to temporarily replace the injured kidney function of cleaning the blood. Two main types of RRT in the ICU are called continuous renal replacement therapy (CRRT), which is performed almost continuously, i.e., for >18 h per day, and intermittent hemodialysis (IHD), which is a more rapid RRT that is usually completed in a little bit over 6 h, several times per week. The slower CRRT may be gentler on the kidneys and is more likely to be used in the sickest patients, who may not be able to tolerate IHD. We conducted a data-analysis study to evaluate whether long-term effects on kidney function (assessed by ongoing need for RRT, i.e., RRT dependence) differ depending on use of CRRT vs. IHD. In a very large US linked hospital-discharge/claims database we found that among ICU patients discharge alive after RRT for AKI, fewer CRRT-treated patients had RRT dependence at hospital discharge (26.5% vs. 29.8%, p = 0.04) and at 90 days after discharge (4.9% vs. 7.4% p = 0.006). In adjusted models, RRT dependence at 90 days postdischarge was >30% lower for CRRT than IHD-treated patients. These results from a non-randomized study suggest that among survivors of RRT for AKI, CRRT may result in less RRT dependence 90 days after hospital discharge.

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. In this manuscript, we review the recent literature of clinical trials in sepsis with focus on studies that explore SA-AKI as a primary or secondary outcome. We discuss lessons learned and potential opportunities to improve the design of clinical trials and generate actionable evidence in future research. We specifically discuss the role of enrichment strategies to target populations that are most likely to derive benefit and the importance of patient-centered clinical trial endpoints and appropriate trial designs with the aim to provide guidance in designing future trials.

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. Methods: The analysis combined a 90-day decision tree using CCL14 operating characteristics to predict PS-AKI and clinical outcomes in 66-year-old patients, and a Markov cohort estimating lifetime costs and quality-adjusted life years (QALYs). Cost and QALYs from admission, 30-day readmission, intensive care, dialysis, and death were compared. Clinical and cost inputs were informed by a large retrospective cohort of US hospitals in the PINC AI Healthcare Database. Inputs and assumptions were challenged in deterministic and probabilistic sensitivity analyses. Two-way analyses were used to explore the efficacy and costs of an intervention preventing PS-AKI. Results: Depending on selected costs and early intervention efficacy, CCL14-directed care led to lower costs and more QALYs (dominating) or was cost-effective at the $50,000/QALY threshold. Assuming the intervention would avoid 10% of PS-AKI complications in AKI stage 2-3 patients identified as true positive resulted in 0.066 additional QALYs and $486 reduced costs. Results were robust to substantial parameter variation. Conclusion: The analysis suggests that in the presence of an efficacious intervention preventing PS-AKI, identifying people at risk using CCL14 in addition to SOC is likely to represent a cost-effective use of resources.

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. SUMMARY: Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI. KEY MESSAGES: Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.

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. In general, AKI is more common in older adults, in those with preexisting chronic kidney disease and in those with known risk factors for AKI (including diabetes and hypertension). The development of AKI is associated with an increase in both mortality and morbidity, including the development of post-AKI chronic kidney disease. Currently, AKI is defined by a rise in serum creatinine from either a known or derived baseline value and/or oliguria or anuria. However, clinicians may fail to recognize the initial development of AKI because of a delay in the rise of serum creatinine or because of inaccurate urine output monitoring. This, in turn, delays any putative measures to treat AKI or to limit its degree. Consequently, efforts have focused on new biomarkers associated with AKI that may allow early recognition of this syndrome with the intent that this will translate into improved patient outcomes. Here we outline current biomarkers associated with AKI and explore their potential in aiding diagnosis, understanding the pathophysiology and directing therapy.

Interpretation for "sepsis associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative Workgroup" / 中华肾脏病杂志

Sepsis-associated acute kidney injury (SA-AKI) is defined as the presence of acute kidney injury (AKI) in the context of sepsis. In the setting of genetic susceptibility, sepsis can lead to SA-AKI through various mechanisms. Based on differences in pathophysiological mechanisms, SA-AKI is categorized into different "endotypes" and manifests as distinct "subtypes". The combination of biomarkers and predictive models has the potential to early identify high-risk AKI patients and elucidate SA-AKI "endotypes". Volume resuscitation and blood purification are optimized strategies for SA-AKI treatment. Furthermore, clinical research on SA-AKI in children is promising.

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. Unstructured text from clinical notes were converted into concept unique identifiers (CUIs) using the clinical Text Analysis and Knowledge Extraction System. The primary outcome was the development of Kidney Disease Improvement Global Outcomes stage 2 AKI within 7 days after leaving the operating room. We derived unimodal extreme gradient boosting machines (XGBoost) and elastic net logistic regression (GLMNET) models using structured-only data and multimodal models combining structured data with CUI features. Model comparison was performed using the receiver operating characteristic curve (AUROC), with Delong's test for statistical differences. Results: The study cohort included 138 389 adult patient admissions (mean [SD] age 58 [16] years; 11 506 [8%] African-American; and 70 826 [51%] female) across the 2 sites. Of those, 2959 (2.1%) developed stage 2 AKI or higher. Across all data types, XGBoost outperformed GLMNET (mean AUROC 0.81 [95% confidence interval (CI), 0.80-0.82] vs 0.78 [95% CI, 0.77-0.79]). The multimodal XGBoost model incorporating CUIs parameterized as term frequency-inverse document frequency (TF-IDF) showed the highest discrimination performance (AUROC 0.82 [95% CI, 0.81-0.83]) over unimodal models (AUROC 0.79 [95% CI, 0.78-0.80]). Discussion: A multimodality approach with structured data and TF-IDF weighting of CUIs increased model performance over structured data-only models. Conclusion: These findings highlight the predictive power of CUIs when merged with structured data for clinical prediction models, which may improve the detection of postoperative AKI.

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. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

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. Digital health solutions comprise various tools and models to improve care processes and patient outcomes in multiple medical fields. AKI development, progression, recovery, or lack thereof, offers tremendous opportunities for developing, validating, and implementing digital health solutions in multiple settings. This article will review the definitions and components of digital health, the characteristics of AKI that allow digital health solutions to be considered, and the opportunities and threats in implementing these solutions. RECENT FINDINGS: Over the past two decades, the academic output related to the use of digital health solutions in AKI has exponentially grown. While this indicates the growing interest in the topic, most topics are primarily related to clinical decision support by detecting AKI within hospitals or using artificial intelligence or machine learning technologies to predict AKI within acute care settings. However, recently, projects to assess the impact of digital health solutions in more complex scenarios, for example, managing nephrotoxins among adults of pediatric patients who already have AKI, is increasing. Depending on the type of patients, chosen digital health solution intervention, comparator groups, and selected outcomes, some of these studies showed benefits, while some did not indicate additional gain in care processes or clinical outcomes. SUMMARY: Careful needs assessment, selection of the correct digital health solution, and appropriate clinical validation of the benefits while avoiding additional health disparities are moral, professional, and ethical obligations for all individuals using these healthcare tools, including clinicians, data scientists, and administrators.

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. More specifically, we discuss which patients may benefit from intraoperative renal replacement therapy and the impact of hyponatremia and hyperammonemia on the dialysis prescription. Additionally, we discuss the complex interplay between anesthesia and intraoperative renal replacement therapy and how the need for clearance and ultrafiltration changes throughout the different phases of the transplant (preanhepatic, anhepatic, and postanhepatic). Lastly, this review will cover the limited data around patient outcomes following intraoperative renal replacement therapy during liver transplantation as well as the best evidence for when to stop dialysis.

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. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

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. A growing body of evidence has unveiled key aspects of the clinical risk factors, pathobiology, response to treatment and elements of renal recovery that have advanced our ability to detect, prevent and treat SA-AKI. Despite these advancements, SA-AKI remains a critical clinical condition and a major health burden, and further studies are needed to diminish the short and long-term consequences of SA-AKI. We review the current treatment standards and discuss novel developments in the pathophysiology, diagnosis, outcome prediction and management of SA-AKI.

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. In the prospective cohort, ICU providers were allowed to order [TIMP-2]*[IGFBP7] for patients at their discretion, then offered AKI practice recommendations based on the results. Outcomes were compared to a historical cohort in which biomarker values were not reported to clinical teams. RESULTS: There was no difference in 7-day progression to severe AKI between the prospective (n = 116) and historical cohorts (n = 63) when [TIMP-2]*[IGFBP7] ≥0.3 (24 [28%] versus 8 [21%], p = 0.38) despite more stage 1 AKI at time of biomarker measurement in the prospective cohort (58 [67%] versus 9 [23%], p < 0.001). In the prospective cohort, patients with higher [TIMP-2]*[IGFBP7] values were more likely to receive a nephrology consult. Early consultation (within 24 h of biomarker measurement, n = 20) had a nonsignificant trend toward net negative volume balance (-1,787 mL [6,716 mL] versus + 4,974 mL [15,540 mL]) and more diuretic use (19 [95%] versus 8 [80%]) and was associated with less severe AKI (9 [45%] versus 10 [100%], p = 0.004) and inpatient dialysis (2 [10%] versus 7 [70%], p = 0.002) compared to delayed consultation (n = 10). CONCLUSIONS: Despite the prospective cohort having more preexisting stage 1 AKI, there were equal rates of progression to severe AKI in the prospective and historical cohorts. In the setting of [TIMP-2]*[IGFBP7] reporting, there were more nephrology consults in response to elevated biomarker levels. Early nephrology consultation resulted in improved volume balance and favorable outcomes compared to delayed consultation.