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.

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.

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.

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.

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.

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.

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.

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.

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.

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). RMC-035 was considered safe and generally well tolerated when evaluated in four clinical phase 1 studies. METHODS AND ANALYSIS: This is a phase 2, randomised, double-blind, adaptive design, parallel group clinical study that evaluates RMC-035 compared with placebo in approximately 268 cardiac surgical patients at high risk for CS-AKI. RMC-035 is administered as an intravenous infusion. In total, five doses will be given. Dosing is based on presurgery estimated glomerular filtration rate (eGFR), and will be either 1.3 or 0.65 mg/kg.The primary study objective is to evaluate whether RMC-035 reduces the incidence of postoperative AKI, and key secondary objectives are to evaluate whether RMC-035 improves postoperative renal function compared with placebo. A blinded interim analysis with potential sample size reassessment is planned once 134 randomised subjects have completed dosing. An independent data monitoring committee will evaluate safety and efficacy data at prespecified intervals throughout the trial. The study is a global multicentre study at approximately 30 sites. ETHICS AND DISSEMINATION: The trial was approved by the joint ethics committee of the physician chamber Westfalen-Lippe and the University of Münster (code '2021-778 f-A') and subsequently approved by the responsible ethics committees/relevant institutional review boards for the participating sites. The study is conducted in accordance with Good Clinical Practice, the Declaration of Helsinki and other applicable regulations. Results of this study will be published in a peer-reviewed scientific journal. TRIAL REGISTRATION NUMBER: NCT05126303.

Serial Urinary C-C Motif Chemokine Ligand 14 and Risk of Persistent Severe Acute Kidney Injury.

To assess the added prognostic value of serial monitoring of urinary C-C motif chemokine ligand 14 (uCCL14) over that of single measurements, which have been shown to be prognostic for development of persistent severe acute kidney injury (AKI) in critically ill patients. DESIGN: Retrospective observational study. SETTING: Data derived from two multinational ICU studies (Ruby and Sapphire). PATIENTS: Critically ill patients with early stage 2-3 AKI. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed three consecutive uCCL14 measurements at 12-hour intervals after diagnosis of stage 2-3 AKI by Kidney Disease Improving Global Outcomes criteria. Primary outcome was persistent severe AKI, defined as 72 consecutive hours of stage 3 AKI, death, or receipt of dialysis prior to 72 hours. uCCL14 was measured using the NEPHROCLEAR uCCL14 Test on the Astute 140 Meter (Astute Medical, San Diego, CA). Based on predefined, validated cutoffs, we categorized uCCL14 as: low (≤ 1.3 ng/mL), medium (> 1.3 to ≤ 13 ng/mL), or high (> 13 ng/mL). Seventy-five of 417 patients with three consecutive uCCL14 measurements developed persistent severe AKI. Initial uCCL14 category strongly correlated with primary endpoint and, in most cases (66%), uCCL14 category was unchanged over the first 24 hours. Compared with no change and accounting for baseline category, decrease in category was associated with decreased odds of persistent severe AKI (odds ratio [OR], 0.20; 95% CI, 0.08-0.45; p < 0.001) and an increase in category with increased odds (OR, 4.04; 95% CI, 1.75-9.46; p = 0.001). CONCLUSIONS: In one-third of patients with moderate to severe AKI uCCL14 risk category altered over three serial measurements and such changes were associated with altered risk for persistent severe AKI. Serial CCL-14 measurement may detect progression or resolution of underlying kidney pathology and help refine AKI prognosis.

Predicting the Development of Renal Replacement Therapy Indications by Combining the Furosemide Stress Test and Chemokine (C-C Motif) Ligand 14 in a Cohort of Postsurgical Patients.

OBJECTIVES: Optimal timing of renal replacement therapy (RRT) initiation in severe acute kidney injury (AKI) remains controversial. Initiation of treatment early in the course of AKI may lead to some patients undergoing unnecessary RRT, whereas delayed treatment is associated with increased mortality. This study aims to investigate whether the combination of the furosemide stress test (FST) and AKI-associated biomarkers can predict the development of indications for RRT. DESIGN: Single-center, prospective, observational study. SETTING: University Hospital of Muenster, Germany. PATIENTS: Critically ill, postoperative patients with moderate AKI (Kidney Disease: Improving Global Outcomes stage 2) and risk factors for further progression (vasopressors and/or mechanical ventilation) receiving an FST. INTERVENTIONS: Sample collection and measurement of different biomarkers (chemokine [C-C motif] ligand 14 [CCL14], neutrophil gelatinase-associated lipocalin, dipeptidyl peptidase 3). MEASUREMENT AND MAIN RESULTS: The primary endpoint was the development of greater than or equal to one predefined RRT indications (hyperkalemia [≥ 6 mmol/L], diuretic-resistant hypervolemia, high urea serum levels [≥ 150 mg/dL], severe metabolic acidosis [pH ≤ 7.15], oliguria [urinary output < 200 mL/12 hr], or anuria). Two hundred eight patients were available for the primary analysis with 108 having a negative FST (urine output < 200 mL in 2 hr following FST). Ninety-eight patients (47%) met the primary endpoint, 82% in the FST negative cohort. At the time of inclusion, the combination of a negative FST test and high urinary CCL14 levels had a significantly higher predictive value for the primary endpoint with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.82-0.92) compared with FST or CCL14 alone (AUC, 0.79; 95% CI, 0.74-0.85 and AUC, 0.83; 95% CI, 0.77-0.89; p < 0.001, respectively). Other biomarkers showed lower AUCs. CONCLUSIONS: The combination of the FST with the renal biomarker CCL14 predicts the development of indications for RRT.

Outcomes, Healthcare Resource Utilization, and Costs of Overall, Community-Acquired, and Hospital-Acquired Acute Kidney Injury in COVID-19 Patients.

Background: In hospitalized patients with COVID-19, acute kidney injury (AKI) is associated with higher mortality, but data are lacking on healthcare resource utilization (HRU) and costs related to AKI, community-acquired AKI (CA-AKI), and hospital-acquired AKI (HA-AKI). Objectives: To quantify the burden of AKI, CA-AKI, and HA-AKI among inpatients with COVID-19. Methods: This retrospective cohort study included inpatients with COVID-19 discharged from US hospitals in the Premier PINC AI™ Healthcare Database April 1-October 31, 2020, categorized as AKI, CA-AKI, HA-AKI, or no AKI by ICD-10-CM diagnosis codes. Outcomes were assessed during index (initial) hospitalization and 30 days postdischarge. Results: Among 208 583 COVID-19 inpatients, 30%, 25%, and 5% had AKI, CA-AKI, and HA-AKI, of whom 10%, 7%, and 23% received dialysis, respectively. Excess mortality, HRU, and costs were greater for HA-AKI than CA-AKI. In adjusted models, for patients with AKI vs no AKI and HA-AKI vs CA-AKI, odds ratios (ORs) (95% CI) were 3.70 (3.61-3.79) and 4.11 (3.92-4.31) for intensive care unit use and 3.52 (3.41-3.63) and 2.64 (2.52-2.78) for in-hospital mortality; mean length of stay (LOS) differences and LOS ratios (95% CI) were 1.8 days and 1.24 (1.23-1.25) and 5.1 days and 1.57 (1.54-1.59); and mean cost differences and cost ratios were $7163 and 1.35 (1.34-1.36) and $19 127 and 1.78 (1.75-1.81) (all P < .001). During the 30 days postdischarge, readmission LOS was ≥6% longer for AKI vs no AKI and HA-AKI vs CA-AKI; outpatient costs were ≥41% higher for HA-AKI vs CA-AKI or no AKI. Only 30-day new dialysis (among patients without index hospitalization dialysis) had similar odds for HA-AKI vs CA-AKI (2.37-2.8 times higher for AKI, HA-AKI, or CA-AKI vs no AKI). Discussion: Among inpatients with COVID-19, HA-AKI had higher excess mortality, HRU, and costs than CA-AKI. Other studies suggest that interventions to prevent HA-AKI could decrease excess morbidity, HRU, and costs among inpatients with COVID-19. Conclusions: In adjusted models among COVID-19 inpatients, AKI, especially HA-AKI, was associated with significantly higher mortality, HRU, and costs during index admission, and higher dialysis and longer readmission LOS during the 30 days postdischarge. These findings support implementation of interventions to prevent HA-AKI in COVID-19 patients.