, Introduction
Acute kidney injury (AKI) is a critical medical condition, that often requires continuous kidney replacement therapy (CKRT), particularly in severe cases. The period following AKI is crucial and is characterized by a transitional phase known as acute kidney disease (AKD), followed by a sequential phase of chronic kidney disease (CKD) [1]. AKD is defined as a state in which kidney function incompletely recovers after an AKI event, leading to subacute deterioration of kidney function that persists for up to 3 months [2,3]. Positioned between AKI and CKD on the disease spectrum, AKD and its subsequent sequelae require close attention and management due to their potential for worse outcomes [4].
Extensive research in the intensive care unit settings has explored the prevalence of AKI and its progression to AKD and CKD [5–7]. Although the long-term outcomes of AKI have been analyzed [8,9], the specific focus on short-term outcomes (e.g., AKD), particularly concerning the deterioration of kidney function over time, is lacking. Additionally, there is a notable gap in the understanding of biomarkers associated with AKI and AKD and their subsequent disease spectra, as well as in the development of patient-specific treatments to improve kidney function. This gap underscores the urgent need for targeted research on AKI preceding AKD and CKD. Thus, identifying biomarkers and therapeutic strategies is essential to enhance the management and recovery of kidney function following AKI.
This study, involving a consortium linking health medical records, biospecimens, and biosignals in Korean patients with AKI and the subsequent disease spectrum (named the LINKA cohort), aimed to enhance the understanding of AKI and AKD before the onset of CKD using retrospective and prospective cohorts centered on the commencement of the study. This approach offers a comprehensive view of the spectra of AKI, AKD, and CKD by integrating advanced analytical tools to predict and influence outcomes. Ultimately, this study contributes to the development of future guidelines and treatment strategies for managing AKI and AKD and their subsequent disease spectra.
Methods
Ethics statements
The study protocol for LINKA (named from the consortium LINking health medical records, biospecimens, and biosignals in Korean patients with Acute kidney injury) was reviewed and approved by the Institutional Review Boards of each participating center; Hallym University Sacred Heart Hospital (2022-11-015), Seoul National University Hospital (J-2312-022-1489, J-2312-014-1488), Seoul National University Boramae Medical Center (10-2022-112, 10-2023-84), Seoul National University Bundang Hospital (B-2312-871-401, B-2312-871-402), The Catholic University of Korea, Yeouido St. Mary’s Hospital (SC23TNDE0128), Korea University Guro Hospital (2023GR0392), Keimyung University Dongsan Hospital (2023-08-064), and Kyungpook National University Hospital (2023-07-027). In addition, an overarching ethics oversight committee specifically established for LINKA rigorously reviewed the study design and protocol. This committee will continuously oversee and address any ethical issues that arise during the course of the study, ensuring adherence to the highest standards of research ethics and participant safety.
Study design and population
This was a multicenter, observational cohort study that incorporated both a biobank and a databank, specifically targeting patients with AKI requiring CKRT. Nephrologists from eight major university-affiliated hospitals spanning metropolitan and regional areas across Korea participated in this study, along with the inclusion of a diverse patient population.
This study included retrospective and prospective cohorts. The retrospective cohort comprised adult patients aged ≥ 9 years who underwent CKRT between January 2017 and June 2022 in the intensive care unit. The inclusion criteria were based on AKI according to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline [10,11], which defines AKI as an increase in serum creatinine by ≥0.3 mg/dL within 48 hours or an increase to ≥1.5 times baseline within the last 7 days, or a urine volume of less than 0.5 mL/kg/hr for 6 hours. We included AKI patients who had CKRT initiated due to critical changes in fluid, electrolyte, and acid-base balance, including severe hyperkalemia, metabolic acidosis, or complications arising from fluid overload that are unresponsive to medical management. Patients without baseline kidney function data, with unknown kidney function at 3 months, or those who died within 3 months of CKRT initiation were excluded. The study involving the prospective cohort commenced in January 2024 and involves the collection of biospecimens.
Common exclusion criteria for both retrospective and prospective cohorts comprise patients on maintenance dialysis for end-stage kidney disease (ESKD), those with suspected post-renal AKI based on clinical assessments including imaging studies or those undergoing acute intermittent hemodialysis before CKRT, and pregnant women. Through these exclusion criteria, we aimed to maintain a relatively homogenous study population with clinically severe AKI. Patients with advance directives against resuscitation, those enrolled in other randomized trials and those who refused consent were also excluded from this prospective cohort study. Inclusion and exclusion of patients will be conducted with appropriate consultation and discussion with the nephrology department. A schematic of the patient recruitment flowchart is shown in Fig. 1.
Sample size calculation
The sample size for this study was determined using diagnostic power calculations based on our previous research findings [12]. Among the 1,764 patients who underwent CKRT for severe AKI, 331 survivors with confirmed kidney function at 3 months were identified, resulting in a prevalence of 0.1876. Among the 331 patients, 125 developed AKD, equating to a proportion of 0.3776. Setting a width of 0.04 for the 95% confidence interval, the required sample size (n) was calculated to be 288 patients [13]. Further, factoring in the prevalence, the total sample size (N = n/prevalence) was calculated to be approximately 1,535 patients. Considering these aspects, we aimed to recruit approximately 1,500 participants in both the retrospective and prospective cohorts.
Data collection parameters
Table 1 outlines the parameters for data collection in this study, including demographic and clinical information, biospecimens, treatment details, and key outcomes at designated time points from CKRT initiation to final follow-up. The outcomes include weaning status from CKRT (continuing CKRT, transitioned to intermittent dialysis, or not undergoing dialysis) at 7 days, 3 months, 12 months, and the final follow-up, progression to AKD, CKD, ESKD, and all-cause mortality. AKD and CKD are defined by the KDIGO guidelines as an incomplete recovery of renal function after AKI: AKD persists for 7 days to 3 months, and CKD for 3 months or more, irrespective of the cause [10,11]. The retrospective cohort was used for data extraction from existing medical records. Conversely, participants in the prospective cohort or their legal guardians will provide informed consent for the use and collection of medical information and biospecimens. Participants in this cohort had the right to withdraw upon request, ensuring secure handling and potential destruction of their data and biospecimens. A comprehensive framework of the LINKA cohort is illustrated in Fig. 2.
Biospecimen collection procedures, quality control, and standard operating procedures
Biospecimen collection will be conducted as part of the prospective cohort study utilizing a standardized multicenter system for the collection of human-derived biospecimens. The plasma, serum, and urine samples will be collected and stored in cryotubes in a deep freezer (–80 °C). A minimum of five vials of plasma and serum will be stored, each with a volume of 300 μL. Whereas, a minimum of ten vials of urine samples will be stored, each with a volume of 1 mL.
Specimen collection points include at the time of CKRT initiation, at 1 week, 3 months (±1 month), and 1 year (±1 month) after initiation. This study aimed to standardize the collection and management of biospecimens across institutions by adhering to the Central Biobank operating procedures for biospecimen collection and management. Each blood and urine sample will be assigned a 7-digit SPREC (Standard PREanalytical Code) to encode pre-processing information for quality control of the biospecimens [14]. Visual checks for hemolysis will be conducted for serum and plasma samples. The collection and storage protocols have been designed to comply with the containers and standards specified for each type of resource. The reliability of the tests will be ensured via an external quality control conducted by the Korean Association of External Quality Assessment Services.
Biosignal collection procedures
Biosignal collection involves the measurement of vital signs of patients undergoing CKRT in the intensive care unit, including blood pressure, pulse rate, respiratory rate, body temperature, and oxygen saturation. These measurements will be recorded at minimum hourly intervals, with additional measurements performed as required. Electrocardiogram (ECG) and photoplethysmography (PPG) signals will be collected from monitoring devices such as Solar 8000M (GE Healthcare) and Tram-Rac4A (GE Healthcare) and stored at up to 500 Hz. The CKRT machines provide data on the pressure at four ports: ultrafiltration rate, clearance rate, blood flow rate, dialysate and replacement fluid rates, and transmembrane pressure, which is recorded every minute. They could offer real-time insights into both the machine’s operational status and the physiological response of the patient. Changes in machine settings will also be logged in minutes, providing a much more precise time unit compared to those recorded in the actual medical chart. Additionally, information on medication, treatment interventions, and outcomes, such as recovery from AKI and death, will be collected, ensuring ease of access, review, and annotation by researchers through organized database management.
Database development with de-identification
To support our research efforts, we developed a protocol for constructing a high-quality extensive database that leverages cloud-based technologies. We assembled a committee consisting of research experts and advisors to supervise this initiative with a specific focus on standardizing the extraction and storage of biosignal data, including vital signs, ECG, and PPG, in formats such as XML, HL7, and PDF. ECG data will be transformed for integration into HL7 standards in accordance with the ECG waveform data standard regulations certified by the American National Standards Institute. XML transformation facilitates the collection and refinement of electronic health information, thereby promoting compatibility and interoperability within multicenter healthcare systems. Thus, we can efficiently manage data and detect anomalies in real-time using advanced XML decryption and HL7 format processing coupled with real-time server-based monitoring.
In compliance with the guidelines set forth by the Ministry of Health and Welfare, our database development process will incorporate rigorous de-identification measures to safeguard the participants’ privacy. By implementing these techniques, patient information such as names, registration numbers, and visit information collected from various hospitals will be pseudonymized, and data that could potentially identify individuals will be either aggregated, reduced, or masked. This will ensure compliance with the Personal Information Protection Act, and that the database encompassing clinical information, along with unstructured biosignal data, undergoes a rigorous process of de-identification and standardization, thereby establishing a comprehensive and secure repository of health information.
Development of prediction model
We initially utilized logistic and Cox proportional hazards models to identify the risk factors influencing the progression of AKI to AKD, CKD, or ESKD, as well as the risk of mortality. To enhance our analysis, we included interaction terms and time-varying covariates to enable a more nuanced assessment of how risk factors evolve over time or in response to specific interventions. Furthermore, by examining the subgroups within our cohort, we aim to identify disparities in disease progression that could inform more targeted treatment approaches. By leveraging these conventional statistical models, we aimed to identify significant predictors to potentially guide early intervention and inform personalized treatment strategies.
Following the statistical analysis, we will further explore the utility of advanced artificial intelligence (AI) models to predict outcomes. This analysis will incorporate various AI techniques, including gradient boosting machines and graph convolutional networks, to process and analyze a wide spectrum of clinical data, ranging from vital signs to laboratory test results. To address the issue of missing data, which can significantly affect prediction accuracy, we will employ interpolation methods such as forward-fill and gated recurrent unit networks [15]. The most appropriate AI model for prediction will be selected based on its performance and undergo rigorous validation using data from both retrospective and prospective cohorts to ensure robustness and reliability. To enhance the model explainability, we will utilize methods such as Shapley Additive Explanations, providing insights into the relevance and impact of various input features on model prediction [16].
Development of reinforcement learning model
Currently, we are developing a reinforcement learning model with the primary objective of reducing progression to AKD, CKD, and ESKD. The reward function is designed to minimize disease progression via patient-specific interventions such as adjusting CKRT settings and administering vasoactive drugs. It integrates observational features from blood tests and biosignals for a holistic assessment of the treatment impact.
To refine this reinforcement learning, we employed an offline learning method, transformer models such as decision transformers and trajectory transformers, that can effectively handle the intricacies of our dataset [17,18]. Additionally, we considered the use of dead-end discovery methods to enhance the explanatory power of variables and screen for high-risk patient groups [19]. This method identifies optimal treatments by analyzing preprocessed data for key variables, ensuring the adaptability and accuracy of the model, with the ultimate goal of reducing mortality and improving renal recovery through personalized CKRT interventions.
Development of guidelines
The development of guidelines for the prevention and management of AKD and its subsequent disease spectrum involves a collaborative decision-making process. The guidelines aim to define disease characteristics, assess epidemiology and risk factors, and categorize and evaluate underlying causes. They will also include comprehensive management strategies covering various aspects such as blood test types and frequency, inpatient and outpatient care, fluid and nutrition treatment, and pharmacological and dialytic interventions. Additionally, the guidelines address options for CKRT, criteria for initiation and termination, preventive measures, and the evaluation and management of the transition to AKD and CKD.
The methodology for developing these guidelines involves several steps. First, a research group is formed which determines the development strategies, including whether to develop guidelines from scratch or adapt existing ones. In this study, prior guidelines were evaluated and core questions were selected using the PICO (Patient, Intervention, Comparison, and Outcome) framework. Literature reviews were conducted using specific databases and search strategies, and the research findings were evaluated, including meta-analyses, where applicable. Guideline recommendations were drafted based on the evidence and a consensus on the evidence level was reached. The draft guidelines underwent an external review and approval to ensure robustness, evidence-based content, and practicality for clinical applications. This structured approach aimed to standardize care for patients with AKD, thereby promoting optimal outcomes and preventing progression to CKD and ESKD.
Discussion
AKI and its progression to AKD and CKD pose significant challenges in nephrology, necessitating a thorough exploration to improve patient outcomes. This study aimed to leverage a multicenter observational framework to collect a vast array of data, including medical records, biospecimens, and biosignals. By focusing on patients with AKI undergoing CKRT, this study aimed to address critical gaps in the literature concerning epidemiology, clinical trajectory, and outcomes. Further, the emphasis on a diverse patient population across multiple centers enhances the generalizability and applicability of the anticipated findings and paves the way for significant advancements in understanding the continuum of AKI, AKD, and CKD.
Previous prospective cohort studies have underscored the significance of AKI as a precursor to more severe conditions, with studies demonstrating its association with increased risk of death, kidney disease progression, and cardiovascular events [20,21]. Studies on CKRT have also elucidated its variability in prescription and delivery in critically ill patients [22] and revealed key biomarkers (e.g., cystatin C) for successful renal recovery post-CKRT, signifying significant progress in optimizing patient outcomes [23]. However, the transition phase from AKI to AKD or CKD remains poorly understood, with a lack of comprehensive data on the risk factors and biochemical parameters that predict its progression. This gap poses challenges in developing effective interventions and management strategies to mitigate the risk of AKD and CKD. Thus, the current study represents a pioneering research endeavor aimed at addressing the complexities of AKI and AKD using an integrative approach. By harmonizing health medical records with comprehensive biospecimen and biosignal analyses, this study aims to uncover the underlying mechanisms of disease progression and identify novel biomarkers, and risk factors.
A cornerstone of this study is the innovative use of AI to analyze extensive datasets, creating predictive models that signal a paradigm shift in managing AKI and its disease spectrum. Previous studies have documented successful efforts to harness AI for AKI prediction, showing substantial efficacy in terms of predictive accuracy and performance metrics [24,25]. These AI-driven models have the potential to identify patients at high risk of AKD or CKD in the early period, facilitating timely and personalized interventions. Consequently, this technological leap underscores the power of AI in refining prognostication and tailoring care strategies, ultimately optimizing patient outcomes.
The insights derived from this study are expected to play a crucial role in shaping comprehensive treatment guidelines for AKI and its disease spectrum. By analyzing collected biospecimens, we can discover and validate biomarkers that are crucial for understanding the pathophysiology and progression of AKI. Advanced bioinformatic approaches may identify proteins or metabolites that correlate with patients progressing from AKI to AKD and highlight potential therapeutic targets. Precision medicine utilizing AI can explore which treatments improve patient outcomes for specific individuals. These guidelines provide evidence-based recommendations to bridge the pivotal transition from AKI to AKD and CKD and foster standardized care practices. Further, the establishment of such guidelines is expected to enhance the consistency and effectiveness of AKI and its disease spectrum management, thereby mitigating the burden of kidney diseases and their associated healthcare costs.
However, despite its comprehensive approach, this study had some limitations. This phase study exclusively targeted patients with severe AKI who underwent CKRT, potentially limiting the generalizability of our findings to all AKI cases. Moreover, the frequency of biospecimen collection is not optimal, which may restrict our ability to closely monitor disease progression. Consequently, in subsequent phase cohorts (yet to be determined and decided upon the completion of this phase study), diverse AKI patient groups will be gathered, and the frequency of biospecimen collection will be extended or diversified.
In conclusion, this cohort study represents a pivotal advancement in nephrology, leveraging the integration of health records, biospecimens, and biosignals to uncover new insights into the pathophysiology of AKI and AKD, and their disease spectra. Additionally, it identifies biomarkers and develops predictive models for enhanced patient care. This collaborative, multidisciplinary effort highlights the critical role of innovative research in addressing AKI and AKD outcomes. As the findings emerge, they will guide the creation of evidence-based guidelines and treatment strategies, bolstering efforts to combat AKI and AKD. Thus, our study lays a solid foundation for future studies to build upon its contributions.
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