Management Of Acute Kidney Injury Research Articles

Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.

This study reviews the studies utilizing Artificial Intelligence (AI) and AI-driven tools and methods in managing Acute Kidney Injury (AKI). It categorizes the studies according to medical specialties, analyses the gaps in the existing research, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the three most common databases (PubMed, Scopus, and EBSCO), which resulted in 27 eligible studies, published between 2012 and 2023. The study showed significant heterogeneity in the design of the models, with variations in clinical settings, patient characteristics, cohort regions, and statistical methods. Most models were developed for AKI in hospitalized patients, particularly those undergoing surgery or in intensive care units. Compact models with a subset of significant predictors were deemed more clinically applicable than full models with all predictors. The findings suggest that AI tools, such as machine learning (ML) algorithms, have high prediction capabilities despite the dynamic and complex association among the influencing factors and AKI. Based on these findings and the recognized need for broader inclusivity, future studies should consider adopting a more inclusive approach by incorporating diverse healthcare settings, including resource-limited or developing countries. This inclusivity will lead to a more holistic understanding of AKI management challenges and facilitate the development of adaptable and universally applicable AI-driven solutions. Additionally, further investigations should focus on refining AI models to enhance their accuracy and interpretability, promoting seamless integration and implementation of AI-based tools in real-world clinical practice. Addressing these key aspects will elevate the effectiveness and impact of AI-driven approaches in managing AKI.

Prevalence and associated factors of acute kidney injury in Ethiopia, systematic review and meta-analysis.

Acute kidney injury, characterized by a sudden decline in glomerular filtration rate, presents a significant global health challenge, with its high prevalence exacerbated by hypertension and limited healthcare access. This systematic review and meta-analysis examined 19 studies encompassing 11,955 participants to assess acute kidney injury prevalence and associated factors in hospitalized patients inEthiopia. The overall pooled prevalence of acute kidney injury was found to be 3.20% (95% CI 2.91-3.48), with considerable heterogeneity (I2 = 99.3%; p < 0.001). Subgroup analyses identified elevated rates ofacute kidney injury in studies conducted post-2020 (31.656%, 95% CI 24.465-38.848), particularly in the Oromia region (41.622%, 95% CI 11.873-71.372), and smaller study sample (< 400) (44.524%, 95% CI 30.117-58.932). Hypertension, congestive heart failure, sepsis, chronic kidney diseases, and nephrotoxicity emerged as significant risk factors. Regular monitoring of renal function through blood and urine analyses is crucial for early detection and management of acute kidney injury, particularly in high-risk groups. Addressing healthcare disparities and reducing nephrotoxic medication use are essential strategies to mitigate acute kidney injury incidence and improve kidney health outcomes worldwide.

Risk and management of acute kidney injury resulting from hemoglobinuria following pulsed-field ablation in atrial fibrillation

Abstract Background High-voltage pulses can cause hemolysis leading to hemoglobinuria. Purpose We evaluated the prevalence of hemoglobinuria after pulsed-field ablation (PFA) and its impact on renal function in patients with atrial fibrillation (AF). Methods A consecutive series of 128 AF patients undergoing PFA were included in this analysis. The initial patients that did not receive post-ablation hydration were classified as group 1 (n=28) and the rest of the study population that received planned fluid infusion of ≥2 L after the procedure, were categorized as group 2 (n=100). Patients with estimated glomerular filtration rate (eGFR) &amp;lt;45 were excluded. All underwent extensive ablation beyond pulmonary vein isolation. If urine tested positive for hemoglobin, blood samples were tested for Haptoglobin. Results Baseline characteristics of the study population are provided in Table 1. Number of overall PFA applications were comparable between groups (group 1: 52.07±23.99 vs group 2: 59.83±22.78, p=0.149). Four patients in group 1 received 94.63±3.20 PFA applications. In group 2, 4 patients received &amp;gt;90 applications (94±2.82) and 2 patients received &amp;gt;150 (158.5±4.95) PFA applications. Of the 28 in group 1, 21 (75%) developed hemoglobinuria during the 24-hour following catheter ablation. Mean post-ablation S-Cr was significantly higher than the baseline value in these 21 patients (1.46±0.28 vs 0.86±0.24, p &amp;lt;0.001). Of the 21, 4 (19%) patients that received a mean of 94.63±3.20 PFA applications had the S-Cr. &amp;gt;2.5 mg/dL (mean: 2.95±0.21). These 4 patients were also markedly oliguric (&amp;lt;400 ml/day) in the immediate post-ablation period. Group 2 patients received significantly higher amount of fluid infusion following catheter ablation compared to group 1 (2092.53±198.94 vs 487.48±72.74 ml, p&amp;lt;0.001). None of the 100 patients from group 2 developed detectable hemoglobinuria or elevation of S-Cr including the 4 patients with &amp;gt;90 and 2 patients with &amp;gt;150 PFA applications. Mean post-ablation Haptoglobin was 10.00± 8.40 (normal: 36–195 mg/dL). The accuracy of number of applications predicting Haptoglobin decline was excellent with the area-under the curve (AUC) 0.886 ± 0.068 with 95% CI of 0.754 – 1.000. (P-value = 0.002). In multivariable analysis, both hydration (R-square 0.34, p &amp;lt;0.01) and number of PFA applications (R-square 0.40, p &amp;lt;0.038) were independent predictors of post-procedure acute kidney injury. Conclusions Based on our findings, number of PFA applications was an independent predictor of renal insult in patients receiving extensive ablation that could be prevented using planned fluid infusion immediately after the procedure.

Predictors of Acute Kidney Injury Resolution and Associated Clinical Outcomes Among Hospitalized Patients with Cirrhosis.

Background: Acute kidney injury (AKI) is one of the common complications of liver cirrhosis. It occurs in nearly 20% of patients with cirrhosis who are hospitalized. Prior literature demonstrated that the AKI occurrence in patients with cirrhosis is independently associated with higher mortality. However, there are data assessing predictors and outcomes of AKI resolution in hospitalized patients with cirrhosis. Therefore, the aim of the current study was to identify clinical predictors of AKI resolution among inpatients with cirrhosis that are easily obtained and to evaluate the clinical outcomes of those patients. Methods: The current study is a retrospective cohort of patients with cirrhosis who were hospitalized and had AKI between 2012 and 2020 at a tertiary referral center. Patients included in this study were identified using the International Classification of Diseases 9 codes and then they were manually verified by two independent chart reviewers. AKI was classified according to the AKI Network (AKIN) serum creatinine (Cr) criteria, with AKIN resolution defined as AKIN stage 1 or lower at the time of discharge, while unresolved AKIN was defined as AKIN stage 2 or 3 at the time of discharge. For univariate analysis, Fisher's exact and the two-sample T-test were utilized. For multivariable analysis, stepwise logistic regression was performed to evaluate variables associated with AKIN resolution. Survival curves were estimated and compared using the Kaplan-Meier method and Log-Rank Test. A p-value cutoff of 0.05 was used for statistical significance. Results: Between 2012 and 2020, there were 140 patients who were included (59% males). The majority of patients had viral hepatitis (54%) as the cirrhosis etiology with 80% of them having hepatitis C virus. Most patients had fluid-responsive AKI (49%), and stage 1 AKIN (69%). In terms of outcomes, the majority of patients (117 patients; 84%) had AKIN resolution at the time of discharge. In the multivariable analysis, after adjusting for clinical meaningful variables, our study shows that higher albumin value at the time of admission (adjusted Odds Ratio "aOR" = 3.28; p = 0.01) and non-metabolic dysfunction-associated steatotic liver disease (non-MASLD) cirrhosis (aOR = 9.43; p < 0.01) were variables associated with higher odds of AKIN resolution at the time of discharge. Conversely, we show that a higher Cr value at the time of admission was associated with lower odds of AKIN resolution at the time of discharge (aOR = 0.31; p < 0.01). When evaluating mortality, patients with unresolved AKIN at the time of discharge had higher rates of in-hospital mortality (p < 0.01) compared to those with resolved AKIN. Survival curve analyses using the Kaplan-Meier method indicated that patients with resolved AKIN experienced higher 90-day survival rates (p < 0.01). Additionally, those with resolved AKIN demonstrated greater transplant-free survival compared to patients with unresolved AKIN at both the 1-year (p = 0.04) and 3-year (p < 0.01) follow-ups. Conclusions: When evaluating clinical predictors of AKIN resolution in admitted patients with cirrhosis, our study showed that a higher admission albumin value and non-MASLD etiology of cirrhosis were associated with higher odds of AKIN resolution at the time of discharge. Conversely, a higher admission Cr value was associated with lower odds of AKIN resolution at the time of discharge. We also demonstrate that AKIN resolution during index admission was associated with improved short- and long-term transplant-free survival (up to 3 years). Our findings warrant external validation in larger cohorts to further evaluate the impact of inpatient AKI resolution on cirrhosis outcomes. Our findings can help clinicians predict AKIN outcomes and encourage more aggressive management of AKI, especially in high-risk patients, which can improve mortality.

Proenkephalin A 119-159 in Perioperative and Intensive Care-A Promising Biomarker or Merely Another Option?

Acute kidney injury (AKI) is a severe and prevalent syndrome, primarily observed in intensive care units (ICUs) and perioperative settings. The discovery of a new biomarker for kidney function and injury, capable of overcoming the limitations of traditional markers, has the potential to improve the diagnosis and management of AKI. Proenkephalin A 119-159 (PENK) has emerged as a novel biomarker for AKI and has been validated in various clinical settings. It has demonstrated a faster response to AKI compared to creatinine and has been shown to predict successful weaning from renal replacement therapy in the ICU. PENK has also shown promise as an AKI biomarker in perioperative patients. Additionally, PENK has been proven to be effective in estimating mortality and morbidity in patients undergoing cardiac surgery, and those with traumatic brain injury or ischemic stroke. Incorporating PENK into a novel estimation of the glomerular filtration rate, referred to as the PENK-Crea equation, has yielded promising results.

A global snapshot on health systems capacity for detection, monitoring, and management of acute kidney injury: A multinational study from the ISN-GKHA.

Acute kidney injury (AKI) is a significant cause of morbidity and mortality, especially in low and lower-middle income countries. Data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) were used to evaluate the organization of structures and services for the provision of AKI care in world countries and ISN regions. An international survey of key stakeholders (clinicians, policymakers, and patient advocates) from countries affiliated with the ISN was conducted from July to September 2022 to assess structures and services for AKI care across countries. Main findings of the study show that overall, 167 countries or jurisdictions participated in the survey, representing 97.4% of the world's population. Only 4% of countries had an AKI detection program based on national policy or guideline, and 50% of these countries used a reactive approach for AKI identification (i.e., cases managed as identified through clinical practice). Only 19% of national governments recognized AKI as a healthcare priority. Almost all countries (98% of the countries surveyed) reported capacity to provide acute hemodialysis (HD) for AKI, but in 31% of countries, peritoneal dialysis (PD) was unavailable for AKI. About half of all countries (44% of countries surveyed) provided acute dialysis (HD or PD) via public funding, but funding availability varied across ISN regions, including less than a quarter of countries in Oceania and South East Asia (17%) and Africa (24%) and highest availability in Western Europe (91%). Availability increased with the increasing country income level. Initiatives have been developed to propose and promote optimal care for AKI (including the ISN 0-by-25 initiative), but capacity for optimal AKI care remains low, particularly in low- and lower-middle-income countries. Concerted efforts by the global community are required to close these gaps, to improve AKI outcomes across the world.

Factors associated with acute kidney injury and outcomes in patients with malaria in a district hospital in Rwanda

Introduction: Acute kidney injury (AKI) remains one of the complications of severe malaria. Evidence on associated factors and outcomes for patients with complicated malaria and AKI is limited in Rwanda. Aim: To assess the factors associated with acute kidney injury and outcomes in patients with malaria in a district hospital in Rwanda. Method: A retrospective study design was applied. A census sampling strategy was used to select 122 files of patients admitted as severe malaria patients in 2016- 2017. A developed clinical audit form was used to collect data from patients’ files. Both descriptive and inferential statistics were used to analyze data. Results: Among the confirmed severe malaria files, 44% of participants were over 50 years and 52.5% were males. The majority, (91.5%) had community-based health insurance and 16.3% had acute kidney injury. The significant associated clinical factors were dehydration (p=.01), high-grade fever (p=.002), profuse sweating (p=.034), vomiting (p=.043), and diarrhea (p=.025). Of the 20 patients who developed AKI, 55% completely recovered, 15% died and 30% of cases were transferred to the highest facilities for hemodialysis. Conclusion: The existence of AKI among severe malaria patients was evident with some recovering and others dying. There is a need for educating healthcare professionals, mostly at district hospitals about the diagnosis and management of AKI as a result of complicated malaria. Keywords: Acute kidney injury; severe malaria; complications of malaria.

6769 Rhabdomyolysis In Diabetic Emergencies Highlighting The Necessity Of Prompt Creatine Kinase Monitoring

Abstract Disclosure: A. Tiwari: None. M. Corsten: None. W. salari: None. N. Malik: None. A. Dabaja: None. C. Saad: None. Rhabdomyolysis results from skeletal muscle necrosis, causing the release of intracellular contents into the bloodstream. While its association with life-threatening diabetic emergencies is rare, it remains a significant complication. We detail the case of a 37-year-old male admitted with acute pancreatitis who subsequently developed a hyperosmolar hyperglycemic state accompanied by significant electrolyte imbalances. Upon arrival at our emergency department, he displayed severe dehydration, stupor, a blood glucose level surpassing 2,000 mg/dl, and high anion gap metabolic acidosis. His condition worsened when his creatine kinase levels surged from 210 U/L to 39,000 U/L, leading to oliguric renal failure and the need for hemodialysis. His thyroid profile was unremarkable and was not on any medication. Comprehensive treatment included crystalloids, parenteral insulin, and rigorous ICU monitoring with mechanical ventilation. Following this, the patient showed rapid improvement and was discharged without requiring further dialysis. A thorough history and observation established a rare but strong link between rhabdomyolysis and hyperosmolar hyperglycemic states. These situations can intensify nephrotoxic events, especially in patients already facing severe dehydration and prerenal factors. This case emphasizes the critical need for consistent monitoring of creatine kinase levels in diabetic emergencies. Such monitoring ensures early detection and facilitates effective management of rhabdomyolysis-induced acute kidney injury, reducing potential complications and improving patient outcomes. Presentation: 6/3/2024

Advances in AI-based combination therapies versus standard treatments for heart failure in patients with advanced kidney injury

Background: Recent advancements in AI and Machine Learning (ML) can help manage kidney disease and heart failure by providing predictive analytics and personalized treatment plans, improving diagnostic accuracy, and assisting clinicians in controlling chronic disease symptoms like acute kidney injury (AKI) and Managing Heart Failure (HF). We evaluated AI and ML role in predicting AKI and managing HF patients and it involved analyzing various predictive models and technologies to improve early detection provide personalized treatment, and enhance overall patient outcomes in complex renal and cardiac conditions. Methodology: We conducted our search on databases Scopus, PubMed, and Web of Science. Empirical evidence and state of the art of cutting-edge literature and real-time field research was analyzed. Results: AI advancements in HF management for advanced kidney disease AKI include unsupervised machine learning for risk stratification, reinforcement learning for predictive modeling and dynamic management, and LLMs and chatbots for diagnostic support and patient education. New developments in AI and medical technology are predictive analytics for AKI, automated ultrasound interpretation, AI-enhanced dialysis monitoring, HF risk prediction, remote monitoring, and telehealth integration, and making personal treatment strategies according to patient needs. Innovations like wearable dialysis devices, bioengineered kidney tissues, ECMO, LVADs, TAVR, SGLT2 inhibitors, CRT, and nephroprotective drugs have also improved patient outcomes as these have enabled personalized care and early intervention strategies. Conclusion: It is possible to conclude that AI has revolutionized AKI and HF management. Novel technologies offer precise and adaptive care that strictly addresses individual patient needs.

Research progress on the pathogenesis of AKI complicated by ECMO.

Extracorporeal membrane oxygenation (ECMO) stands as a pivotal intervention for patients grappling with cardiopulmonary insufficiency. However, alongside its therapeutic benefits, ECMO carries the risk of complications, with acute kidney injury (AKI) emerging as a significant concern. The precise pathophysiological underpinnings of AKI in the context of ECMO remain incompletely elucidated. A comprehensive literature review was conducted to explore the epidemiology and pathophysiological mechanisms underlying the utilization of ECMO in the management of AKI. ECMO initiates a multifaceted cascade of inflammatory reactions, encompassing complement activation, endothelial dysfunction, white blood cell activation, and cytokine release. Furthermore, factors such as renal hypoperfusion, ischemia-reperfusion injury, hemolysis, and fluid overload exacerbate AKI. Specifically, veno-arterial ECMO (VA-ECMO) may directly induce renal hypoperfusion, whereas veno-venous ECMO (VV-ECMO) predominantly impacts pulmonary function, indirectly influencing renal function. While ECMO offers significant therapeutic advantages, AKI persists as a potentially fatal complication. A thorough comprehension of the pathogenesis underlying ECMO-associated AKI is imperative for effective prevention and management strategies. Moreover, additional research is warranted to delineate the incidence of AKI secondary to ECMO and to refine clinical approaches accordingly.

Fluid management of acute kidney injury.

Acute kidney injury (AKI) is commonly encountered in critical care medicine as is intravenous fluid therapy. It is accepted that there is interplay between fluid use and AKI, both potentially positive and negative. An understanding of the physiological rationale for fluid is important to help clinicians when considering fluid therapy in patients with, or at risk for AKI; this includes understanding choice of fluid, method of monitoring, administration and clinical sequelae. There is increasing interest in combining both static and dynamic measures to assess fluid balance, fluid responsiveness effects of fluid therapy, which are areas requiring ongoing study to translate this theory into clinically useful practice at the bedside. Whilst the debate of choice of crystalloid in ICU practice continues, further evidence for benefits for balanced solutions emerges in the form of international guidelines and patient data meta-analysis of previously performed trials. This review assesses the physiological rationale for fluid use in ICU cohorts with AKI of various types, as well as a systematic approach for choice of fluid therapy using a number of different variables, which aims to help guide clinicians in managing fluid use and fluid balance in critically ill patients with AKI.

Hemodynamic management of acute kidney injury.

To discuss the role of hemodynamic management in critically ill patients with acute kidney injury. Acute kidney injury (AKI) may be associated with persistent alterations in renal perfusion, even when cardiac output and blood pressure are preserved. The effects of interventions aiming at increasing renal perfusion are best evaluated by renal Doppler or contrast enhance ultrasound. However, limited data have been acquired with these techniques and the essential of the literature is based on surrogates of renal function such as incidence of use of renal replacement therapy. Fluids may increase renal perfusion but their effects are quite unpredictable and can be dissociated from their impact on cardiac output and arterial pressure. Inotropes can also be used in selected conditions. At the de-escalation phase, fluid withdrawal should be considered. Safe fluid withdrawal may be achieved when applied in selected patients with preserved tissue perfusion presenting signs of fluid intolerance. When applied, stopping rules should be set. Dobutamine, milrinone and levosimendan increase renal perfusion in AKI associated with cardiac failure or after cardiac surgery. However, the impact of these agents in sepsis is not well defined. Regarding vasopressors, norepinephrine is the first-line vasopressor agent, but vasopressin derivative may limit the requirement of renal replacement therapy. Angiotensin has promising effects in a limited size post-Hoc analysis of a RCT, but these data need to be confirmed. While correction of severe hypotension is associated with improved renal perfusion and function, the optimal mean arterial pressure (MAP) target level remains undefined, Systematic increase in MAP results in variable changes in renal perfusion. It sounds reasonable to individualize MAP target, paying attention to central venous and intraabdominal pressures, as well as to the response to an increase in MAP. Recent studies have refined the impact of the various hemodynamic interventions on renal perfusion and function in critically ill patients with AKI. Though several of these interventions improve renal perfusion, their impact on renal function is more variable.

Methods for phenotyping adult patients with acute kidney injury: a systematic review.

Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and predict disease trajectories. We conducted a systematic review of the literature from 2017 to 2022, focusing on studies that utilized machine learning techniques to identify AKI subphenotypes in adult patients. Data were extracted regarding patient demographics, clustering methodologies, discriminators, and validation efforts from selected studies. The review highlights significant variability in subphenotype identification across different populations. All studies utilized clinical data such as comorbidities and laboratory variables to group patients. Two studies incorporated biomarkers of endothelial activation and inflammation into the clinical data to identify subphenotypes. The primary discriminators were comorbidities and laboratory trajectories. The association of AKI subphenotypes with mortality, renal recovery and treatment response was heterogeneous across studies. The use of diverse clustering techniques contributed to variability, complicating the application of findings across different patient populations. Identifying AKI subphenotypes enables clinicians to better understand and manage individual patient trajectories. Future research should focus on validating these phenotypes in larger, more diverse cohorts to enhance their clinical applicability and support personalized medicine in AKI management.

Advances in critical care nephrology through artificial intelligence.

This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology. AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24-48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation. The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.

Management Of Septic Shock Et Causa Secondary Peritonitis With Complications Acute Kidney Injury And Hospital Acquired Pneumonia In Treated Patients In The Intensive Care Unit

Bacground: Sepsis and Septic shock are reported to be the most common causes of death in the Intensive Care Unit (ICU). The reported incidence of sepsis in the world varies between 100-300 per 100,000 world population per year. Data in Indonesia itself is still limited. Peritonitis is a case of sepsis that often occurs. The mortality rate increases when it develops into septic shock. It can occur together with nosocomal infections. More than 40% of patients with septic shock also suffer from acute kidney injury (AKI). One case of sepsis which developed into septic shock due to secondary peritonitis, which was also complicated by acute kidney injury and hospital acquired pneumonia (HAP) was reported. Case Report: Male, 51 years old, admitted to the Intensive Care Unit with sepsis due to secondary peritonitis, underwent laparotomy. In the course of treatment, experienced septic shock, human acquired pneumonia, acute kidney injury, hyperkalemia, hypoalbuminemia. Treated for 6 days with mechanical ventilation, given antibiotics according to culture and vasopressor support. Improvement occurred on day 6, with minimal vasopressor support as well as resolution and improvement. Conclusion: Early management of critical patients regarding fluid resuscitation, mechanical ventilation management, antibiotic management, AKI management and management of HAP according to culture, is expected to reduce mortality rates and provide good outcomes

Understanding fluid dynamics and renal perfusion in acute kidney injury management.

Acute kidney injury (AKI) is associated with an increased risk of morbidity, mortality, and healthcare expenditure, posing a major challenge in clinical practice, and affecting about 50% of patients in the intensive care unit (ICU), particularly the elderly and those with pre-existing chronic comorbidities. In health, intra-renal blood flow is maintained and auto-regulated within a wide range of renal perfusion pressures (60-100 mmHg), mediated predominantly through changes in pre-glomerular vascular tone of the afferent arteriole in response to changes of the intratubular NaCl concentration, i.e. tubuloglomerular feedback. Several neurohormonal processes contribute to regulation of the renal microcirculation, including the sympathetic nervous system, vasodilators such as nitric oxide and prostaglandin E2, and vasoconstrictors such as endothelin, angiotensin II and adenosine. The most common risk factors for AKI include volume depletion, haemodynamic instability, inflammation, nephrotoxic exposure and mitochondrial dysfunction. Fluid management is an essential component of AKI prevention and management. While traditional approaches emphasize fluid resuscitation to ensure renal perfusion, recent evidence urges caution against excessive fluid administration, given AKI patients' susceptibility to volume overload. This review examines the main characteristics of AKI in ICU patients and provides guidance on fluid management, use of biomarkers, and pharmacological strategies.

A Ferroptosis‐Inhibitive Polypeptosome for Synergistic Management of Acute Kidney Injury Through Antioxidation and Iron Binding

AbstractAcute kidney injury (AKI) is a critical condition with a wide range of triggers that cause heavy burden on public health. Despite its high incidence and mortality, the treatment of AKI still highly relies on hemodialysis, which is costly and usually accompanied by severe complications. Therefore, development of new therapeutic targets and methods for AKI is urgently needed. Herein, a ferroptosis‐targeting strategy for AKI treatment by rationally designed ferroptosis‐inhibitive polypeptide‐based polymersomes (polypeptosomes, FIPs) is proposed. Such FIPs are self‐assembled from poly(glutamic acid)‐block‐poly(methionine‐stat‐tyrosine) [PGA9‐b‐P(Met8‐stat‐PTyr13)]. The ferroptosis‐inhibition function of the FIPs arises from the rationally‐selected amino acid composition: P(Met‐stat‐Tyr) endows the FIPs with a high scavenging efficiency of over 90% toward different reactive oxygen species (ROS); while the glutamate and tyrosine residues, as well as the amide groups, can bind iron ions, thus blocking iron overload and lipid peroxide in cells. In vivo experiments confirm that the FIPs can effectively alleviate oxidative stress, inhibit ferroptosis, and restore kidney functions. Transcriptome sequencing further reveals the therapeutic mechanisms of FIPs. Overall, such polypeptosomes capable of blocking multiple pathways of ferroptosis open a new avenue for AKI treatment and provide fresh insights into the development of generalized therapies for inflammation.

A case report of hemodialysis management of acute kidney injury in a Russell’s viper-envenomed dog

A four-year-old Russell’s viper-envenomed dog presented with severe azotemia and was diagnosed with acute kidney injury (AKI). Russell’s viper envenomation-induced AKI is highly fatal and medical treatment alone was insufficient. In the current case, intermittent hemodialysis (IHD) was implemented and was found to be highly efficient in removing uremic toxins from the bloodstream of the envenomed dog. After two dialysis sessions, the patient’s renal function levels were markedly improved and clinical signs at presentation were resolved. This case report describes the medical treatment and detailed guidelines for the management of IHD in a Russell’s viper-envenomed AKI dog for veterinarians who consider IHD a suitable therapy option for their patients.

Development and Validation of a Policy Tree Approach for Optimizing Intravenous Fluids in Critically Ill Patients with Sepsis and Acute Kidney Injury.

Intravenous fluids are mainstay of management of acute kidney injury (AKI) after sepsis but can cause fluid overload. Recent literature shows that restrictive fluid strategy may be beneficial in some patients with AKI, however, identifying these patients is challenging. We aimed to develop and validate a machine learning algorithm to identify patients who would benefit from a restrictive fluid strategy. We included patients with sepsis who developed AKI within 48 hours of ICU admission and defined restrictive fluid strategy as receiving <500mL fluids within 24 hours after AKI. Our primary outcome was early AKI reversal within 48 hours of AKI onset, and secondary outcomes included sustained AKI reversal and major adverse kidney events (MAKE) at discharge. We used a causal forest, a machine learning algorithm to estimate individual treatment effects and policy tree algorithm to identify patients who would benefit by restrictive fluid strategy. We developed the algorithm in MIMIC-IV and validated it in eICU database. Among 2,091 patients in the external validation cohort, policy tree recommended restrictive fluids for 88.2%. Among these, patients who received restrictive fluids demonstrated significantly higher rate of early AKI reversal (48.2% vs 39.6%, p<0.001), sustained AKI reversal (36.7% vs 27.4%, p<0.001) and lower rates of MAKE by discharge (29.3% vs 35.1%, p=0.019). These results were consistent in adjusted analysis. Policy tree based on causal machine learning can identify septic patients with AKI who benefit from a restrictive fluid strategy. This approach needs to be validated in prospective trials.

Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.