Neonatal acute kidney injury risk stratification score: STARZ study

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

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

Incidence and predictors of acute kidney injury among asphyxiated neonates in comprehensive specialized hospitals, northwest Ethiopia, 2023

Acute kidney injury (AKI) is characterized by a sudden decline in the kidneys' abilities to remove waste products and maintain water and electrolyte homeostasis. This study aims to determine the incidence and predictors of acute kidney injury among neonates with perinatal asphyxia admitted at the neonatal intensive care unit of West Amhara Comprehensive Specialized Hospital, Northwest Ethiopia, 2023. Multicentred institution-based retrospective follow-up study was conducted from October 1, 2021, to September 30, 2023, among 421 perinatal asphyxia neonates. A simple random sampling technique was used. The data were collected using a data extraction checklist from the medical registry of neonates. The collected data were entered into EPI-DATA V.4.6.0.0. and analyzed using STATA V.14. The Kaplan-Meier failure curve and log-rank test were employed. Bivariable and multivariable Cox regression was carried out to identify predictors of Acute kidney injury. Statistical significance was declared at a p ≤ 0.05. The overall incidence of AKI was 54 (95% CI 47.07-62.51) per 100 neonate days. C/S delivery (AHR = 0.64; (95% CI 0.43-0.94), prolonged labor (AHR = 1.43; 95% CI 1.03-1.99) low-birth weight times (AHR = 1.49; (95% CI 1.01-2.20), stage three HIE(AHR: 1.68; (95% CI (1.02-2.77), No ANC follow up (AHR = 1.43; 95% CI 1.9 (1.07-3.43) and Hyperkalemia (AHR = 1.56; 95% CI 1.56 (1.05-2.29); 95% CI) were significant predictors. The incidence rate of acute kidney injury was higher than in other studies conducted on other groups of neonates. Cesarean section delivery, prolonged low birthweight, no Anc follow-up, stage 3 HIE, and neonatal hyperkalemia were predictors of acute kidney injury. However, it needs further prospective study. Therefore, the concerned stakeholders should give due attention and appropriate intervention to these predictors.

Use of furosemide in preterm neonates with acute kidney injury is associated with increased mortality: results from the TINKER registry

BACKGROUND

Diuretics are commonly used in neonatal AKI with the rationale to decrease positive fluid balance in critically sick neonates. The patterns of furosemide use vary among hospitals, which necessitates the need for a well-designed study.

METHODS

The TINKER (The Indian Iconic Neonatal Kidney Educational Registry) study provides a database, spanning 14 centres across India since August 2018. Admitted neonates (≤ 28 days) receiving intravenous fluids for at least 48 h were included. Neonatal KDIGO criteria were used for the AKI diagnosis. Detailed clinical and laboratory parameters were collected, including the indications of furosemide use, detailed dosing, and the duration of furosemide use (in days).

RESULTS

A total of 600 neonates with AKI were included. Furosemide was used in 8.8% of the neonates (53/600). Common indications of furosemide use were significant cardiac disease, fluid overload, oliguria, BPD, RDS, hypertension, and hyperkalemia. The odds of mortality was higher in neonates < 37 weeks gestational age with AKI who received furosemide compared to those who did not receive furosemide 3.78 [(1.60-8.94); p = 0.003; univariate analysis] and [3.30 (1.11-9.82); p = 0.03]; multivariate logistic regression].

CONCLUSIONS

In preterm neonates with AKI, mortality was independently associated with furosemide treatment. The furosemide usage rates were higher in neonates with associated co-morbidities, i.e. significant cardiac diseases or surgical interventions. Sicker babies needed more resuscitation at birth, and died early, and hence needed shorter furosemide courses. Thus, survival probability was higher in neonates treated with long furosemide courses vs. short courses.

Artificial intelligence: a new field of knowledge for nephrologists?

Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

Acute Kidney Injury: Gaps and Opportunities for Knowledge and Growth

Acute kidney injury (AKI) occurs frequently in hospitalized patients, regardless of age or prior medical history. Increasing awareness of the epidemiologic problem of AKI has directly led to increased study of global recognition, diagnostic tools, both reactive and proactive management, and analysis of long-term sequelae. Many gaps remain, however, and in this article we highlight opportunities to add significantly to the increasing bodies of evidence surrounding AKI. Practical considerations related to initiation, prescription, anticoagulation, and monitoring are discussed. In addition, the importance of AKI follow-up evaluation, particularly for those surviving the receipt of renal replacement therapy, is highlighted as a push for global equity in the realm of critical care nephrology is broached. Addressing these gaps presents an opportunity to impact patient care directly and improve patient outcomes.

Management of Acute Kidney Injury in Critically Ill Children

Acute kidney injury (AKI) is common in critically ill patients, affecting almost one in four critically ill children and one in three neonates. Higher stages of AKI portend worse outcomes. Identifying AKI timely and instituting appropriate measures to prevent and manage severe AKI is important, since it is independently associated with mortality. Methods to predict severe AKI should be applied to all critically ill patients. Assessment of volume status to prevent the development of fluid overload is useful to prevent adverse outcomes. Patients with metabolic or clinical complications of AKI need prompt kidney replacement therapy (KRT). Various modes of KRT are available, and the choice of modality depends most on the technical competence of the center, patient size, and hemodynamic stability. Given the significant risk of chronic kidney disease, patients with AKI require long-term follow-up. It is important to focus on improving awareness about AKI, incorporate AKI prevention as a quality initiative, and improve detection, prevention, and management of AKI with the aim of reducing acute and long-term morbidity and mortality.

Neonatal AKI: An update

Neonatal acute kidney injury (AKI) is a common complication, especially in the neonatal intensive care unit, that is associated with long term consequences and poor outcomes. Early detection and treatment is critical. Currently, neonatal AKI is defined with urinary markers and serum creatinine, with limitations on early detection and individual treatment. There have been numerous biomarkers and risk factor scores that have been studied for their ability to predict neonatal AKI. To move towards personalized medicine, neonatal AKI must be categorized into phenotypes and subphenotypes that fully encapsulate the diverse causes and specific treatments. This review aims to advance our understanding of neonatal AKI detection through the use of biomarkers, subphenotypes, and phenotypes to move towards personalized treatment strategies.

Neonatal multiple organ failure after perinatal asphyxia

Perinatal asphyxia is an event with far-reaching consequences that can lead not only to the development of neonatal encephalopathy, but also to multiple organ failure (MOF). This ailment may result from the redistribution of blood flow, which would preserve the perfusion of vital organs such as the heart, brain and adrenal glands at the expense of other organs. The objective of the study was to determine the incidence and aetiopathogenesis of failure in the organs most frequently involved in neonatal MOF following perinatal asphyxia. We conducted a systematic literature search in the PubMed, Scopus and Cochrane Library databases using the MeSH terms (ischemia AND hypoxia AND multiorgan dysfunction AND neonat*), (asphyxia AND multiorgan dysfunction AND neonat*) and (liver/kidney/digestive OR gastrointestinal/heart injury AND ischemia AND hypoxia AND neonat*). We selected clinical and preclinical studies published after 2000 and excluded case series, letters to the editor, cohort studies without comparison groups and abstracts. In this study, we found that MOF associated with perinatal asphyxia is a frequent phenomenon with a relevant impact on neonatal morbidity and mortality, as it can cause changes not only in the kidney, liver and gastrointestinal tract, but also cardiomyopathy if the ailment is protracted or severe.

Validation of the STARZ neonatal acute kidney injury risk stratification score

BACKGROUND

Neonatal acute kidney injury (AKI) is common in neonatal intensive care units (NICU) and leads to worse outcomes. Stratifying neonates into an "at risk" category allows health care providers to objectively recognize opportunities for improvements in quality of care.

METHODS

The "Neonatal AKI Risk Prediction Scoring" was devised as the "STARZ [Sethi, Tibrewal, Agrawal, Raina, waZir]" Score. The STARZ score was derived from our prior multicentre study analysing risk factors for AKI in neonates admitted to the NICU. This tool includes 10 variables with a total score ranging from 0 to 100 and a cut-off score of 31.5. In the present study, the scoring model has been validated in our multicentre cohort of 744 neonates.

RESULTS

In the validation cohort, this scoring model had sensitivity of 82.1%, specificity 91.7%, positive predictive value 81.2%, negative predictive value 92.2% and accuracy 88.8%. Based on the STARZ cut-off score of ≥ 31.5, an area under the receiver operating characteristic (ROC) curve was observed to be 0.932 (95% CI, 0.910-0.954; p < 0.001) signifying that the discriminative power was high. In the validation cohort, the probability of AKI was less than 20% for scores up to 32, 20-40% for scores between 33 and 36, 40-60% for scores between 37 and 43, 60-80% for scores between 44 and 49, and ≥ 80% for scores ≥ 50.

CONCLUSIONS

To promote the survival of susceptible neonates, early detection and prompt interventional measures based on highly evidenced research is vital. The risk of AKI in admitted neonates can be quantitatively determined by the rapid STARZ scoring system. A higher resolution version of the Graphical abstract is available as Supplementary information.

Neonatal Acute Kidney Injury

Acute kidney injury (AKI) is a common occurrence in the neonatal intensive care unit (NICU). In recent years, our knowledge of the incidence and impact of neonatal AKI on outcomes has expanded exponentially. Neonatal AKI has been shown to be associated with adverse outcomes including increased length of mechanical ventilation, prolonged length of stay, and rise in mortality. There has also been increasing work suggesting that neonates with AKI are at higher risk of chronic kidney disease (CKD). In the past, AKI had been defined multiple ways. The utilization of the neonatal modified Kidney Disease: Improving Global Outcomes (KDIGO) criteria as the standard definition for neonatal AKI in research and clinical care has driven the advances in our understanding of neonatal AKI over the last 10 years. This definition has allowed researchers and clinicians to better understand the incidence, risk factors, and outcomes associated with neonatal AKI across populations through a multitude of single-center studies and the seminal, multicenter Assessment of Worldwide Acute Kidney Injury Epidemiology in Neonates (AWAKEN) study. As the impacts of neonatal AKI have become clear, a shift in efforts toward identifying those at highest risk, protocolizing AKI surveillance, improving prevention and diagnosis, and expanding kidney support therapy (KST) for neonates has occurred. These efforts also include improving risk stratification (identifying high risk populations, including those with nephrotoxic medication exposure) and diagnostics (novel biomarkers and diagnostic tools). Recent work has also shown that the targeted use of methylxanthines may prevent AKI in a variety of high-risk populations. One of the most exciting developments in neonatal AKI is the advancement in technology to provide KST to neonates with severe AKI. In this comprehensive review we will provide an overview of recent work and advances in the field of neonatal AKI. This will include a detailed review of (1) the definition of neonatal AKI, (2) the epidemiology, risk factors, and outcomes associated with neonatal AKI, (3) improvements in risk stratification and diagnostics, (4) mitigation and treatment, (5) advancements in the provision of KST to neonates, and (6) the incidence and risk of subsequent CKD.