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Ramya K, Mukhopadhyay K, Kumar J. Predictive factors and risk scoring system for acute kidney injury (aki) in sick neonates-a prospective cohort study. Eur J Pediatr 2024; 183:5419-5424. [PMID: 39407040 DOI: 10.1007/s00431-024-05816-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 10/01/2024] [Accepted: 10/07/2024] [Indexed: 11/01/2024]
Abstract
Neonatal AKI (acute kidney injury) is an underreported entity in sick neonates associated with significant morbidity and mortality. This was a prospective cohort study, to study the incidence, risk factors, and outcomes of AKI among sick neonates. We included sick inborn neonates admitted at a level III neonatal intensive care unit. Neonates with congenital anomalies and who died within 72 h of life were excluded. AKI was defined and categorized as per KDIGO guidelines. Two hundred and seventy-six neonates were enrolled over 1 year, of which 115 (42%) had AKI. The incidence of AKI was highest n = 27/38 (71%) among extremely preterm (< 28 weeks) infants. On Cox regression analysis, sepsis, invasive ventilation, acidosis, and perinatal asphyxia were significantly associated with AKI with a hazard ratio (95% CI) of 4 (1.21-13.42), 2.3 (1.32-4.03), 1.9 (1.13-3.36), and 1.5 (1.04-2.31), respectively. The risk prediction model, using the 4 predictors mentioned above, had good diagnostic accuracy (area under the curve, 83.6%) with a sensitivity and specificity of 77% and 80%, respectively. Infants with AKI have significantly higher mortality, compared to those who did not have AKI n = 45/115 (39%) vs. n = 5/161 (3%), p < 0.01. CONCLUSIONS Nearly half of sick neonates admitted to NICU have AKI, which is maximum in extremely preterm infants. Sepsis, invasive ventilation, acidosis, and perinatal asphyxia have good diagnostic accuracy in identifying neonates likely to develop AKI. WHAT IS KNOWN • Asphyxia, prematurity, sepsis, shock, hypotension, drugs, congenital heart diseases contribute to neonatal AKI. WHAT IS NEW • Our simple risk prediction model can be used in sick neonates to identify infants who are at risk for developing AKI.
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Affiliation(s)
- Kagnur Ramya
- Neonatal Unit, Department of Pediatrics, Post Graduate Institute of Medical Education And Research (PGIMER), Chandigarh, India.
| | - Kanya Mukhopadhyay
- Neonatal Unit, Department of Pediatrics, Post Graduate Institute of Medical Education And Research (PGIMER), Chandigarh, India
| | - Jogender Kumar
- Neonatal Unit, Department of Pediatrics, Post Graduate Institute of Medical Education And Research (PGIMER), Chandigarh, India
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Zywno H, Figiel W, Grat M, Nazarewski S, Galazka Z, Malyszko J. Can Novel Biomarkers Effectively Predict Acute Kidney Injury in Liver or Kidney Transplant Recipients? Int J Mol Sci 2024; 25:12072. [PMID: 39596140 PMCID: PMC11593440 DOI: 10.3390/ijms252212072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Revised: 11/05/2024] [Accepted: 11/09/2024] [Indexed: 11/28/2024] Open
Abstract
Acute kidney injury (AKI) constitutes a common complication associated with liver or kidney transplantation, which may significantly impact the graft condition and perioperative mortality. Current AKI diagnostic criteria based on serum creatinine (sCr) and urine output alterations are widely utilized in routine clinical practice. However, the diagnostic value of sCr may be limited by various confounding factors, including age, sex, reduced or increased muscle mass, and pre-existing chronic kidney disease (CKD). Furthermore, sCr is rather a late indicator of AKI, as its concentration tends to increase only when the severity of the injury is enough to decrease the estimated glomerular filtration rate (eGFR). Recent expertise highlights the need for novel biomarkers in post-transplantation AKI diagnosis, prediction of event-associated mortality, or evaluation of indications for renal replacement treatment (RRT). Over the last decade, the diagnostic performance of various AKI biomarkers has been assessed, among which some showed the potential to outperform sCr in AKI diagnosis. Identifying susceptible individuals, early diagnosis, and prompt intervention are crucial for successful transplantation, undisturbed graft function in long-term follow-up, and decreased mortality. However, the research on AKI biomarkers in transplantation still needs to be explored. The field lacks consistent results, rigorous study designs, and external validation. Considering the rapidly growing prevalence of CKD and cirrhosis that are associated with the transplantation at their end-stage, as well as the existing knowledge gap, the aim of this article was to provide the most up-to-date review of the studies on novel biomarkers in the diagnosis of post-transplantation AKI.
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Affiliation(s)
- Hubert Zywno
- Department of Nephrology, Dialysis, and Internal Diseases, University Clinical Centre, Medical University of Warsaw, 02-097 Warsaw, Poland;
- Doctoral School of Medical University of Warsaw, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Wojciech Figiel
- Department of General, Transplant, and Liver Surgery, University Clinical Centre, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Michal Grat
- Department of General, Transplant, and Liver Surgery, University Clinical Centre, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Slawomir Nazarewski
- Department of General, Endocrinological, and Vascular Surgery, University Clinical Centre, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Zbigniew Galazka
- Department of General, Endocrinological, and Vascular Surgery, University Clinical Centre, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Jolanta Malyszko
- Department of Nephrology, Dialysis, and Internal Diseases, University Clinical Centre, Medical University of Warsaw, 02-097 Warsaw, Poland;
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Raina R, Nada A, Shah R, Aly H, Kadatane S, Abitbol C, Aggarwal M, Koyner J, Neyra J, Sethi SK. Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions. Pediatr Nephrol 2024; 39:2309-2324. [PMID: 37889281 DOI: 10.1007/s00467-023-06191-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/27/2023] [Accepted: 09/26/2023] [Indexed: 10/28/2023]
Abstract
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.
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Affiliation(s)
- Rupesh Raina
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA.
- Department of Nephrology, Akron Children's Hospital, Akron, OH, USA.
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA.
| | - Arwa Nada
- Le Bonheur Children's Hospital & St. Jude Research Hospital, The University of Tennessee Health Science Center, Memphis, TN, USA
| | - Raghav Shah
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Hany Aly
- Department of Neonatology, Cleveland Clinic Children's, Cleveland, OH, USA
| | - Saurav Kadatane
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Carolyn Abitbol
- Department of Pediatrics, Division of Pediatric Nephrology, University of Miami Miller School of Medicine/Holtz Children's Hospital, Miami, FL, USA
| | - Mihika Aggarwal
- Paediatric Nephrology & Paediatric Kidney Transplantation, Kidney and Urology Institute, Medanta, The Medicity Hospital, Gurgaon, India
| | - Jay Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Pritzker School of Medicine, Chicago, IL, USA
| | - Javier Neyra
- Department of Medicine, Division of Nephrology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sidharth Kumar Sethi
- Paediatric Nephrology & Paediatric Kidney Transplantation, Kidney and Urology Institute, Medanta, The Medicity Hospital, Gurgaon, India
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Berezin AE, Berezina TA, Hoppe UC, Lichtenauer M, Berezin AA. An overview of circulating and urinary biomarkers capable of predicting the transition of acute kidney injury to chronic kidney disease. Expert Rev Mol Diagn 2024; 24:627-647. [PMID: 39007888 DOI: 10.1080/14737159.2024.2379355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/09/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Acute kidney injury (AKI) defined by a substantial decrease in kidney function within hours to days and is often irreversible with higher risk to chronic kidney disease (CKD) transition. AREAS COVERED The authors discuss the diagnostic and predictive utilities of serum and urinary biomarkers on AKI and on the risk of AKI-to-CKD progression. The authors focus on the relevant literature covering evidence of circulating and urinary biomarkers' capability to predict the transition of AKI to CKD. EXPERT OPINION Based on the different modalities of serum and urinary biomarkers, multiple biomarker panel seems to be potentially useful to distinguish between various types of AKI, to detect the severity and the risk of AKI progression, to predict the clinical outcome and evaluate response to the therapy. Serum/urinary neutrophil gelatinase-associated lipocalin (NGAL), serum/urinary uromodulin, serum extracellular high mobility group box-1 (HMGB-1), serum cystatin C and urinary liver-type fatty acid-binding protein (L-FABP) were the most effective in the prediction of AKI-to-CKD transition regardless of etiology and the presence of critical state in patients. The current clinical evidence on the risk assessments of AKI progression is mainly based on the utility of combination of functional, injury and stress biomarkers, mainly NGAL, L-FABP, HMGB-1 and cystatin C.
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Affiliation(s)
- Alexander E Berezin
- Department of Internal Medicine II, Division of Cardiology, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Tetiana A Berezina
- Department of Internal Medicine & Nephrology, VitaCenter, Zaporozhye, Ukraine
| | - Uta C Hoppe
- Department of Internal Medicine II, Division of Cardiology, Paracelsus Medical University, Salzburg, Austria
| | - Michael Lichtenauer
- Department of Internal Medicine II, Division of Cardiology, Paracelsus Medical University, Salzburg, Austria
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Ghazi L, Farhat K, Hoenig MP, Durant TJS, El-Khoury JM. Biomarkers vs Machines: The Race to Predict Acute Kidney Injury. Clin Chem 2024; 70:805-819. [PMID: 38299927 DOI: 10.1093/clinchem/hvad217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/20/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI. CONTENT This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided. SUMMARY The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.
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Affiliation(s)
- Lama Ghazi
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Kassem Farhat
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Melanie P Hoenig
- Renal Division, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, United States
- Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
| | - Joe M El-Khoury
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, United States
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Yang K, Du G, Liu J, Zhao S, Dong W. Gut microbiota and neonatal acute kidney injury biomarkers. Pediatr Nephrol 2023; 38:3529-3547. [PMID: 36997773 DOI: 10.1007/s00467-023-05931-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 04/01/2023]
Abstract
One of the most frequent issues in newborns is acute kidney injury (AKI), which can lengthen their hospital stay or potentially raise their chance of dying. The gut-kidney axis establishes a bidirectional interplay between gut microbiota and kidney illness, particularly AKI, and demonstrates the importance of gut microbiota to host health. Since the ability to predict neonatal AKI using blood creatinine and urine output as evaluation parameters is somewhat constrained, a number of interesting biomarkers have been developed. There are few in-depth studies on the relationships between these neonatal AKI indicators and gut microbiota. In order to gain fresh insights into the gut-kidney axis of neonatal AKI, this review is based on the gut-kidney axis and describes relationships between gut microbiota and neonatal AKI biomarkers.
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Affiliation(s)
- Kun Yang
- Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Department of Perinatology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, 646000, China
| | - Guoxia Du
- Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Department of Perinatology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, 646000, China
| | - Jinjing Liu
- Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Department of Perinatology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, 646000, China
| | - Shuai Zhao
- Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Department of Perinatology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Sichuan Clinical Research Center for Birth Defects, Luzhou, 646000, China
| | - Wenbin Dong
- Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
- Department of Perinatology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
- Sichuan Clinical Research Center for Birth Defects, Luzhou, 646000, China.
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Hu J, Ananth D, Sethi SK, Taliwal N, Govindan S, Raina R. Neonatal AKI: An update. J Neonatal Perinatal Med 2023; 16:361-373. [PMID: 37718869 DOI: 10.3233/npm-230120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
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.
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Affiliation(s)
- J Hu
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - D Ananth
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - S K Sethi
- Pediatric Nephrology & Pediatric Kidney Transplantation, Kidney and Urology Institute, Medanta, The Medicity Hospital, Gurgaon, India
| | - N Taliwal
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA
| | - S Govindan
- Department of Pediatric Nephrology, Dr. Mehta's Hospitals, Chetpet and Vellapanchavadi, Chennai, India
| | - R Raina
- Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA
- Department of Nephrology, Akron Children's Hospital, Akron, OH, USA
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