Serum and Urine Neutrophil Gelatinase-Associated Lipocalin Levels Measured at Admission Predict Progression to Chronic Kidney Disease in Sepsis-Associated Acute Kidney Injury Patients

Tuan, Pham Ngoc Huy; Quyen, Dao Bui Quy; Khoa, Huynh Van; Loc, Nguyen Duc; My, Pham Van; Dung, Nguyen Huu; Toan, Nguyen Duy; Đỗ, Quyết; Viet, Thang Le

doi:10.1155/2020/8883404

Cited by 9 publications

(10 citation statements)

References 44 publications

(50 reference statements)

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“…This may be due to differences in the study population. A study by Tuan et al studied sepsisassociated AKI patients, however, they predicted progression to chronic kidney disease rather than AKD (39). Therefore, to our knowledge, this is the first study to use longitudinal data to predict the occurrence of AKD with the application of machine learning.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

Lin

Duan

2021

Front. Med.

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Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7–90 days according to Acute Disease Quality Initiative-16.Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD.Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon−renal plays an important role in predicting the occurrence of AKD.

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Section: Discussionmentioning

confidence: 99%

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

Lin

Duan

2021

Front. Med.

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“…[26] Gambar 3. Kurva ROC antara GFR, NGAL serum dan NGAL urin [36] Oleh karena NGAL pada plasma dihasilkan pada proses inflamasi sistemik, kadarnya dapat ditemukan meningkat pada penyakit-penyakit sistemik lain di luar organ ginjal, seperti infeksi sistemik, hipertensi kronis, dan keganasan. NGAL juga diekspresikan pada plak atherosklerosis sehingga dapat mempengaruhi kadar NGAL plasma.…”

Section: Ekspresi Dan Fungsi Dari Ngalunclassified

Neutrophil Gelatinase-Associated Lipocalin (Ngal): Biomarker Urin Potensial Dalam Diagnosis Penyakit Ginjal Kronis

Alnaz¹,

Ridha

Abdillah

2022

ESSENTIAL

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ABSTRACT Introduction: Chronic Kidney Disease (CKD) is a common disease that is still being a public health problem, especially in the elderly, as a result of cardiovascular complication which is the most important cause of death among the elderly in developing countries. This literature review aims to comprehend the diagnostic accuracy of urinary NGAL in CKD. This literature review is made by doing literature searching on search engines such as Google Scholar, PubMed, and Sciencedirect, with utilizing NGAL, uNGAL, biomarker, CKD, and elderly as the keywords. From the process, 37 journals were obtained and used as this literature review references. Discussions: By analyzing the literatures, it is known that NGAL is a polypeptide that belongs to the lipocalin family, where the expression will increase in inflammations, including in the kidneys. In diagnosis of CKD, the diagnostic ability of urinary NGAL shown by AUC values were higher than the other biomarkers. In addition, urinary NGAL has a stronger correlation with the deterioration of kidney function, compared to that of plasma NGAL. Conclusion: In conclusion, urinary NGAL is a potential urinary biomarker in diagnosing CKD in the elderly. Keywords: Biomarker, CKD, NGAL, Urine

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“…40,48,59,60 Good at predicting the progression of AKI to CKD. 61 Systemic inflammation or sepsis can cause increased serum levels which can lead to increased urine levels in the absence of renal injury. 60 X X X X X Acute renal injury: LMW biomarkers of PT injury with normal glomerular function uB2M Q Found in every nucleated cell and forms the light chain component of class I histocompatibility antigens.…”

Section: Estimated Gfrmentioning

confidence: 99%

“…207 Similarly in humans, serum and urine NGAL were good prognostic biomarkers for predicting the progression of AKI to CKD in sepsis-associated AKI patients. 61 Because of potential nonrenal sources of serum NGAL, the association of serum NGAL changes with renal injury is ideally informed by effects on biomarkers of systemic inflammation as well as changes in other renal injury biomarkers.…”

Section: Neutrophil Gelatinase-associated Lipocalinmentioning

confidence: 99%

A Review of Specific Biomarkers of Chronic Renal Injury and Their Potential Application in Nonclinical Safety Assessment Studies

et al. 2021

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A host of novel renal biomarkers have been developed over the past few decades which have enhanced monitoring of renal disease and drug-induced kidney injury in both preclinical studies and in humans. Since chronic kidney disease (CKD) and acute kidney injury (AKI) share similar underlying mechanisms and the tubulointerstitial compartment has a functional role in the progression of CKD, urinary biomarkers of AKI may provide predictive information in chronic renal disease. Numerous studies have explored whether the recent AKI biomarkers could improve upon the standard clinical biomarkers, estimated glomerular filtration rate (eGFR), and urinary albumin to creatinine ratio, for predicting outcomes in CKD patients. This review is an introduction to alternative assays that can be utilized in chronic (>3 months duration) nonclinical safety studies to provide information on renal dysfunction and to demonstrate specific situations where these assays could be utilized in nonclinical drug development. Novel biomarkers such as symmetrical dimethyl arginine, dickkopf homolog 3, and cystatin C predict chronic renal injury in animals, act as surrogates for GFR, and may predict changes in GFR in patients over time, ultimately providing a bridge from preclinical to clinical renal monitoring.

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Serum and Urine Neutrophil Gelatinase-Associated Lipocalin Levels Measured at Admission Predict Progression to Chronic Kidney Disease in Sepsis-Associated Acute Kidney Injury Patients

Cited by 9 publications

References 44 publications

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

Neutrophil Gelatinase-Associated Lipocalin (Ngal): Biomarker Urin Potensial Dalam Diagnosis Penyakit Ginjal Kronis

A Review of Specific Biomarkers of Chronic Renal Injury and Their Potential Application in Nonclinical Safety Assessment Studies

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