Mortality prediction of serum neutrophil gelatinase-associated lipocalin in patients requiring continuous renal replacement therapy

Park, Yohan; Ban, Tae Hyun; Kim, Hyung Duk; Ko, Eun Jeong; Lee, Jongmin; Kim, Seok Chan; Park, Cheol Whee; Yang, Chul Woo; Kim, Yong Soo; Chung, Byung Ha

doi:10.3904/kjim.2019.446

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…First, Park et al [ 30 ] reported that circulatory diseases such as coronary artery disease, myocardial infarction, HF, cardiomyopathy, myocarditis, and endocarditis were the second-leading indications of CRRT in Korea. Nephrologists in our center reported an association with continuous renal replacement therapy in 2020 [ 31 ]. In their study, the most common cause of indication for continuous renal replacement therapy was sepsis (52.6%) and cardiac disease was the second most common cause (37.5%).…”

Section: Discussionmentioning

confidence: 99%

Role of Renal Replacement Therapy During the Peri-Transplant Period of Heart Transplantation

et al. 2020

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Background Heart transplantation (HT) is the most useful treatment modality for heart failure. Although several studies have reported the impact of acute kidney injury (AKI) on clinical outcomes after transplantation, little is known about the impact of peri-transplant renal replacement therapy (RRT) on clinical outcomes. We compared the clinical outcomes according to RRT use status among patients with AKI during the peri-transplant period. Material/Methods The medical records of 21 patients who underwent HT from January 2006 to May 2019 were reviewed. We assessed the heart failure cause, comorbidities, immunosuppressant type, requirement for extracorporeal membrane oxygenation, AKI incidence, and cardiac and renal functions over time. The patients were divided into 3 groups: those without AKI (non-AKI group, n=6), those who underwent perioperative RRT (RRT group, n=10), and those who did not undergo RRT (non-RRT group, n=5). Results The most common cause of HT was dilated cardiomyopathy (52.4%). Fifteen patients (71.4%) experienced AKI during the peri-transplant period. Among them, 9 (90%) in the RRT group underwent continuous RRT and only 1 (10%) underwent intermittent hemodialysis. Until 6 months after HT, the renal function of the RRT group was worse than that of the non-RRT group (estimated glomerular filtration rate 44.2 vs. 69.2 mL/min/1.73 m 2 , P =0.015), but the differences dissipated by 9 months. Finally, all patients, even in the RRT group, withdrew from dialysis. Conclusions RRT during the peri-transplant period in HT may be a good bridge therapy for renal function recovery in patients with cardiorenal AKI.

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Section: Discussionmentioning

confidence: 99%

Role of Renal Replacement Therapy During the Peri-Transplant Period of Heart Transplantation

et al. 2020

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“…In patients undergoing RRT, NGAL predicts mortality 25 , 26 . As is the case in patients with cardio-renal syndrome or with AKI and underlying cirrhosis, where NGAL has also been suggested to be indicative of disease progression 27 , 28 .…”

Section: Introductionmentioning

confidence: 99%

Plasma neutrophil gelatinase-associated lipocalin independently predicts dialysis need and mortality in critical COVID-19

Engström,

Koozi,

Didriksson

et al. 2024

Sci Rep

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Neutrophil gelatinase-associated lipocalin (NGAL) is a novel kidney injury and inflammation biomarker. We investigated whether NGAL could be used to predict continuous renal replacement therapy (CRRT) and mortality in critical coronavirus disease 2019 (COVID-19). This prospective multicenter cohort study included adult COVID-19 patients in six intensive care units (ICUs) in Sweden between May 11, 2020 and May 10, 2021. Blood was sampled at admission, days two and seven in the ICU. The samples were batch analyzed for NGAL, creatinine, and cystatin c after the end of the study period. Initiation of CRRT and 90-day survival were used as dependent variables in regression models. Of 498 included patients, 494 were analyzed regarding CRRT and 399 were analyzed regarding survival. Seventy patients received CRRT and 154 patients did not survive past 90 days. NGAL, in combination with creatinine and cystatin c, predicted the subsequent initiation of CRRT with an area under the curve (AUC) of 0.95. For mortality, NGAL, in combination with age and sex, had an AUC of 0.83. In conclusion, NGAL is a valuable biomarker for predicting subsequent initiation of CRRT and 90-day mortality in critical COVID-19. NGAL should be considered when developing future clinical scoring systems.

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Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms

Wang,

Sun,

et al. 2024

Annals of Medicine

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Background To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm. Methods The study retrospectively included patients with AKI who underwent CRRT during their initial hospitalization in the United States using the medical information mart for intensive care (MIMIC) database IV (version 2.0), as well as in the intensive care unit (ICU) of Huzhou Central Hospital. Patients from the MIMIC database were used as the training cohort to construct the models (from 2008 to 2019, n = 1068). Patients from Huzhou Central Hospital were utilized as the external validation cohort to evaluate the models (from June 2019 to December 2022, n = 327). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was employed to select features for constructing the model and subsequently established nine ML predictive models. The performance of these nine models on the external validation cohort dataset was comprehensively evaluated based on the area under the receiver operating characteristic curve (AUROC) and the optimal model was selected. A static nomogram and a web-based dynamic nomogram were presented, with a comprehensive evaluation from the perspectives of discrimination (AUROC), calibration (calibration curve) and clinical practicability (DCA curves). Results Finally, 1395 eligible patients were enrolled, including 1068 patients in the training cohort and 327 patients in the external validation cohort. In the training cohort, LASSO regression with cross-validation was employed to select features and nine models were individually constructed. Compared to the other eight models, the Lasso regularized logistic regression (Lasso-LR) model exhibited the highest AUROC (0.756) and the optimal calibration curve. The DCA curve suggested a certain clinical utility in predicting in-hospital mortality among critically ill patients with AKI undergoing CRRT. Consequently, the Lasso-LR model was the optimal model and it was visualized as a common nomogram (static nomogram) and a web-based dynamic nomogram ( https://chsyh2006.shinyapps.io/dynnomapp/ ). Discrimination, calibration and DCA curves were employed to assess the performance of the nomogram. The AUROC for the training and external validation cohorts in the nomogram model was 0.771 (95%CI: 0.743, 0.799) and 0.756 (95%CI: 0.702, 0.809), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.195, while for the external validation cohort, they were 0.849 and 0.197, respectively. The DCA indicated that the model had a certain clinical application value. Conclusions Our study selected the optimal model and visualized it as...

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Mortality prediction of serum neutrophil gelatinase-associated lipocalin in patients requiring continuous renal replacement therapy

Cited by 5 publications

References 29 publications

Role of Renal Replacement Therapy During the Peri-Transplant Period of Heart Transplantation

Role of Renal Replacement Therapy During the Peri-Transplant Period of Heart Transplantation

Plasma neutrophil gelatinase-associated lipocalin independently predicts dialysis need and mortality in critical COVID-19

Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms

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