Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU

Arıhan, Okan; Wernly, Bernhard; Lichtenauer, Michael; Franz, Marcus; Kabisch, Bjoern; Muessig, Johanna M.; Masyuk, Maryna; Lauten, Alexander; Schulze, P. Christian; Hoppe, Uta C.; Kelm, Malte; Jung, Christian

doi:10.1371/journal.pone.0191697

Cited by 102 publications

(78 citation statements)

References 19 publications

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“…This is likely because critically ill patients develop hemostatic abnormalities, and PT-INR has been associated with early death among patients with sepsis-associated coagulation disorders [ 42 ]. Similar to previous studies, we also found that BUN and CRP can predict mortality in critically ill patients and for those receiving palliative care [ 43 , 44 ]. A prior study also found that total bilirubin is an excellent predictor of short-term (1-week) mortality in patients with chronic liver failure [ 45 ].…”

Section: Discussionsupporting

confidence: 90%

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Lin¹,

Chen²,

Tang³

et al. 2020

JMIR Med Inform

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Background Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care. Objective We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed. Methods A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set. Results The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group. Conclusions Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.

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Section: Discussionsupporting

confidence: 90%

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Lin¹,

Chen²,

Tang³

et al. 2020

JMIR Med Inform

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“…Glomerular filtration rate (GFR), urea-BUN, and creatinine (Cr) were used to evaluate patients' renal function and fluid status [20]. Both Cr and urea are filtered from the glomerulus due to their small molecular size.…”

Section: Discussionmentioning

confidence: 99%

The Relationship Between Hematological Parameters and Mortality in Cardiovascular Patients With Postcardiac Arrest Syndrome

Erol¹,

Kankılıç²,

Kaya³

et al. 2019

Cureus

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Background: Post-cardiac arrest syndrome is the insufficiency of cardiac and cerebral functions caused by ischemia after sudden cardiac arrest. We aimed to determine the hematological parameters associated with mortality in the intensive care follow-up of patients with postcardiac arrest syndrome. Methods: The hematological parameters of 285 cardiovascular patients who were admitted to the emergency department of Harran University Medical Faculty between 2013 and 2018 and followed up in the intensive care unit with post-cardiac arrest syndrome were examined. A total of 85 patients were included in the study. These parameters were recorded as the time of arrival to the emergency department (0 hour) and hematological parameters at the 24 th and 48 th hours of intensive care follow-up. Results: In the mortality group, albumin (P:0.030), hemoglobin (Hg) (P: 0.049), and hematocrit (HCT) (P: 0.020) values in the blood parameters, at the time of admission to the emergency department, were significantly lower than those in the survival group. Red blood cell distribution width (RDW) (P: 0.009) and urea (P <0.001) values at the time of arrival were higher than the survival group. In the 24 th and 48 th hours, mean hemoglobin (MCHC) (P <0.05) values were lower and RDW (P <0.05) values were higher in the mortality group compared to the survival group. Conclusions: In this retrospective validation, low albumin, Hg, HCT, MCHC, and high RDW and urea levels may increase mortality in cardiovascular patients who develop post-cardiac arrest syndrome within the first 48 hours. Correcting these values early may reduce mortality.

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“…Subjects in the studies had similar protein intakes, irrespective of taking GMP-AAs or AAs. BUN is an indicator of the relationship between nutritional status and protein metabolism of patients [ 36 ]. In the study by van Calcar et al [ 17 ], performed with 11 subjects, BUN was significantly lower and plasma insulin was higher when measured 2.5 h after eating a breakfast containing GMP-AAs.…”

Section: Discussionmentioning

confidence: 99%

The Use of Glycomacropeptide in Patients with Phenylketonuria: A Systematic Review and Meta-Analysis

et al. 2018

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In phenylketonuria (PKU), synthetic protein derived from L-amino acids (AAs) is essential in a low-phenylalanine (Phe) diet. Glycomacropeptide (GMP), an intact protein, is very low in Phe in its native form. It has been modified and adapted for PKU to provide an alternative protein source through supplementation with rate-limiting amino acids (GMP-AAs), although it still contains residual Phe. This review aims to systematically evaluate published intervention studies on the use of GMP-AAs in PKU by considering its impact on blood Phe control (primary aim) and changes in tyrosine control, nutritional biomarkers, and patient acceptability or palatability (secondary aims). Four electronic databases were searched for articles published from 2007 to June 2018. Of the 274 studies identified, only eight were included. Bias risk was assessed and a quality appraisal of the body of evidence was completed. A meta-analysis was performed with two studies with adequate comparable methodology which showed no differences between GMP-AAs and AAs for any of the interventions analysed. This work underlines the scarcity and nature of studies with GMP-AAs interventions. All were short-term with small sample sizes. There is a need for better-designed studies to provide the best evidence-based recommendations.

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Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU

Cited by 102 publications

References 19 publications

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study

The Relationship Between Hematological Parameters and Mortality in Cardiovascular Patients With Postcardiac Arrest Syndrome

The Use of Glycomacropeptide in Patients with Phenylketonuria: A Systematic Review and Meta-Analysis

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