The blood urea nitrogen/creatinine (BUN/cre) ratio was U-shaped associated with all-cause mortality in general population

Shen, Song; Yan, Xudong; Xu, Baojun

doi:10.1080/0886022x.2022.2030359

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…Renal dysfunction has been evaluated as a prognostic factor of all-cause mortality in general population. 22 …”

Section: Discussionmentioning

confidence: 99%

“…Renal dysfunction has been evaluated as a prognostic factor of all-cause mortality in general population. 22 We also compared the CONUT score with other nutritional prognostic indicators (GNRI 15 and PNI 16 17 ), which are candidate indices that can be evaluated in the field of home medical care. The AUC (0.707) of the CONUT score for 5-year survival was higher than that of a standard ROC curve (AUC=0.62; 95% CI 0.580 to 0.665) of the CONUT score for 5-year survival in patients with resected breast cancer.…”

Section: Discussionmentioning

confidence: 99%

“…One report found that serum Hb was associated with in-hospital mortality in older adults,12 but these data only assessed short-term outcomes. Renal dysfunction has been evaluated as a prognostic factor of all-cause mortality in general population 22…”

Section: Discussionmentioning

confidence: 99%

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Impact of the Controlling Nutritional Status (CONUT) score as a prognostic factor for all-cause mortality in older patients without cancer receiving home medical care: hospital ward-based observational cohort study

Miura

Goto

2023

BMJ Open

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ObjectivesMalnutrition in cancer-free older patients receiving home medical care may affect prognosis, but indicators of long-term nutrition-related prognosis have not been developed. This study investigated the utility of the Controlling Nutritional Status (CONUT) score as a prognostic factor for older patients without cancer receiving home medical care.DesignThis was a single-centre, hospital ward-based observational cohort study.Setting and participantsIn total, 625 cancer-free older patients (median age, 81.0 years; 47.4% males) receiving continuous home medical care through clinics were enrolled on admission to a hospital ward from March 2011 to September 2018.Primary outcome measuresContinuous cumulative survival curves were obtained using the Kaplan-Meier method after dividing the CONUT score into four groups. The prognostic factors for overall mortality were evaluated using the Cox proportional hazards model. Comparisons with other predictive tools were performed.ResultsThe Kaplan-Meier curves of CONUT scores revealed a stepwise shortening of the median survival time with increasing scores. The HR of CONUT scores adjusted by age, sex and other confounding variables was 1.422 (95% CI 1.232 to 1.643, p<0.001). The areas under the receiver operating characteristic curve of the CONUT score for 1-year and 5-year survival were 0.684 and 0.707, respectively. The CONUT score displayed greater predictive utility than other nutrition-related predictive tools.ConclusionsThe CONUT score on hospital admission could be used to predict overall mortality in older patients without cancer receiving home medical care. It is expected to be a simpler and cheaper screening tool for assessing the nutritional status in the field of home medical care.

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“…Renal dysfunction has been evaluated as a prognostic factor of all-cause mortality in general population. 22 …”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Impact of the Controlling Nutritional Status (CONUT) score as a prognostic factor for all-cause mortality in older patients without cancer receiving home medical care: hospital ward-based observational cohort study

Miura

Goto

2023

BMJ Open

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“…Acute kidney injury (AKI) is defined by increased serum creatinine or decreased urine volume, which are significantly associated with mortality in sepsis ( Peerapornratana et al, 2019 ), whereas the increase in BUN is not only affected by renal function, but also by stress-nutrition status and bleeding-volume status. A study reported a U-shape relationship between the BUN/creatinine ratio and all-cause mortality in the general population ( Shen et al, 2022 ). Therefore, some important features may be missed due to the nonlinear relationship between features and risks in logistic regression.…”

Section: Discussionmentioning

confidence: 99%

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learning

Chen²,

Zheng³

2022

Front. Microbiol.

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BackgroundAcinetobacter baumannii complex-caused bloodstream infection (ABCBSI) is a potentially fatal infection in intensive care units (ICUs). This study proposed an interpretable machine learning (ML) model to predict ABCBSI fulminant fatality.MethodsA retrospective study of ICU patients with ABCBSI was performed in China from 2009 to 2020. Patients were stratified into two groups: those that suffered from fulminant sepsis and died within 48 h, and those that survived for more than 48 h. The clinical score systems and ML models with Shapley additive explanation (SHAP) were used to develop the prediction models. The ML model was internally validated with five-fold cross-validation, and its performance was assessed using seven typical evaluation indices. The top 20 features ranked by the SHAP scores were also calculated.ResultsAmong 188 ICU patients with ABCBSI, 53 were assigned to the non-survival group and 135 to the survival group. The XGBoost model exhibited the greatest area under the receiver operating characteristic curve (AUC), which outperformed other models (logistic regression, AUC = 0.914; support vector machine, AUC = 0.895; random forest, AUC = 0.972; and naive Bayesian, AUC = 0.908) and clinical scores (Acute Physiology and Chronic Health Evaluation II (APACHE II), AUC = 0.855; Sequential Organ Failure Assessment (SOFA), AUC = 0.837). It also had a sensitivity of 0.868, a specificity of 0.970, an accuracy of 0.941, a positive predictive value of 0.920, a negative predictive value of 0.949, and an F1 score of 0.893. As well as identifying the top 12 different important predictors that contribute to early mortality, it also assessed their quantitative contribution and noteworthy thresholds.ConclusionBased on the XGBoost model, early mortality in ABCBSI is estimated to be more reliable than other models and clinical scores. The 12 most important features with corresponding thresholds were identified and more importantly, the SHAP method can be used to interpret this predictive model and support individual patient treatment strategies.

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Preliminary investigation on impact of intergenerational treatment of resveratrol endorses the development of ‘super-pups’

et al. 2023

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The blood urea nitrogen/creatinine (BUN/cre) ratio was U-shaped associated with all-cause mortality in general population

Cited by 12 publications

References 20 publications

Impact of the Controlling Nutritional Status (CONUT) score as a prognostic factor for all-cause mortality in older patients without cancer receiving home medical care: hospital ward-based observational cohort study

Impact of the Controlling Nutritional Status (CONUT) score as a prognostic factor for all-cause mortality in older patients without cancer receiving home medical care: hospital ward-based observational cohort study

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learning

Preliminary investigation on impact of intergenerational treatment of resveratrol endorses the development of ‘super-pups’

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