NT-proBNP levels might predict outcomes in severe sepsis, but renal function cannot be ignored

Ye, Jiarong; Liang, Qianrong; Xi, Xiaotu

doi:10.1186/s13054-019-2615-2

Crit Care

2019

DOI: 10.1186/s13054-019-2615-2

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NT-proBNP levels might predict outcomes in severe sepsis, but renal function cannot be ignored

Jiarong Ye

Qianrong Liang

Xiaotu Xi

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Cited by 3 publications

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“…As they have noted, it does not seem persuasive that NT-proBNP could completely predict outcomes without adjusting for the covariate of renal function. They suggest that the relationship of NT-proBNP levels during the acute phase of sepsis and physical function and muscle strength outcomes in sepsis survivors be stratified based on the renal function [2]. In keeping with this, we would like to comment further.…”

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confidence: 78%

N-terminal pro-brain natriuretic peptide levels during the acute phase of sepsis may be a useful indicator of higher risk of long-term impairments: some confounders to consider

Honoré¹,

David²,

Mugisha³

et al. 2020

Crit Care

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mentioning

confidence: 78%

N-terminal pro-brain natriuretic peptide levels during the acute phase of sepsis may be a useful indicator of higher risk of long-term impairments: some confounders to consider

Honoré¹,

David²,

Mugisha³

et al. 2020

Crit Care

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Small samples-oriented intrinsically explainable machine learning using Variational Bayesian Logistic Regression: An intensive care unit readmission prediction case for liver transplantation patients

Liu,

Wu,

Xie

et al. 2024

Expert Systems with Applications

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Predicting Intensive Care Unit Readmission Among Patients After Liver Transplantation Using Machine Learning

Gong

Shouliang

et al. 2023

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Intensive care unit (ICU) readmission of patients following liver transplantation (LT) is associated with poor outcomes. However, its risk factors remain unclarified. Nowadays, machine learning methods are widely used in many aspects of medical health. This study aims to develop a reliable prognostic model for ICU readmission for post-LT patients using machine learning methods. In this paper, a single center cohort ([Formula: see text]) was studied, of which 5.9% ([Formula: see text]) were readmitted to the ICU during hospitalization for LT. A retrospective review of baseline and perioperative factors possibly related to ICU readmission was performed. Three feature selection techniques were used to detect the best features influencing ICU readmission. Moreover, seven machine learning classifiers were proposed and compared to detect the risk of ICU readmission. Alanine transaminase (ALT) at hospital admission, intraoperative fresh frozen plasma (FFP) and red blood cell (RBC) transfusion, and N-Terminal pro-brain natriuretic peptide (NT-proBNP) after LT were found to be essential features for ICU readmission risk prediction. And the stacking model produced the best performance, identifying patients that were readmitted to the ICU after LT at an accuracy of 97.50%, precision of 96.34%, recall of 96.32%, and F1-score of 96.32%. RBC transfusion is the most crucial feature of the stacking classification model, which produced the best performance with overall accuracy, precision, recall, and F1-score of 88.49%, 88.66%, 76.01%, and 81.84%, respectively.

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NT-proBNP levels might predict outcomes in severe sepsis, but renal function cannot be ignored

Cited by 3 publications

References 5 publications

N-terminal pro-brain natriuretic peptide levels during the acute phase of sepsis may be a useful indicator of higher risk of long-term impairments: some confounders to consider

N-terminal pro-brain natriuretic peptide levels during the acute phase of sepsis may be a useful indicator of higher risk of long-term impairments: some confounders to consider

Small samples-oriented intrinsically explainable machine learning using Variational Bayesian Logistic Regression: An intensive care unit readmission prediction case for liver transplantation patients

Predicting Intensive Care Unit Readmission Among Patients After Liver Transplantation Using Machine Learning

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