Preemptive renal replacement therapy in post-cardiotomy cardiogenic shock patients: a new concept?

Quenot, Jean‐Pierre; Jacquier, Marine; Dargent, Auguste; Roudaut, Jean-Baptiste; Andreu, Pascal; Aptel, François; Labruyère, Marie; Barbar, Saber Davide

doi:10.21037/atm.2020.03.100

Ann Transl Med

2020

DOI: 10.21037/atm.2020.03.100

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Preemptive renal replacement therapy in post-cardiotomy cardiogenic shock patients: a new concept?

Jean‐Pierre Quenot

Marine Jacquier

Auguste Dargent

et al.

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Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Bai¹,

Huang²,

Wang³

et al. 2021

Ann Transl Med

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Background: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients. Methods: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.

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Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Bai¹,

Huang²,

Wang³

et al. 2021

Ann Transl Med

View full text Add to dashboard Cite

show abstract

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Preemptive renal replacement therapy in post-cardiotomy cardiogenic shock patients: a new concept?

Cited by 1 publication

References 25 publications

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

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