Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2024
Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Aghezzaf Samy, Coisne Augustin, Hamzi Kenza, Toupin Solenn, Bouleti Claire, Fauvel Charles, Brette Jean-Baptiste, Montaigne David, Vasram Reza Rossanaly, Trimaille Antonin, Lemesle Gilles, Schurtz Guillaume, Gerbaud Edouard, Delmas Clément, Bedossa Marc, Dib Jean-Claude, Roule Vincent, Puymirat Etienne, Gilard Martine, Boukhris Marouane, Mansencal Nicolas, Bouali Nabil, Andrieu Stephane, Gonçalves Trecy, Dillinger Jean-Guillaume, Henry Patrick, Pezel Theo
Аннотация
INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.
OBJECTIVES: We aimed to investigate the accuracy of a machine learning (ML)-model based on initial transthoracic echocardiography (TTE) to predict in-hospital major adverse events (MAEs) in a broad spectrum of patients admitted to ICCU.
METHODS: All consecutive patients hospitalized in ICCUs with a complete TTE performed within the first 24 hours of admission were included in this prospective multicenter study (39 centers). Sixteen TTE parameters were evaluated. The ML model involved automated feature selection by random survival forest and model building with an extreme gradient boosting (XGBoost) algorithm. The primary outcome was in-hospital MAEs defined as all-cause death, resuscitated cardiac arrest, or cardiogenic shock.
RESULTS: Of 1,499 consecutive patients (63 ± 15 years, 70% male), MAEs occurred in 67 patients (4.5%). The 5 TTE parameters selected in the model were left ventricular outflow tract velocity-time integral, E/e' ratio, systolic pulmonary artery pressure, tricuspid annular plane systolic excursion, and left ventricular ejection fraction. Using the XGBoost, the ML model exhibited a higher area under the receiver operating curve compared with any existing scores (ML model, 0.83 vs logistic regression, 0.76, ACUTE-HF score:,0.66; thrombolysis in myocardial infarction score, 0.60; Global Registry of Acute Coronary Events score, 0.58, all P < .001). The ML model had an incremental prognostic value for predicting MAE over a traditional model including clinical and biological data (C index 0.80 vs 0.73, P = .012; chi-square 59.7 vs 32.4; P < .001).
CONCLUSION: The ML model based on initial TTE exhibited a higher prognostic value to predict in-hospital MAEs compared with existing scores or clinical and biological data in the ICCU.
OBJECTIVES: We aimed to investigate the accuracy of a machine learning (ML)-model based on initial transthoracic echocardiography (TTE) to predict in-hospital major adverse events (MAEs) in a broad spectrum of patients admitted to ICCU.
METHODS: All consecutive patients hospitalized in ICCUs with a complete TTE performed within the first 24 hours of admission were included in this prospective multicenter study (39 centers). Sixteen TTE parameters were evaluated. The ML model involved automated feature selection by random survival forest and model building with an extreme gradient boosting (XGBoost) algorithm. The primary outcome was in-hospital MAEs defined as all-cause death, resuscitated cardiac arrest, or cardiogenic shock.
RESULTS: Of 1,499 consecutive patients (63 ± 15 years, 70% male), MAEs occurred in 67 patients (4.5%). The 5 TTE parameters selected in the model were left ventricular outflow tract velocity-time integral, E/e' ratio, systolic pulmonary artery pressure, tricuspid annular plane systolic excursion, and left ventricular ejection fraction. Using the XGBoost, the ML model exhibited a higher area under the receiver operating curve compared with any existing scores (ML model, 0.83 vs logistic regression, 0.76, ACUTE-HF score:,0.66; thrombolysis in myocardial infarction score, 0.60; Global Registry of Acute Coronary Events score, 0.58, all P < .001). The ML model had an incremental prognostic value for predicting MAE over a traditional model including clinical and biological data (C index 0.80 vs 0.73, P = .012; chi-square 59.7 vs 32.4; P < .001).
CONCLUSION: The ML model based on initial TTE exhibited a higher prognostic value to predict in-hospital MAEs compared with existing scores or clinical and biological data in the ICCU.