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Integrated use of bedside lung ultrasound and echocardiography in acute respiratory failure: a prospective observational study in ICU.
Chest. 2014 Aug 21;
Authors: Bataille B, Riu B, Ferre F, Moussot PE, Mari A, Brunel E, Ruiz J, Mora M, Fourcade O, Genestal M, Silva S
Abstract
Abstract: Background.(It has been suggested that the complementary use of echocardiography could improve the diagnostic accuracy of lung ultasonography (LUS) in acute respiratory failure (ARF) patients. Nevertheless, the additional diagnostic value of echocardiographic data when coupled to LUS is still debated in this setting. The aim of the current study was to compare the diagnostic accuracy of LUS and an integrative cardiopulmonary ultrasound approach (TUS) in patients with ARF. Methods.We prospectively recruited patients consecutively admitted for ARF in ICU (University Teaching Hospital) over a 12-month period. Inclusion criteria were age at least 18 years and the presence of criteria of severe ARF justifying ICU admission. We compared both LUS and TUS approaches and the final diagnosis determined by the panel of experts using machine-learning methods to improve the accuracy of the final diagnostic classifiers. Results.One hundred thirty-six patients were included (age 68 ± 15 yr; sex ratio 1). A 3 dimensional PLS-mod (partial least square and multinomial logistic regressions) was developed and subsequently tested using an independent sample of patients. Overall, the diagnostic accuracy of TUS was significantly greater compared to LUS (p < 0.05; learning and test sample). Comparisons between ROC curves shown that TUS significantly improve the diagnosis of cardiogenic edema (p < 0.001; learning and test samples), pneumonia (p < 0.001; learning and test samples) and pulmonary embolism (p <0.001; learning sample). Conclusion.This study demonstrated for the first time a significantly better performance of TUS compared to LUS in the diagnosis of ARF. The value of TUS approach was particularly important to disambiguate cases of hemodynamic pulmonary edema and pneumonia. We suggest that the patient's bedside use of artificial intelligence methods in this setting, could pave the way for the development of new clinically relevant integrative diagnostic models.
Background.: (It has been suggested that the complementary use of echocardiography could improve the diagnostic accuracy of lung ultasonography (LUS) in acute respiratory failure (ARF) patients. Nevertheless, the additional diagnostic value of echocardiographic data when coupled to LUS is still debated in this setting. The aim of the current study was to compare the diagnostic accuracy of LUS and an integrative cardiopulmonary ultrasound approach (TUS) in patients with ARF.
Methods.: We prospectively recruited patients consecutively admitted for ARF in ICU (University Teaching Hospital) over a 12-month period. Inclusion criteria were age at least 18 years and the presence of criteria of severe ARF justifying ICU admission. We compared both LUS and TUS approaches and the final diagnosis determined by the panel of experts using machine-learning methods to improve the accuracy of the final diagnostic classifiers.
Results.: One hundred thirty-six patients were included (age 68 ± 15 yr; sex ratio 1). A 3 dimensional PLS-mod (partial least square and multinomial logistic regressions) was developed and subsequently tested using an independent sample of patients. Overall, the diagnostic accuracy of TUS was significantly greater compared to LUS (p < 0.05; learning and test sample). Comparisons between ROC curves shown that TUS significantly improve the diagnosis of cardiogenic edema (p < 0.001; learning and test samples), pneumonia (p < 0.001; learning and test samples) and pulmonary embolism (p <0.001; learning sample).
Conclusion.: This study demonstrated for the first time a significantly better performance of TUS compared to LUS in the diagnosis of ARF. The value of TUS approach was particularly important to disambiguate cases of hemodynamic pulmonary edema and pneumonia. We suggest that the patient's bedside use of artificial intelligence methods in this setting, could pave the way for the development of new clinically relevant integrative diagnostic models.
PMID: 25144893 [PubMed - as supplied by publisher]