Radiographic assessment of lung edema (RALE) score is associated with clinical outcomes in patients with refractory cardiogenic shock and refractory cardiac arrest after percutaneous implantation of extracorporeal life support

Voigt, Ingo; Mighali, Marco; Manda, Daniela; Aurich, Phillip; Bruder, Oliver

doi:10.1007/s11739-022-02937-7

Cited by 7 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although several studies have built models to detect pneumonia, few studies have annotated lung images beyond present/absent labels and do not include information regarding degrees of opacity present (Roberts et al, 2021 ). The degree of lung opacities on CXRs have been shown to predict patient outcomes (Au-Yong et al, 2022 ; Balbi et al, 2021 ; Voigt et al, 2022 ) and, therefore, the ability to make this distinction accurately may prove vital in future studies. Pulmonary opacity, which represents air/gas in the alveolar space replaced by a process such as fluids or cells, has been closely related to both pulmonary disease severity and prognosis (Au-Yong et al, 2022 ; Balbi et al, 2021 ; Voigt et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…The degree of lung opacities on CXRs have been shown to predict patient outcomes (Au-Yong et al, 2022 ; Balbi et al, 2021 ; Voigt et al, 2022 ) and, therefore, the ability to make this distinction accurately may prove vital in future studies. Pulmonary opacity, which represents air/gas in the alveolar space replaced by a process such as fluids or cells, has been closely related to both pulmonary disease severity and prognosis (Au-Yong et al, 2022 ; Balbi et al, 2021 ; Voigt et al, 2022 ). Recent studies have developed algorithms to compute opacity-related scores using deep learning frameworks (Cohen et al, 2020 ; Li et al, 2020 ), though they have been limited by a low number of COVID-19 training or test images (up to 300 and 200 images, respectively).…”

Section: Discussionmentioning

confidence: 99%

“…Accurate machine learning characterization of lung opacity on CXR could have numerous potential clinical applications. Lung opacity seen in CXRs has been shown to predict patient outcomes in entities such as COVID-19 and lung edema (Au-Yong et al, 2022 ; Voigt et al, 2022 ). Further work is needed to determine whether our CXR ML algorithm can be used to predict patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Chest radiographs (CXRs) are the most ubiquitous imaging tests for diagnosing respiratory diseases worldwide (Mettler Jr. et al, 2009 ; United Nations Scientific Committee on the Effects of Atomic Radiation, 2008 ). Importantly, the degree of lung opacities on CXRs have been shown to predict patient outcomes (Au-Yong et al, 2022 ; Balbi et al, 2021 ; Voigt et al, 2022 ). Clinically, CXR reports are typically not standardized, and the language used to convey certainty of a diagnosis is highly variable (Makhnevich et al, 2019 ; Makhnevich et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays

et al. 2023

View full text Add to dashboard Cite

Chest radiographs (CXRs) are the most widely available radiographic imaging modality used to detect respiratory diseases that result in lung opacities. CXR reports often use non-standardized language that result in subjective, qualitative, and non-reproducible opacity estimates. Our goal was to develop a robust deep transfer learning framework and adapt it to estimate the degree of lung opacity from CXRs. Following CXR data selection based on exclusion criteria, segmentation schemes were used for ROI (Region Of Interest) extraction, and all combinations of segmentation, data balancing, and classification methods were tested to pick the top performing models. Multifold cross validation was used to determine the best model from the initial selected top models, based on appropriate performance metrics, as well as a novel Macro-Averaged Heatmap Concordance Score (MA HCS). Performance of the best model is compared against that of expert physician annotators, and heatmaps were produced. Finally, model performance sensitivity analysis across patient populations of interest was performed. The proposed framework was adapted to the specific use case of estimation of degree of CXR lung opacity using ordinal multiclass classification. Acquired between March 24, 2020, and May 22, 2020, 38,365 prospectively annotated CXRs from 17,418 patients were used. We tested three neural network architectures (ResNet-50, VGG-16, and ChexNet), three segmentation schemes (no segmentation, lung segmentation, and lateral segmentation based on spine detection), and three data balancing strategies (undersampling, double-stage sampling, and synthetic minority oversampling) using 38,079 CXR images for training, and validation with 286 images as the out-of-the-box dataset that underwent expert radiologist adjudication. Based on the results of these experiments, the ResNet-50 model with undersampling and no ROI segmentation is recommended for lung opacity classification, based on optimal values for the MAE metric and HCS (Heatmap Concordance Score). The degree of agreement between the opacity scores predicted by this model with respect to the two sets of radiologist scores (OR or Original Reader and OOBTR or Out Of Box Reader) in terms of performance metrics is superior to the inter-radiologist opacity score agreement.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays

et al. 2023

View full text Add to dashboard Cite

show abstract

“…IL-6 is one of the main cytokines involved in this pathology [ 21 ]. The RALE assessment has been found to be associated with a worse outcome in different conditions, such as ARDS [ 28 , 31 ], refractory cardiogenic shock and refractory cardiac arrest [ 32 ]. Interestingly, in recent studies the RALE score has been indicated among the variables associated with a higher risk of death in COVID-19 patients [ 29 , 30 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Relationship between hyponatremia at hospital admission and cardiopulmonary profile at follow-up in patients with SARS-CoV-2 (COVID-19) infection

Malandrino

Berni²,

Fibbi³

et al. 2022

J Endocrinol Invest

View full text Add to dashboard Cite

Purpose Hyponatremia occurs in about 30% of patients with pneumonia, including those with SARS-CoV-2 (COVID-19) infection. Hyponatremia predicts a worse outcome in several pathologic conditions and in COVID-19 has been associated with a higher risk of non-invasive ventilation, ICU transfer and death. The main objective of this study was to determine whether early hyponatremia is also a predictor of long-term sequelae at follow-up. Methods In this observational study, we collected 6-month follow-up data from 189 laboratory-confirmed COVID-19 patients previously admitted to a University Hospital. About 25% of the patients (n = 47) had hyponatremia at the time of hospital admission. Results Serum [Na+] was significantly increased in the whole group of 189 patients at 6 months, compared to the value at hospital admission (141.4 ± 2.2 vs 137 ± 3.5 mEq/L, p < 0.001). In addition, IL-6 levels decreased and the PaO2/FiO2 increased. Accordingly, pulmonary involvement, evaluated at the chest X-ray by the RALE score, decreased. However, in patients with hyponatremia at hospital admission, higher levels of LDH, fibrinogen, troponin T and NT-ProBNP were detected at follow-up, compared to patients with normonatremia at admission. In addition, hyponatremia at admission was associated with worse echocardiography parameters related to right ventricular function, together with a higher RALE score. Conclusion These results suggest that early hyponatremia in COVID-19 patients is associated with the presence of laboratory and imaging parameters indicating a greater pulmonary and right-sided heart involvement at follow-up.

show abstract

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning

Liang,

Xue,

Rajaraman

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Radiographic assessment of lung edema (RALE) score is associated with clinical outcomes in patients with refractory cardiogenic shock and refractory cardiac arrest after percutaneous implantation of extracorporeal life support

Cited by 7 publications

References 45 publications

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays

A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays

Relationship between hyponatremia at hospital admission and cardiopulmonary profile at follow-up in patients with SARS-CoV-2 (COVID-19) infection

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning

Contact Info

Product

Resources

About