Epidemiological characteristics, practice of ventilation, and clinical outcome in patients at risk of acute respiratory distress syndrome in intensive care units from 16 countries (PRoVENT): an international, multicentre, prospective study

Background Scant information exists about the epidemiological characteristics and outcome of patients in the intensive care unit (ICU) at risk of acute respiratory distress syndrome (ARDS) and how ventilation is managed in these individuals. We aimed to establish the epidemiological characteristics of patients at risk of ARDS, describe ventilation management in this population, and assess outcomes compared with people at no risk of ARDS. Methods PRoVENT (PRactice of VENTilation in critically ill patients without ARDS at onset of ventilation) is an international, multicentre, prospective study undertaken at 119 ICUs in 16 countries worldwide. All patients aged 18 years or older who were receiving mechanical ventilation in participating ICUs during a 1-week period between January, 2014, and January, 2015, were enrolled into the study. The Lung Injury Prediction Score (LIPS) was used to stratify risk of ARDS, with a score of 4 or higher defining those at risk of ARDS. The primary outcome was the proportion of patients at risk of ARDS. Secondary outcomes included ventilatory management (including tidal volume [VT] expressed as mL/kg predicted bodyweight [PBW], and positive end-expiratory pressure [PEEP] expressed as cm H2O), development of pulmonary complications, and clinical outcomes. The PRoVENT study is registered at ClinicalTrials.gov, NCT01868321. The study has been completed. Findings Of 3023 patients screened for the study, 935 individuals fulfilled the inclusion criteria. Of these critically ill patients, 282 were at risk of ARDS (30%, 95% CI 27â33), representing 0·14 cases per ICU bed over a 1-week period. VTwas similar for patients at risk and not at risk of ARDS (median 7·6 mL/kg PBW [IQR 6·7â9·1] vs 7·9 mL/kg PBW [6·8â9·1]; p=0·346). PEEP was higher in patients at risk of ARDS compared with those not at risk (median 6·0 cm H2O [IQR 5·0â8·0] vs 5·0 cm H2O [5·0â7·0]; p<0·0001). The prevalence of ARDS in patients at risk of ARDS was higher than in individuals not at risk of ARDS (19/260 [7%] vs 17/556 [3%]; p=0·004). Compared with individuals not at risk of ARDS, patients at risk of ARDS had higher in-hospital mortality (86/543 [16%] vs 74/232 [32%]; p<0·0001), ICU mortality (62/533 [12%] vs 66/227 [29%]; p<0·0001), and 90-day mortality (109/653 [17%] vs 88/282 [31%]; p<0·0001). VTdid not differ between patients who did and did not develop ARDS (p=0·471 for those at risk of ARDS; p=0·323 for those not at risk). Interpretation Around a third of patients receiving mechanical ventilation in the ICU were at risk of ARDS. Pulmonary complications occur frequently in patients at risk of ARDS and their clinical outcome is worse compared with those not at risk of ARDS. There is potential for improvement in the management of patients without ARDS. Further refinements are needed for prediction of ARDS. Funding None

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset