Purpose Management and outcomes of pregnant women with coronavirus disease 2019 (COVID-19) admitted to intensive care unit (ICU) remain to be investigated. Methods A retrospective multicenter study conducted in 32 ICUs in France, Belgium and Switzerland. Maternal management as well as maternal and neonatal outcomes were reported. Results Among the 187 pregnant women with COVID-19 (33 ± 6 years old and 28 ± 7 weeks’ gestation), 76 (41%) were obese, 12 (6%) had diabetes mellitus and 66 (35%) had pregnancy-related complications. Standard oxygenation, high-flow nasal oxygen therapy (HFNO) and non-invasive ventilation (NIV) were used as the only oxygenation technique in 41 (22%), 55 (29%) and 18 (10%) patients, respectively, and 73 (39%) were intubated. Overall, 72 (39%) patients required several oxygenation techniques and 15 (8%) required venovenous extracorporeal membrane oxygenation. Corticosteroids and tocilizumab were administered in 157 (84%) and 25 (13%) patients, respectively. Awake prone positioning or prone positioning was performed in 49 (26%) patients. In multivariate analysis, risk factors for intubation were obesity (cause-specific hazard ratio (CSH) 2.00, 95% CI (1.05–3.80), p = 0.03), term of pregnancy (CSH 1.07, 95% CI (1.02–1.10), per + 1 week gestation, p = 0.01), extent of computed tomography (CT) scan abnormalities > 50% (CSH 2.69, 95% CI (1.30–5.60), p < 0.01) and NIV use (CSH 2.06, 95% CI (1.09–3.90), p = 0.03). Delivery was required during ICU stay in 70 (37%) patients, mainly due to maternal respiratory worsening, and improved the driving pressure and oxygenation. Maternal and fetal/neonatal mortality rates were 1% and 4%, respectively. The rate of maternal and/or neonatal complications increased with the invasiveness of maternal respiratory support. Conclusion In ICU, corticosteroids, tocilizumab and prone positioning were used in few pregnant women with COVID-19. Over a third of patients were intubated and delivery improved the driving pressure. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-022-06833-8.

Background There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma. Methods This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both “functional data” from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and “structural data” (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants. Results The final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94–0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes. Conclusions A fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data. Clinical Trial Numberhttp://ClinicalTrials.gov (No. NCT03482115).

Accumulating evidence indicates that coronavirus disease 2019 (COVID-19) is a major cause of delirium. Given the global dimension of the current pandemic and the fact that delirium is a strong predictor of cognitive decline for critically ill patients, this raises concerns regarding the neurological cost of COVID-19. Currently, there is a major knowledge gap related to the covert yet potentially incapacitating higher-order cognitive impairment underpinning COVID-19 related delirium. The aim of the current study was to analyze the electrophysiological signatures of language processing in COVID-19 patients with delirium by using a specifically designed multidimensional auditory event-related potential battery to probe hierarchical cognitive processes, including self-processing (P300) and semantic/lexical priming (N400). Clinical variables and electrophysiological data were prospectively collected in controls subjects (n = 14) and in critically ill COVID-19 patients with (n = 19) and without (n = 22) delirium. The time from intensive care unit admission to first clinical sign of delirium was of 8 (3.5 - 20) days and the delirium lasted for 7 (4.5–9.5) days. Overall, we have specifically identified in COVID-19 patients with delirium, both a preservation of low-level central auditory processing (N100, P200) and a coherent ensemble of covert higher-order cognitive dysfunctions encompassing self-related processing (P300) and sematic/lexical language priming (N400) (spatial-temporal clustering, p-cluster ≤ 0.05). We suggest that our results shed new light on the neuropsychological underpinnings of COVID-19 related delirium, and may constitute a valuable method for patient’s bedside diagnosis and monitoring in this clinically challenging setting.