Introduction Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches. Methods PRoVENT–COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov , NCT04346342 . Findings Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO 2 /FiO 2 , pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0–15 vs 5, 0–17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day m...

To elucidate the mechanisms underlying the reduced incidence of brain tumors in children with Neurofibromatosis type 1 (NF1) and asthma, we leverage Nf1 optic pathway glioma (Nf1OPG) mice, human and mouse RNAseq data, and two different experimental asthma models. Following ovalbumin or house dust mite asthma induction at 4–6 weeks of age (WOA), Nf1OPG mouse optic nerve volumes and proliferation are decreased at 12 and 24 WOA, indicating no tumor development. This inhibition is accompanied by reduced expression of the microglia-produced optic glioma mitogen, Ccl5. Human and murine T cell transcriptome analyses reveal that inhibition of microglia Ccl5 production results from increased T cell expression of decorin, which blocks Ccl4-mediated microglia Ccl5 expression through reduced microglia NFκB signaling. Decorin or NFκB inhibitor treatment of Nf1OPG mice at 4–6 WOA inhibits tumor formation at 12 WOA, thus establishing a potential mechanistic etiology for the attenuated glioma incidence observed in children with asthma.

Study Objectives The objective of this study was to develop and externally validate a model to predict adjunctive vasopressin response in patients with septic shock being treated with norepinephrine for bedside use in the intensive care unit. Design This was a retrospective analysis of two adult tertiary intensive care unit septic shock populations. Setting Barnes‐Jewish Hospital (BJH) from 2010 to 2017 and Beth Israel Deaconess Medical Center (BIDMC) from 2001 to 2012. Patients Two septic shock populations (548 BJH patients and 464 BIDMC patients) that received vasopressin as second‐line vasopressor. Intervention Patients who were vasopressin responsive were compared with those who were nonresponsive. Vasopressin response was defined as survival with at least a 20% decrease in maximum daily norepinephrine requirements by one calendar day after vasopressin initiation, without a third‐line vasopressor. Measurements Two supervised machine learning models (gradient‐boosting machine [XGBoost] and elastic net penalized logistic regression [EN]) were trained in 1000 bootstrap replications of the BJH data and externally validated in the BIDMC data to predict vasopressin responsiveness. Main Results Vasopressin responsiveness was similar among each cohort (BJH 45% and BIDMC 39%). Mortality was lower for vasopressin responders compared with nonresponders in the BJH (51% vs. 73%) and BIDMC (45% vs. 83%) cohorts, respectively. Both models demonstrated modest discrimination in the training (XGBoost area under receiver operator curve [AUROC] 0.61 [95% confidence interval (CI) 0.61–0.61], EN 0.59 [95% CI 0.58–0.59]) and external validation (XGBoost 0.68 [95% CI 0.63–0.73], EN 0.64 [95% CI 0.59–0.69]) datasets. Conclusion Vasopressin nonresponsiveness is common and associated with increased mortality. The models' modest performances highlight the complexity of septic shock and indicate that more research will be required before clinical decision support tools can aid in anticipating patient‐specific responsiveness to vasopressin.

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