Faculty Bibliography

BACKGROUND:Recruitment maneuvers are used in patients with ARDS to enhance oxygenation and lung mechanics. Heterogeneous lung and chest-wall mechanics lead to unpredictable transpulmonary pressures and could impact recruitment maneuver success. Tailoring care based on individualized transpulmonary pressure might optimize recruitment, preventing overdistention. This study aimed to identify the optimal transpulmonary pressure for effective recruitment and to explore its association with baseline characteristics. METHODS:We performed post hoc analysis on the Esophageal Pressure Guided Ventilation (EpVent2) trial. We estimated the dose-response relationship between end-recruitment end-inspiratory transpulmonary pressure and the change in lung elastance after a recruitment maneuver by using logistic regression weighted by a generalized propensity score. A positive change in lung elastance was indicative of overdistention. We examined how patient characteristics, disease severity markers, and respiratory parameters predict transpulmonary pressure by using multivariate linear regression models and dominance analyses. RESULTS: CONCLUSIONS:Higher end-recruitment transpulmonary pressure increases the volume of recruitment but raises the risk of overdistention, providing the rationale for transpulmonary pressure to be used as a clinical target. Predictors, for example, body mass index, could guide recruitment maneuver individualization to balance adequate volume gain with overdistention.

BACKGROUND:Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. METHODS:), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. RESULTS:O. CONCLUSIONS:Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

Although only a fraction of CTCF motifs are bound in any cell type, and approximately half of the occupied sites overlap cohesin, the mechanisms underlying cell-type specific attachment and ability to function as a chromatin organizer remain unknown. To investigate the relationship between CTCF and chromatin we applied a combination of imaging, structural and molecular approaches, using a series of brain and cancer associated CTCF mutations that act as CTCF perturbations. We demonstrate that binding and the functional impact of WT and mutant CTCF depend not only on the unique properties of each protein, but also on the genomic context of bound sites. Our studies also highlight the reciprocal relationship between CTCF and chromatin, demonstrating that the unique binding properties of WT and mutant proteins have a distinct impact on accessibility, TF binding, cohesin overlap, chromatin interactivity and gene expression programs, providing insight into their cancer and brain related effects.

BACKGROUND:Ventilation management may differ between COVID-19 ARDS (COVID-ARDS) patients and patients with pre-COVID ARDS (CLASSIC-ARDS); it is uncertain whether associations of ventilation management with outcomes for CLASSIC-ARDS also exist in COVID-ARDS. METHODS:Individual patient data analysis of COVID-ARDS and CLASSIC-ARDS patients in six observational studies of ventilation, four in the COVID-19 pandemic and two pre-pandemic. Descriptive statistics were used to compare epidemiology and ventilation characteristics. The primary endpoint were key ventilation parameters; other outcomes included mortality and ventilator-free days and alive (VFD-60) at day 60. RESULTS:O; p < 0.001). Following multivariable adjustment, higher ΔP had an independent association with higher 60-day mortality and less VFD-60 in both groups. Higher PEEP had an association with less VFD-60, but only in COVID-ARDS patients. CONCLUSIONS:Our findings show important differences in key ventilation parameters and associations thereof with outcomes between COVID-ARDS and CLASSIC-ARDS. TRIAL REGISTRATION/BACKGROUND:Clinicaltrials.gov (identifier NCT05650957), December 14, 2022.

This review explores the complex interactions between sedation and invasive ventilation and examines the potential of volatile anesthetics for lung- and diaphragm-protective sedation. In the early stages of invasive ventilation, many critically ill patients experience insufficient respiratory drive and effort, leading to compromised diaphragm function. Compared with common intravenous agents, inhaled sedation with volatile anesthetics better preserves respiratory drive, potentially helping to maintain diaphragm function during prolonged periods of invasive ventilation. In turn, higher concentrations of volatile anesthetics reduce the size of spontaneously generated tidal volumes, potentially reducing lung stress and strain and with that the risk of self-inflicted lung injury. Taken together, inhaled sedation may allow titration of respiratory drive to maintain inspiratory efforts within lung- and diaphragm-protective ranges. Particularly in patients who are expected to require prolonged invasive ventilation, in whom the restoration of adequate but safe inspiratory effort is crucial for successful weaning, inhaled sedation represents an attractive option for lung- and diaphragm-protective sedation. A technical limitation is ventilatory dead space introduced by volatile anesthetic reflectors, although this impact is minimal and comparable to ventilation with heat and moisture exchangers. Further studies are imperative for a comprehensive understanding of the specific effects of inhaled sedation on respiratory drive and effort and, ultimately, how this translates into patient-centered outcomes in critically ill patients.

IMPORTANCE:The optimal screening frequency and spontaneous breathing trial (SBT) technique to liberate adults from ventilators are unknown. OBJECTIVE:To compare the effects of screening frequency (once-daily screening vs more frequent screening) and SBT technique (pressure-supported SBT with a pressure support level that was >0-≤8 cm H2O and a positive end-expiratory pressure [PEEP] level that was >0-≤5 cm H2O vs T-piece SBT) on the time to successful extubation. DESIGN, SETTING, AND PARTICIPANTS:Randomized clinical trial with a 2 × 2 factorial design including critically ill adults who were receiving invasive mechanical ventilation for at least 24 hours, who were capable of initiating spontaneous breaths or triggering ventilators, and who were receiving a fractional concentration of inspired oxygen that was 70% or less and a PEEP level of 12 cm H2O or less. Recruitment was between January 2018 and February 2022 at 23 intensive care units in North America; last follow-up occurred October 18, 2022. INTERVENTIONS:Participants were enrolled early to enable protocolized screening (more frequent vs once daily) to identify the earliest that patients met criteria to undergo pressure-supported or T-piece SBT lasting 30 to 120 minutes. MAIN OUTCOME AND MEASURES:Time to successful extubation (time when unsupported, spontaneous breathing began and was sustained for ≥48 hours after extubation). RESULTS:Of 797 patients (198 in the once-daily screening and pressure-supported SBT group, 204 in once-daily screening and T-piece SBT, 195 in more frequent screening and pressure-supported SBT, and 200 in more frequent screening and T-piece SBT), the mean age was 62.4 (SD, 18.4) years and 472 (59.2%) were men. There were no statistically significant differences by screening frequency (hazard ratio [HR], 0.88 [95% CI, 0.76-1.03]; P = .12) or by SBT technique (HR, 1.06 [95% CI, 0.91-1.23]; P = .45). The median time to successful extubation was 2.0 days (95% CI, 1.7-2.7) for once-daily screening and pressure-supported SBT, 3.1 days (95% CI, 2.7-4.8) for once-daily screening and T-piece SBT, 3.9 days (95% CI, 2.9-4.7) for more frequent screening and pressure-supported SBT, and 2.9 days (95% CI, 2.0-3.1) for more frequent screening and T-piece SBT. An unexpected interaction between screening frequency and SBT technique required pairwise contrasts that revealed more frequent screening (vs once-daily screening) and pressure-supported SBT increased the time to successful extubation (HR, 0.70 [95% CI, 0.50-0.96]; P = .02). Once-daily screening and pressure-supported SBT (vs T-piece SBT) did not reduce the time to successful extubation (HR, 1.30 [95% CI, 0.98-1.70]; P = .08). CONCLUSIONS AND RELEVANCE:Among critically ill adults who received invasive mechanical ventilation for more than 24 hours, screening frequency (once-daily vs more frequent screening) and SBT technique (pressure-supported vs T-piece SBT) did not change the time to successful extubation. However, an unexpected and statistically significant interaction was identified; protocolized more frequent screening combined with pressure-supported SBTs increased the time to first successful extubation. TRIAL REGISTRATION:ClinicalTrials.gov Identifiers: NCT02399267 and NCT02969226.

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects.