Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

BACKGROUND: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. METHODS: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories. RESULTS: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables. CONCLUSIONS: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Rapidly improving ARDS differs clinically and biologically from persistent ARDS.

BACKGROUND: Rapidly improving acute respiratory distress syndrome (RIARDS) is an increasingly appreciated subgroup of ARDS in which hypoxemia improves within 24 h after initiation of mechanical ventilation. Detailed clinical and biological features of RIARDS have not been clearly defined, and it is unknown whether RIARDS is associated with the hypoinflammatory or hyperinflammatory phenotype of ARDS. The purpose of this study was to define the clinical and biological features of RIARDS and its association with inflammatory subphenotypes. METHODS: We analyzed data from 215 patients who met Berlin criteria for ARDS (endotracheally intubated) and were enrolled in a prospective observational cohort conducted at two sites, one tertiary care center and one urban safety net hospital. RIARDS was defined according to previous studies as improvement of hypoxemia defined as (i) PaO2:FiO2 > 300 or (ii) SpO2: FiO2 > 315 on the day following diagnosis of ARDS (day 2) or (iii) unassisted breathing by day 2 and for the next 48 h (defined as absence of endotracheal intubation on day 2 through day 4). Plasma biomarkers were measured on samples collected on the day of study enrollment, and ARDS phenotypes were allocated as previously described. RESULTS: RIARDS accounted for 21% of all ARDS participants. Patients with RIARDS had better clinical outcomes compared to those with persistent ARDS, with lower hospital mortality (13% vs. 57%; p value < 0.001) and more ICU-free days (median 24 vs. 0; p value < 0.001). Plasma levels of interleukin-6, interleukin-8, and plasminogen activator inhibitor-1 were significantly lower among patients with RIARDS. The hypoinflammatory phenotype of ARDS was more common among patients with RIARDS (78% vs. 51% in persistent ARDS; p value = 0.001). CONCLUSIONS: This study identifies a high prevalence of RIARDS in a multicenter observational cohort and confirms the more benign clinical course of these patients. We report the novel finding that RIARDS is characterized by lower concentrations of plasma biomarkers of inflammation compared to persistent ARDS, and that hypoinflammatory ARDS is more prevalent among patients with RIARDS. Identification and exclusion of RIARDS could potentially improve prognostic and predictive enrichment in clinical trials.

Molecular Phenotypes of Acute Respiratory Distress Syndrome in the ROSE Trial Have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally over Time.

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials.

BACKGROUND: In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials. METHODS: We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect. FINDINGS: A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72). INTERPRETATION: Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis. FUNDING: US National Institutes of Health.

Effect of Technology-Enhanced Screening in Addition to Standard Targeted Clinician Education on the Duration of Untreated Psychosis: A Cluster Randomized Clinical Trial.

Importance: Reducing the duration of untreated psychosis (DUP) is essential to improving outcomes for people with first-episode psychosis (FEP). Current US approaches are insufficient to reduce DUP to international standards of less than 90 days. Objective: To determine whether population-based electronic screening in addition to standard targeted clinician education increases early detection of psychosis and decreases DUP, compared with clinician education alone. Design, Setting, and Participants: This cluster randomized clinical trial included individuals aged 12 to 30 years presenting for services between March 2015 and September 2017 at participating sites that included community mental health clinics and school support and special education services. Eligible participants were referred to the Early Diagnosis and Preventative Treatment (EDAPT) Clinic. Data analyses were performed in September and October 2019 for the primary and secondary analyses, with the exploratory subgroup analyses completed in May 2021. Interventions: All sites in both groups received targeted education about early psychosis for health care professionals. In the active screening group, clients also completed the Prodromal Questionnaire-Brief using tablets at intake; referrals were based on those scores and clinical judgment. In the group receiving treatment as usual (TAU), referrals were based on clinical judgment alone. Main Outcomes and Measures: Primary outcomes included DUP, defined as the period from full psychosis onset to the date of the EDAPT diagnostic telephone interview, and the number of individuals identified with FEP or a psychosis spectrum disorder. Exploratory analyses examined differences by site type, completion rates between conditions, and days from service entry to telephone interview. Results: Twenty-four sites agreed to participate, and 12 sites were randomized to either the active screening or TAU group. However, only 10 community clinics and 4 school sites were able to fully implement population screening and were included in the final analysis. The total potentially eligible population size within each study group was similar, with 2432 individuals entering at active screening group sites and 2455 at TAU group sites. A total of 303 diagnostic telephone interviews were completed (178 [58.7%] female individuals; mean [SD] age, 17.09 years [4.57]). Active screening sites reported a significantly higher detection rate of psychosis spectrum disorders (136 cases [5.6%], relative to 65 [2.6%]; P < .001) and referred a higher proportion of individuals with FEP and DUP less than 90 days (13 cases, relative to 4; odds ratio, 0.30; 95% CI, 0.10-0.93; P = .03). There was no difference in mean (SD) DUP between groups (active screening group, 239.0 days [207.4]; TAU group 262.3 days [170.2]). Conclusions and Relevance: In this cluster trial, population-based technology-enhanced screening across community settings detected more than twice as many individuals with psychosis spectrum disorders compared with clinical judgment alone but did not reduce DUP. Screening could identify people undetected in US mental health services. Significant DUP reduction may require interventions to reduce time to the first mental health contact. Trial Registration: ClinicalTrials.gov Identifier: NCT02841956.

The prognostic value of early measures of the ventilatory ratio in the ARDS ROSE trial.

BACKGROUND: The ventilatory ratio (VR, [minute ventilation × PaCO2]/[predicted body weight × 100 × 37.5]) is associated with mortality in ARDS. The aims of this study were to test whether baseline disease severity or neuromuscular blockade (NMB) modified the relationship between VR and mortality. METHODS: This was a post hoc analysis of the PETAL-ROSE trial, which randomized moderate-to-severe ARDS patients to NMB or control. Survival among patients with different VR trajectories or VR cutoff above and below the median was assessed by Kaplan-Meier analysis. The relationships between single-day or 48-h VR trajectories with 28- or 90-day mortality were tested by logistic regression. Randomization allocation to NMB and markers of disease severity were tested as confounders by multivariable regression and interaction term analyses. RESULTS: Patients with worsening VR trajectories had significantly lower survival compared to those with improving VR (n = 602, p < 0.05). Patients with VR > 2 (median) at day 1 had a significantly lower 90-day survival compared to patients with VR ≤ 2 (HR 1.36, 95% CI 1.10-1.69). VR at day 1 was significantly associated with 28-day mortality (OR = 1.40, 95% CI 1.15-1.72). There was no interaction between NMB and VR for 28-day mortality. APACHE-III had a significant interaction with VR at baseline for the outcome of 28-day mortality, such that the relationship between VR and mortality was stronger among patients with lower APACHE-III. There was a significant association between rising VR trajectory and mortality that was independent of NMB, baseline PaO2/FiO2 ratio and generalized markers of disease severity (Adjusted OR 1.81, 95% CI 1.28-2.84 for 28-day and OR 2.07 95% CI 1.41-3.10 for 90-day mortality). APACHE-III and NMB were not effect modifiers in the relationship between VR trajectory and mortality. CONCLUSIONS: Elevated baseline and day 1 VR were associated with higher 28-day mortality. The relationship between baseline VR and mortality was stronger among patients with lower APACHE-III. APACHE-III was not an effect modifier for the relationship between VR trajectory and mortality, so that the VR trajectory may be optimally suited for prognostication and predictive enrichment. VR was not different between patients randomized to NMB or control, indicating that VR can be utilized without correcting for NMB.

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis.

BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. FINDINGS: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). INTERPRETATION: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. FUNDING: US National Institutes of Health and European Society of Intensive Care Medicine.

Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis.

BACKGROUND: Previous latent class analysis of adults with acute respiratory distress syndrome (ARDS) identified two phenotypes, distinguished by the degree of inflammation. We aimed to identify phenotypes in children with ARDS in whom developmental differences might be important, using a latent class analysis approach similar to that used in adults. METHODS: This study was a secondary analysis of data aggregated from the Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) clinical trial and the Genetic Variation and Biomarkers in Children with Acute Lung Injury (BALI) ancillary study. We used latent class analysis, which included demographic, clinical, and plasma biomarker variables, to identify paediatric ARDS (PARDS) phenotypes within a cohort of children included in the RESTORE and BALI studies. The association of phenotypes with clinically relevant outcomes and the performance of paediatric data in adult ARDS classification algorithms were also assessed. FINDINGS: 304 children with PARDS were included in this secondary analysis. Using latent class analysis, a two-class model was a better fit for the cohort than a one-class model (p<0·001). Latent class analysis identified two classes: class 1 (181 [60%] of 304 patients with PARDS) and class 2 (123 [40%] of 304 patients with PARDS), referred to as phenotype 1 and 2 hereafter. Phenotype 2 was characterised by higher concentrations of inflammatory biomarkers, a higher incidence of vasopressor use, and more frequent diagnosis of sepsis, consistent with the adult hyperinflammatory phenotype. All levels of severity of PARDS were observed across both phenotypes. Children with the hyperinflammatory phenotype (phenotype 2) had worse clinical outcomes than those with the hypoinflammatory phenotype (phenotype 1), with a longer duration of mechanical ventilation (median 10·0 days [IQR 6·3-21·0] for phenotype 2 vs 6·6 days [4·1-10·8] for phenotype 1, p<0·0001), and higher incidence of mortality (17 [13·8%] of 123 patients vs four [2·2%] of 181 patients, p=0·0001). When using adult phenotype classification algorithms in children, the soluble tumour necrosis factor receptor-1 (sTNFr1), vasopressor use, and interleukin (IL)-6 variables gave an area under the curve (AUC) of 0·956, and the sTNFr1, vasopressor use, and IL-8 variables gave an AUC of 0·954, compared with the gold standard of latent class analysis. INTERPRETATION: Latent class analysis identified two phenotypes in children with ARDS with characteristics similar to those in adults, including worse outcomes among patients with the hyperinflammatory phenotype. PARDS phenotypes should be considered in design and analysis of future clinical trials in children. FUNDING: US National Institutes of Health.

Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study.

RATIONALE: Using latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA. METHODS: LCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard. RESULTS: A 2-class model best fit the population in VALID (p=0.0010) and in EARLI (p<0.0001). Class 2 comprised 27% and 37% of the populations in VALID and EARLI, respectively. Consistent with the previously described 'hyperinflammatory' subphenotype, Class 2 was characterised by higher proinflammatory biomarkers, acidosis and increased shock and worse clinical outcomes. The similarities between these and prior RCT-derived subphenotypes were further substantiated by the performance of the parsimonious classifier models in both cohorts (area under the curves 0.92-0.94). The hyperinflammatory subphenotype was associated with increased prevalence of chronic liver disease and neutropenia and reduced incidence of chronic obstructive pulmonary disease. Measurement of novel biomarkers showed significantly higher levels of matrix metalloproteinase-8 and markers of endothelial injury in the hyperinflammatory subphenotype, whereas, matrix metalloproteinase-9 was significantly lower. CONCLUSION: Previously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.

Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials.

BACKGROUND: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. METHODS: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. FINDINGS: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. INTERPRETATION: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. FUNDING: NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC).

Measures of both perceived general and specific risks and benefits differentially predict adolescent and young adult tobacco and marijuana use: findings from a Prospective Cohort Study.

Health behavior theorists and prevention researchers use a variety of measures of adolescent and young adult (AYA) risk and benefit perceptions to predict tobacco-use and marijuana-use behaviors. However, studies have not examined whether and how perception measures that ask about likelihood of more general outcomes such as "harm" versus ask about specific risk or benefit outcomes compare or whether they differentially predict AYA willingness to use if one of your best friends were to offer it and intentions to use in the next year; and if these measures have differential ability to predict actual use of tobacco and marijuana. We used data from a prospective cohort of California AYAs to create and test new scales to measure perceptions of specific health and social outcomes related to risks (e.g., smell bad) and benefits (e.g., look cool) related to tobacco and marijuana, and then addressed three questions: (1) Whether and how measures of perceptions of specific social and health risks and benefits (for our purposes "specific measures") and measures of perceived general harm are differentially associated with measures of willingness, social norms, and intentions to use? (2) Are specific versus general measures differentially associated with and predictive of tobacco and cannabis use behavior? (3) Are specific perceptions measures differentially predictive of behavior compared to measures of willingness, social norms, and behavioral intentions? Our results demonstrate that to better predict AYA tobacco and marijuana use, measures that address general outcomes, such as harmfulness, as well as willingness and behavioral intention should be used. We also found that measures of specific perceived risks (short-term, long-term, social) and benefits were unrelated and correlated differently with different products. For example, adolescents perceived both risks and benefits from using products like e-cigarettes, and perceived greater risk from smokeless tobacco compared to combustible cigarettes. These findings indicate that measures of specific perceived social and health outcomes can be useful to discern nuanced differences in motivation for using different substances. Study implications are important for survey dimension-reduction and assessing relationships among perceptions, motivations, and use of tobacco and marijuana products.

The long shadow of childhood trauma for depression in midlife: examining daily psychological stress processes as a persistent risk pathway.

BACKGROUND: Childhood trauma (CT) increases the risk of adult depression. Buffering effects require an understanding of the underlying persistent risk pathways. This study examined whether daily psychological stress processes - how an individual interprets and affectively responds to minor everyday events - mediate the effect of CT on adult depressive symptoms. METHODS: Middle-aged women (N = 183) reported CT at baseline and completed daily diaries of threat appraisals and negative evening affect for 7 days at baseline, 9, and 18 months. Depressive symptoms were measured across the 1.5-year period. Mediation was examined using multilevel structural equation modeling. RESULTS: Reported CT predicted greater depressive symptoms over the 1.5-year time period (estimate = 0.27, s.e. = 0.07, 95% CI 0.15-0.38, p < 0.001). Daily threat appraisals and negative affect mediated the effect of reported CT on depressive symptoms (estimate = 0.34, s.e. = 0.08, 95% CI 0.22-0.46, p < 0.001). Daily threat appraisals explained more than half of this effect (estimate = 0.19, s.e. = 0.07, 95% CI 0.08-0.30, p = 0.004). Post hoc analyses in individuals who reported at least moderate severity of CT showed that lower threat appraisals buffered depressive symptoms. A similar pattern was found in individuals who reported no/low severity of CT. CONCLUSIONS: A reported history of CT acts as a latent vulnerability, exaggerating threat appraisals of everyday events, which trigger greater negative evening affect - processes that have important mental health consequences and may provide malleable intervention targets.

Exploration of Individual and System-Level Well-being Initiatives at an Academic Surgical Residency Program: A Mixed-Methods Study.

Importance: Physician well-being is a critical component of sustainable health care. There are few data on the effects of multilevel well-being programs nor a clear understanding of where and how to target resources. Objective: To inform the design of future well-being interventions by exploring individual and workplace factors associated with surgical trainees' well-being, differences by gender identity, and end-user perceptions of these initiatives. Design, Setting, and Participants: This mixed-methods study among surgical trainees within a single US academic surgical department included a questionnaire in January 2019 (98 participants, including general surgery residents and clinical fellows) and a focus group (9 participants, all clinical residents who recently completed their third postgraduate year [PGY 3]) in July 2019. Participants self-reported gender (man, woman, nonbinary). Exposures: Individual and organizational-level initiatives, including mindfulness-based affective regulation training (via Enhanced Stress Resilience Training), advanced scheduling of time off, wellness half-days, and the creation of a resident-driven well-being committee. Main Outcomes and Measures: Well-being was explored using validated measures of psychosocial risk (emotional exhaustion, depersonalization, perceived stress, depressive symptoms, alcohol use, languishing, anxiety, high psychological demand) and resilience (mindfulness, social support, flourishing) factors. End-user perceptions were assessed through open-ended responses and a formal focus group. Results: Of 98 participants surveyed, 64 responded (response rate, 65%), of whom 35 (55%) were women. Women vs men trainees were significantly more likely to report high depersonalization (odds ratio [OR], 5.50; 95% CI, 1.38-21.85) and less likely to report high mindfulness tendencies (OR, 0.17; 95% CI, 0.05-0.53). Open-ended responses highlighted time and priorities as the greatest barriers to using well-being resources. Focus group findings reflected Job Demand-Resource theory tenets, revealing the value of individual-level interventions to provide coping skills, the benefit of advance scheduling of time off for maintaining personal support resources, the importance of work quality rather than quantity, and the demoralizing effect of inefficient or nonresponsive systems. Conclusions and Relevance: In this study, surgical trainees indicated that multilevel well-being programs would benefit them, but tailoring these initiatives to individual needs and specific workplace elements is critical to maximizing intervention effects.

A Facebook intervention to address cigarette smoking and heavy episodic drinking: A pilot randomized controlled trial.

BACKGROUND: Co-occurrence of tobacco use and heavy episodic drinking (HED; 5+ drinks for men and 4+ drinks for women per occasion) is common among young adults; both warrant attention and intervention. In a two-group randomized pilot trial, we investigated whether a Facebook-based smoking cessation intervention addressing both alcohol and tobacco use would increase smoking abstinence and reduce HED compared to a similar intervention addressing only tobacco. METHODS: Participants were 179 young adults (age 18-25; 49.7% male; 80.4% non-Hispanic white) recruited from Facebook and Instagram who reported smoking 4+ days/week and past-month HED. The Smoking Tobacco and Drinking (STAND) intervention (N = 84) and the Tobacco Status Project (TSP), a tobacco-only intervention (N = 95), both included daily Facebook posts for 90 days and weekly live counseling sessions in private "secret" groups. We verified self-reported 7-day smoking abstinence via remote salivary cotinine tests at 3, 6, and 12 months (with retention at 83%, 66%, and 84%, respectively). Participants self-reported alcohol use. RESULTS: At baseline, the participants averaged 10.4 cigarettes per day (SD = 6.9) and 8.9 HED occasions in the past month (SD = 8.1), with 27.4% in a preparation stage of change for quitting smoking cigarettes. Participants reported significant improvements in cigarette smoking and alcohol use outcomes over time, with no significant differences by condition. At 12 months, intent-to-treat smoking abstinence rates were 3.5% in STAND vs. 0% in TSP (biochemically verified) and 29.4% in STAND vs. 25.5% in TSP (self-reported). Compared to TSP, participants rated the STAND intervention more favorably for supporting health and providing useful information. CONCLUSIONS: Adding an alcohol treatment component to a tobacco cessation social media intervention was acceptable and engaging but did not result in significant differences by treatment condition in smoking or alcohol use outcomes. Participants in both conditions reported smoking and drinking less over time, suggesting covariation in behavioral changes.

Effects of Social Media on Adolescents' Willingness and Intention to Use E-Cigarettes: An Experimental Investigation.

INTRODUCTION: This study examined the effects of experimentally manipulated social media exposure on adolescents' willingness and intention to use e-cigarettes. AIMS AND METHODS: Participants were 135 adolescents of age 13-18 (52.6% female, mean age = 15.3) in California. Participants viewed six social media posts online in a 2 (post source: peer or advertisement) × 2 (e-cigarette content exposure: heavy or light) between-subjects design. Analyses were weighted to population benchmarks. We examined adolescents' beliefs, willingness, and intention to use e-cigarettes in association with social media use intensity in daily life and with experimentally manipulated exposure to social media posts that varied by source (peer or advertisement) and content (e-cigarette heavy or light). RESULTS: Greater social media use in daily life was associated with greater willingness and intention to use e-cigarettes and more positive attitudes, greater perceived norms, and lower perceived danger of e-cigarette use (all p-values <.01). In tests of the experimental exposures, heavy (vs. light) e-cigarette content resulted in greater intention (p = .049) to use e-cigarettes and more positive attitudes (p = .019). Viewing advertisements (vs. peer-generated posts) resulted in greater willingness and intention (p-values <.01) to use e-cigarettes, more positive attitudes (p = .003), and greater norm perceptions (p = .009). The interaction effect of post source by post content was not significant for any of the outcomes (all p-values >.529). CONCLUSIONS: Greater social media use and heavier exposure to advertisements and e-cigarette content in social media posts are associated with a greater risk for e-cigarette use among adolescents. Regulatory action is needed to prohibit sponsored e-cigarette content on social media platforms used by youth. IMPLICATIONS: Adolescents who use social media intensely may be at higher risk for e-cigarette use. Even brief exposure to e-cigarette content on social media was associated with greater intention to use and more positive attitudes toward e-cigarettes. Regulatory action should be taken to prohibit sponsored e-cigarette content on social media used by young people, including posts by influencers who appeal to young people.

Effects of e-cigarette use on cigarette smoking among U.S. youth, 2004-2018.

OBJECTIVE: To determine if the declining trend in U.S. youth cigarette smoking changed after e-cigarettes were introduced, and if youth e-cigarette users would have been likely to smoke cigarettes based on psychosocial and demographic predictors of smoking. METHODS: An interrupted time series analysis was used for cross-sectional data from the 2004 to 2018 National Youth Tobacco Surveys (NYTS) to assess changes in cigarette and e-cigarette use over time. A multivariable logistic regression model used 2004-2009 NYTS data on psychosocial risk factors to predict individual-level cigarette smoking risk from 2011 to 2018. Model-predicted and actual cigarette smoking behavior were compared. RESULTS: The decline in current cigarette smoking slowed in 2014 (-0.75 [95% CI: -0.81, -0.68] to -0.26 [95% CI: -0.40, -0.12] percentage points per year). The decline in ever cigarette smoking accelerated after 2012 (-1.45 [95% CI: -1.59, -1.31] to -1.71 [95% CI: -1.75, -1.66]). Ever and current combined cigarette and/or e-cigarette use declined during 2011-2013 and increased during 2013-2014 with no significant change during 2014-2018 for either variable. The psychosocial model estimated that 69.0% of current cigarette smokers and 9.3% of current e-cigarette users (who did not smoke cigarettes) would smoke cigarettes in 2018. CONCLUSIONS: The introduction of e-cigarettes was followed by a slowing decline in current cigarette smoking, a stall in combined cigarette and e-cigarette use, and an accelerated decline in ever cigarette smoking. Traditional psychosocial risk factors for cigarette smoking suggest that e-cigarette users do not fit the traditional risk profile of cigarette smokers.

Mesenchymal stromal cell extracellular vesicles rescue mitochondrial dysfunction and improve barrier integrity in clinically relevant models of ARDS.

Alveolar epithelial-capillary barrier disruption is a hallmark of acute respiratory distress syndrome (ARDS). Contribution of mitochondrial dysfunction to the compromised alveolar-capillary barrier in ARDS remains unclear. Mesenchymal stromal cells-derived extracellular vesicles (MSC-EVs) are considered as a cell-free therapy for ARDS. Mitochondrial transfer was shown to be important for the therapeutic effects of MSCs and MSC-EVs. Here we investigated the contribution of mitochondrial dysfunction to the injury of alveolar epithelial and endothelial barriers in ARDS and the ability of MSC-EVs to modulate alveolar-capillary barrier integrity through mitochondrial transfer.Primary human small airway epithelial and pulmonary microvascular endothelial cells and human precision cut lung slices (PCLSs) were stimulated with endotoxin or plasma samples from patients with ARDS and treated with MSC-EVs, barrier properties and mitochondrial functions were evaluated. Lipopolysaccharide (LPS)-injured mice were treated with MSC-EVs and degree of lung injury and mitochondrial respiration of the lung tissue were assessed.Inflammatory stimulation resulted in increased permeability coupled with pronounced mitochondrial dysfunction in both types of primary cells and PCLSs. Extracellular vesicles derived from normal MSCs restored barrier integrity and normal levels of oxidative phosphorylation while an extracellular vesicles preparation which did not contain mitochondria was not effective. In vivo, presence of mitochondria was critical for extracellular vesicles ability to reduce lung injury and restore mitochondrial respiration in the lung tissue.In the ARDS environment, MSC-EVs improve alveolar-capillary barrier properties through restoration of mitochondrial functions at least partially via mitochondrial transfer.

Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls.

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.