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Purpose To improve the outcome of the acute respiratory distress syndrome (ARDS), one needs to identify potentially modifiable factors associated with mortality. Methods The large observational study to understand the global impact of severe acute respiratory failure (LUNG SAFE) was an international, multicenter, prospective cohort study of patients with severe respiratory failure, conducted in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across five continents. A pre-specified secondary aim was to examine the factors associated with outcome. Analyses were restricted to patients (93.1 %) fulfilling ARDS criteria on day 1–2 who received invasive mechanical ventilation. Results 2377 patients were included in the analysis. Potentially modifiable factors associated with increased hospital mortality in multivariable analyses include lower PEEP, higher peak inspiratory, plateau, and driving pressures, and increased respiratory rate. The impact of tidal volume on outcome was unclear. Having fewer ICU beds was also associated with higher hospital mortality. Non-modifiable factors associated with worsened outcome from ARDS included older age, active neoplasm, hematologic neoplasm, and chronic liver failure. Severity of illness indices including lower pH, lower PaO 2 /FiO 2 ratio, and higher non-pulmonary SOFA score were associated with poorer outcome. Of the 578 (24.3 %) patients with a limitation of life-sustaining therapies or measures decision, 498 (86.0 %) died in hospital. Factors associated with increased likelihood of limitation of life-sustaining therapies or measures decision included older age, immunosuppression, neoplasia, lower pH and increased non-pulmonary SOFA scores. Conclusions Higher PEEP, lower peak, plateau, and driving pressures, and lower respiratory rate are associated with improved survival from ARDS. Trial Registration: ClinicalTrials.gov NCT02010073.

To determine the impact of specialized treatments, relative to comparator treatments, upon the weight and psychological symptoms of anorexia nervosa (AN) at end-of-treatment (EOT) and follow-up. Randomized controlled trials (RCTs) between January 1980 and December 2017 that reported the effects of at least two treatments on AN were screened. Weight and psychological symptoms were analyzed separately for each study. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed, and studies were assessed using the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) criteria and Cochrane risk of bias tool. We identified 35 eligible RCTs, comprising data from 2524 patients. Meta-analyses revealed a significant treatment effect on weight outcomes at EOT [g = 0.16, 95% CI (0.05-0.28), p = 0.006], but not at follow-up [g = 0.11, 95% CI (-0.04 to 0.27), p = 0.15]. There was no significant treatment effect on psychological outcomes at either EOT [g = -0.03, 95% CI (-0.14 to 0.08), p = 0.63], or follow-up [g = -0.001, 95% CI (-0.11 to 0.11), p = 0.98]. There was no strong evidence of publication bias or significant moderator effects for illness duration, mean age, year of publication, comparator group category, or risk of bias (all p values > 0.05). Current specialized treatments are more adept than comparator interventions at imparting change in weight-based AN symptoms at EOT, but not at follow-up. Specialized treatments confer no advantage over comparator interventions in terms of psychological symptoms. Future precision treatment efforts require a specific focus on the psychological symptoms of AN.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes. Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment. Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy. The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Digital interventions for anxiety disorders are a promising solution to address barriers to evidence-based treatment access. Precise and powerful estimates of digital intervention effectiveness for anxiety disorders are necessary for further adoption in practice. The present systematic review and meta-analysis examined the effectiveness of digital interventions across all anxiety disorders and specific to each disorder wait-list and care-as-usual controls. A systematic search of bibliographic databases identified 15 030 abstracts from inception to 1 January 2020. Forty-seven randomized controlled trials (53 comparisons; 4958 participants) contributed to the meta-analysis. Subgroup analyses were conducted by an anxiety disorder, risk of bias, treatment support, recruitment, location and treatment adherence. A large, pooled effect size of = 0.80 [95% Confidence Interval: 0.68-0.93] was found in favor of digital interventions. Moderate to large pooled effect sizes favoring digital interventions were found for generalized anxiety disorder ( = 0.62), mixed anxiety samples ( = 0.68), panic disorder with or without agoraphobia ( = 1.08) and social anxiety disorder ( = 0.76) subgroups. No subgroups were significantly different or related to the pooled effect size. Notably, the effects of guided interventions ( = 0.84) and unguided interventions ( = 0.64) were not significantly different. Supplemental analysis comparing digital and face-to-face interventions (9 comparisons; 683 participants) found no significant difference in effect [ = 0.14 favoring digital interventions; Confidence Interval: -0.01 to 0.30]. The precise and powerful estimates found further justify the application of digital interventions for anxiety disorders in place of wait-list or usual care.

Art therapy has often been applied in the treatment of traumatized adults, and good results in clinical practice have been reported. However, although art therapy experts underline these benefits, the effectiveness of art therapy in trauma treatment has not been established by systematic review. The aim of this systematic review is to identify and evaluate empirical evidence of the effectiveness of art therapy for trauma treatment. As a result of the systematic review, six controlled, comparative studies on art therapy for trauma in adult patients were found. In half of the included studies, a significant decrease in psychological trauma symptoms was found in the treatment groups, and one study reported a significant decrease in depression. Although there are limitations in the number of included studies, the number of participants, the heterogeneity of included studies, and their methodological quality, the results contribute to insight into the effectiveness of art therapy in trauma treatment and form an evidence base for the urgent need for further research on art therapy and trauma treatment.

Electroconvulsive therapy is the most effective treatment for severe, psychotic or treatment-resistant depression. However, its effectiveness continues to be questioned, both in mainstream media and narratives within the scientific literature. In this analysis, we use an evidence-based approach to demonstrate the efficacy and safety of modern electroconvulsive therapy.

The evaluation of the overlap between the ADOS BOSCC and Standard BOSCC as well as the exploration of child characteristics that may predict change are important steps in consolidating data-driven definitions of “improvement”. Participants were seen between 2 and 5 times with Standard BOSCC and ADOS BOSCC observations over the course of early intervention trials (Grzadzinski et al. in J Autism Dev Disord 46:2464, 2016; Kim et al. in Autism 23:5, 2019). Results showed consistency between the Standard BOSCC and ADOS BOSCC, highlighting the utility of both as metrics of change and treatment outcome across contexts. Baseline characteristics may play a role in the tailoring of early intervention to maximize treatment outcome and may offer guidance when determining which outcome measures to use.

Acceptability is an important factor for predicting intervention use and potential treatment outcomes in psychosocial interventions. Cognitive remediation (CR) improves cognition and functioning in people with a diagnosis of schizophrenia, but its acceptability, and the impact of participants and treatment characteristics, remain to be investigated. Few studies provide a direct measure of acceptability, but treatment drop-out rates are often available and represent a valid surrogate. The systematic search conducted for the most comprehensive CR outcomes database for schizophrenia was updated in December 2020. Eligible studies were randomized clinical trials comparing CR with any other control condition in patients diagnosed with schizophrenia spectrum disorders and that also reported drop-out in treatment and control arms separately. Acceptability was measured as odd-ratios (OR) of drop-out. Of 2119 identified reports, 151 studies, reporting 169 comparisons between CR and control interventions with 10 477 participants were included in the analyses. The overall rate of drop-out was 16.58% for CR programs and 15.21% for control conditions. In the meta-analysis, no difference emerged between CR interventions and controls [OR 1.10, 95% confidence interval (CI) 0.96-1.25, = 0.177]. Factors improving acceptability were: inpatient only recruitment, participants with fewer years of education and lower premorbid IQ, the presence of all CR core elements, and the presence of techniques to transfer cognitive gains into real-world functioning. CR for people diagnosed with schizophrenia is effective and has a good acceptability profile, similar to that of other evidence-based psychosocial interventions.

Depression is considered to have the highest disability burden of all conditions. Although treatment-resistant depression (TRD) is a key contributor to that burden, there is little understanding of the best treatment approaches for it and specifically the effectiveness of available augmentation approaches.AimsWe conducted a systematic review and meta-analysis to search and quantify the evidence of psychological and pharmacological augmentation interventions for TRD. Participants with TRD (defined as insufficient response to at least two antidepressants) were randomised to at least one augmentation treatment in the trial. Pre-post analysis assessed treatment effectiveness, providing an effect size (ES) independent of comparator interventions. Of 28 trials, 3 investigated psychological treatments and 25 examined pharmacological interventions. Pre-post analyses demonstrated N-methyl-d-aspartate-targeting drugs to have the highest ES (ES = 1.48, 95% CI 1.25-1.71). Other than aripiprazole (four studies, ES = 1.33, 95% CI 1.23-1.44) and lithium (three studies, ES = 1.00, 95% CI 0.81-1.20), treatments were each investigated in less than three studies. Overall, pharmacological (ES = 1.19, 95% CI 1.80-1.30) and psychological (ES = 1.43, 95% CI 0.50-2.36) therapies yielded higher ESs than pill placebo (ES = 0.78, 95% CI 0.66-0.91) and psychological control (ES = 0.94, 95% CI 0.36-1.52). Despite being used widely in clinical practice, the evidence for augmentation treatments in TRD is sparse. Although pre-post meta-analyses are limited by the absence of direct comparison, this work finds promising evidence across treatment modalities.Declaration of interestIn the past 3 years, A.H.Y. received honoraria for speaking from AstraZeneca, Lundbeck, Eli Lilly and Sunovion; honoraria for consulting from Allergan, Livanova and Lundbeck, Sunovion and Janssen; and research grant support from Janssen. In the past 3 years, A.J.C. received honoraria for speaking from AstraZeneca and Lundbeck; honoraria for consulting with Allergan, Janssen, Livanova, Lundbeck and Sandoz; support for conference attendance from Janssen; and research grant support from Lundbeck. B.B. has recently been (soon to be) on the speakers/advisory board for Hexal, Lilly, Lundbeck, Mundipharma, Pfizer, and Servier. No other conflicts of interest.

Using language to distance oneself from negative stimuli (e.g., by reducing use of the word “I” and present-tense verbs) is associated with effective emotion regulation. Given that internalizing disorders like anxiety and depression are characterized by maladaptive emotion regulation, stronger linguistic distance may be both a diagnostic marker of lower internalizing symptoms and a prognostic indicator of treatment progress. Here, we tested these hypotheses in a large corpus of naturalistic psychotherapeutic exchanges between clients and their therapists (>1.2 million messages from 6,229 clients). In both exploratory (n = 3,729) and validation (n = 2,500) datasets, we found that clients’ internalizing symptoms decreased over therapy, that client linguistic distance increased over therapy, and that internalizing symptoms tracked fluctuations in linguistic distance both within and between individuals. In other words, clients shifted from discussing themselves and the present moment to discussing other people and time points over treatment, and this psycholinguistic shift was related to symptom reductions. However, effect sizes for linguistic results were small, and we failed to find consistent evidence that linguistic distance statistically mediated changes in symptoms over time. Finally, clustering analyses revealed that data-driven groups of clients defined solely on the basis of their linguistic distance differed in both their symptom severity and treatment outcomes. Together, these findings provide replicable evidence that linguistic distance is a marker of internalizing symptom severity and treatment progress in real-world therapeutic interactions.