An observational treatment study of metacognition in anxious-depression

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (β=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (β=0.31, SE = 0.08, p<0.001), but not among controls (β=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention

An observational treatment study of metacognition in anxious-depression

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (β=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (β=0.31, SE = 0.08, p<0.001), but not among controls (β=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review.

IMPORTANCE: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. OBJECTIVE: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. EVIDENCE REVIEW: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. FINDINGS: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. CONCLUSIONS AND RELEVANCE: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments

An observational treatment study of metacognition in anxious-depression

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (β=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (β=0.31, SE = 0.08, p<0.001), but not among controls (β=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention.ISSN:2050-084

An observational treatment study of metacognition in anxious-depression

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (β=0.17, SE=0.02, p&lt;0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (β=0.31, SE = 0.08, p&lt;0.001), but not among controls (β=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention

Author Correction: A mechanistic framework for auxin dependent Arabidopsis root hair elongation to low external phosphate

The original version of this Article omitted the following from the Acknowledgements: ‘We also thank DBT-CREST BT/HRD/03/01/2002.’ This has been corrected in both the PDF and HTML versions of the Article

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity

Importance: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence review: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. Conclusions and relevance: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments

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

International audienceTwo 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