Materiały źródłowe

• #1 Predicting anxiety treatment outcome in community mental health services using linked health administrative data | Scientific Reports

  https://www.nature.com/articles/s41598-024-71557-2

  Anxiety disorders is ranked as the most common class of mental illness disorders globally, affecting hundreds of millions of people and significantly impacting daily life. Developing reliable predictive models for anxiety treatment outcomes holds immense potential to help guide the development of personalised care, optimise resource allocation and improve patient outcomes. […] The aims of this study were to (a) investigate associations between demographic, treatment, and clinical variables and changes in psychological distress while patients were engaged with community mental health services and (b) develop machine learning models to predict reliable change in Kessler (K10) psychological distress scores using a patients pre-treatment (K10) scores within a community mental health setting and their past health service interactions for anxiety disorders.

• #2 Generalized Anxiety Disorder (GAD): Symptoms & Treatment

  https://my.clevelandclinic.org/health/diseases/23940-generalized-anxiety-disorder-gad

  The prognosis (outlook) for generalized anxiety disorder can vary depending on how severe it is. In some cases, GAD is long-term (chronic) and difficult to treat. However, most people experience improvement in their symptoms with medicine and/or talk therapy.

• #3

  https://pmc.ncbi.nlm.nih.gov/articles/PMC4930076/

  Most patients with anxiety disorders receive treatment in primary care settings. Limited moderator data is available to inform clinicians on likely prognostic outcomes for individual patients. We identify baseline characteristics associated with outcome in adults seeking treatment for anxiety disorders. Randomization to ITV over UC was often the strongest predictor of outcome. Several baseline patient characteristics were associated with poor treatment outcome including comorbid depression, increased severity of underlying anxiety disorder(s) (p.001), low socioeconomic status [perceived (p.001) and actual (p.05)] and limited social support (p.001). Patient characteristics associated with particular benefit from ITV were being female (p.05), increased depression(p.01)/GAD severity (p.05), and low socioeconomic status (p.05). ROC analysis demonstrated prognostic subgroups with large differences in response likelihood. Further research should focus on the effectiveness of implementing the ITV intervention of CALM in community treatment centers where patients typically are of low socioeconomic status and may particularly benefit from ITV. Moderator analyses of the CALM trial yielded several types of patient characteristics strongly and consistently associated with treatment outcome. Presence and severity of comorbid depressive symptoms were strongly associated with poorer outcomes to the anxiety disorder treatment. Overall severity of anxiety disorder symptoms was negatively associated with likelihood of both remission and response. Beyond clinical characteristics, several measures indicating poor socioeconomic status were also strongly associated with poor treatment response, including unemployment, personal income, and current receipt of disability payment. Additionally, patients self-ratings of socio-economic variables such as perceived ranking on community and socio-economic ladder, perceived lack of money, and perceived degree of social supports were all strongly negatively associated with likelihood of treatment response. The ITV model in the CALM trial involving pharmacotherapy, computer-assisted CBT, or both was demonstrated to be highly effective compared to UC. Random assignment to ITV was typically the most or one of the most discriminant predictors of treatment outcome in the trial. Low socioeconomic status (perceived or actual) was highly predictive of poor treatment response and remission across the entire sample in both our logical regression and ROC analyses. This secondary analysis of the CALM trial demonstrated: (1) particular characteristics of patients with anxiety disorders associated with poor treatment outcome comorbid depression, increased severity of underlying anxiety disorder(s), low socioeconomic status (perceived and actual) and limited social support; (2) particular patient characteristics associated with particular benefit from the ITV intervention female, increased depression and GAD severity and low socioeconomic status; and (3) prognostic subgroup identifying likelihood of treatment response of individual patients with anxiety disorders.

• #4 Predicting remission following CBT for childhood anxiety disorders: a machine learning approach | Psychological Medicine | Cambridge Core

  https://www.cambridge.org/core/journals/psychological-medicine/article/predicting-remission-following-cbt-for-childhood-anxiety-disorders-a-machine-learning-approach/733FDA7311D52B2AD802300B7119F4FE

  It has become crucial to identify risk factors that may indicate which children are more or less likely to respond to treatment, that is, identify factors that predict outcome regardless of the treatment received. […] Existing evidence suggests that a diagnosis of social anxiety disorder, comorbid depression, and parent psychopathology are the most robust baseline predictors of poorer treatment outcomes. […] A greater number of diagnosed anxiety disorders predicted a greater likelihood that children would not remit all their anxiety disorders following treatment. […] The presence of a mood disorder predicted a lower likelihood of remission in both models. […] The presence of an externalizing disorder indicated a lower likelihood of remission, but only for the primary anxiety model.

• #5 Predicting remission following CBT for childhood anxiety disorders: a machine learning approach | Psychological Medicine | Cambridge Core

  https://www.cambridge.org/core/journals/psychological-medicine/article/predicting-remission-following-cbt-for-childhood-anxiety-disorders-a-machine-learning-approach/733FDA7311D52B2AD802300B7119F4FE

  The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. […] A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (518 years). […] All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. […] Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. […] These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.

• #6 Predicting anxiety treatment outcome in community mental health services using linked health administrative data | Scientific Reports

  https://www.nature.com/articles/s41598-024-71557-2

  The classification model achieved an AUC of 0.76 on the test dataset of 1193 patients and an AUC between 0.75 and 0.90 indicates a moderate score in psychology and human behavioural research. […] Predicting patient outcomes in mental health is a complex and difficult task but is essential for improving the quality of care for people with anxiety disorders. Research on the prediction of patient outcomes is ongoing and the preliminary findings to date are promising. This study developed classification and regression models that showed moderate prediction performance with features that would be relatively easy to collect and implement in health services organisations and clinics on a linked health administrative dataset collected over a 17-year period. Future research using regular patient outcome monitoring, clinical assessment, consumer and administrative data, may yield more accurate and reliable models for predicting patient outcomes.

• #7 Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5613669/

  The ability to predict outcomes early in the course of therapy can be valuable because it can allow therapists to adjust therapy as soon as possible to improve the chances of a speedy recovery. […] The results show that predicting therapeutic outcome based on socio-demographic data performs essentially equally to random guessing. When considering the email behavior however, an AUC of up to 0.78 is obtained when exploiting all data available: the averages and trends of the email attributes combined with the socio-demographic data using the random forest approach. […] The best model performs significantly better than the socio-demographic data, despite the relatively small dataset. […] Although these results are interesting, in a therapeutic setting it is important to know early in treatment whether it is best to continue, or whether it is best to change strategy to improve the chances for recovery.

• #8 Dimensions of anxiety in Major depressive disorder and their use in predicting antidepressant treatment outcome: an iSPOT-D report | Psychological Medicine | Cambridge Core

  https://www.cambridge.org/core/journals/psychological-medicine/article/dimensions-of-anxiety-in-major-depressive-disorder-and-their-use-in-predicting-antidepressant-treatment-outcome-an-ispotd-report/C8325C17AD12A83851CD78DB9E1AD770

  Major depressive disorder (MDD) commonly co-occurs with clinically significant levels of anxiety. […] Anxiety symptoms are varied and have been inconsistently associated with clinical, functional, and antidepressant treatment outcomes. […] We aimed to identify and characterise dimensions of anxiety in people with MDD and their use in predicting antidepressant treatment outcome. […] Furthermore, only higher somatic anxiety was associated with poorer QIDS-SR16 remission, even after adjusting for covariates and multiple comparisons. […] Anxiety symptoms in people with MDD can be separated onto distinct factors that differentially respond to treatment outcome. […] Future research should consider cognitive and somatic symptoms of anxiety separately when assessing anxiety in MDD and their use in predicting treatment outcome.

• #9 Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs | Molecular Psychiatry

  https://www.nature.com/articles/s41380-023-01998-0

  We tested the biomarkers in all subjects in the test cohort, as well as in a more personalized fashion by gender and psychiatric diagnosis, showing increased accuracy with the personalized approach, particularly in women. […] The gene expression data in the test cohorts was normalized (Z-scored) across genders and various psychiatric diagnoses, before those different demographic groups were combined. […] The top blood biomarkers (n=19 probesets, in 18 genes) with the strongest overall CFE for tracking and predicting anxiety disorders, after all four steps were, in order of CFE4 score: GAD1 (Glutamate Decarboxylase 1), NTRK3 (Neurotrophic Receptor Tyrosine Kinase 3), ADRA2A (Adrenoceptor Alpha 2A), FZD10 (Frizzled Class Receptor 10), GRK4 (G Protein-Coupled Receptor Kinase 4), ATP1B2 (ATPase Na+/K+Transporting Subunit Beta 2), CLIC6 (Chloride Intracellular Channel 6), EFNA5 (Ephrin A5), GPX7 (Glutathione Peroxidase 7), again NTRK3 (Neurotrophic Receptor Tyrosine Kinase 3), SLC6A2 (Solute Carrier Family 6 Member 2), SLC6A4 (Solute Carrier Family 6 Member 4), TMEM138 (Transmembrane Protein 138), ANKRD28 (Ankyrin Repeat Domain 28), CCKBR (Cholecystokinin B Receptor), DYNLL2 (Dynein Light Chain LC8-Type 2), Hs.550187, NRG1 (Neuregulin 1), and TFRC (Transferrin Receptor). […] Biomarkers may also be useful for matching patients to medications and measuring response to treatment (pharmacogenomics). […] Overall, this work is a major step forward toward better understanding, diagnosing, and treating anxiety disorders.

• #10 Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs | Molecular Psychiatry

  https://www.nature.com/articles/s41380-023-01998-0

  Anxiety disorders are increasingly prevalent, affect peoples ability to do things, and decrease quality of life. […] We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. […] Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is a urgent need for more precise and personalized approaches like the one we developed. […] Current medication treatments for anxiety (e.g., SSRIs, SNRIs, benzodiazepines, antihistamines, etc.) do not work well in everybody (e.g., low response/remission rates, trial-and-error prescription, problematic side effects, etc.). Matching the right individuals to the right medications using their biomarker profile is a key actionable outcome of our work.

• #11

  https://link.springer.com/article/10.1007/s10802-021-00824-x

  The overall aim of the current study was to investigate the role of parent-reported social skills performance on treatment outcome and the possible interaction with social anxiety. […] We found that parents of children with SoAD reported significantly lower levels of assertive and responsible social behavior prior to treatment in their children compared to parents of children with an anxiety disorder without SoAD. […] The results of the present study showed that higher levels of assertion, self-control and responsibility predicted a higher likelihood of Treatment-Recovery. […] Children with no SoAD who had higher levels of responsibility had a more favorable outcome as measured with the ADIS-TR than children with lower social skills, but this effect did not show for children with SoAD.

• #12

  https://link.springer.com/article/10.1007/s10802-021-00824-x

  In summary, the present study included a sample of children with an anxiety disorder with a relatively high rate of father and mother participation and a substantial proportion of children with SoAD. […] The findings in the present study indicate that higher parent reported social skills in their children predicted higher treatment-recovery.

• #13 Do Patient Characteristics Predict Outcome of Psychodynamic Psychotherapy for Social Anxiety Disorder? | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147165

  Little is known about patient characteristics as predictors for outcome in manualized short term psychodynamic psychotherapy (PDT). No study has addressed which patient variables predict outcome of PDT for social anxiety disorder. […] Pre-treatment SA predicted up to 39 percent of variance of outcome. Only few additional baseline characteristics predicted better treatment outcome (namely, lower comorbidity and interpersonal problems) with a limited proportion of incremental variance (5.5 to 10 percent), while, e.g., shame, self-esteem or harm avoidance did not. […] We argue that the central importance of pre-treatment symptom severity for predicting outcomes should advocate alternative treatment strategies (e.g. longer treatments, combination of psychotherapy and medication) in those who are most disturbed. Given the relatively small amount of variance explained by the other patient characteristics, process variables and patient-therapist interaction should additionally be taken into account in future research.

• #14 Do Patient Characteristics Predict Outcome of Psychodynamic Psychotherapy for Social Anxiety Disorder? | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147165

  The predictive value of other patient characteristics beyond baseline social anxiety remained only small, however comparable to findings on CBT of social anxiety. […] The strong effect of pre-treatment social anxiety on outcome implies that patients starting treatment with higher social anxiety are still those most affected by symptoms after treatment and those who are less likely to remit. These results might suggest that alternative treatment strategies should be preferred for those patients with a higher initial symptom severity.

• #15 Predicting remission following CBT for childhood anxiety disorders: a machine learning approach | Psychological Medicine | Cambridge Core

  https://www.cambridge.org/core/journals/psychological-medicine/article/predicting-remission-following-cbt-for-childhood-anxiety-disorders-a-machine-learning-approach/733FDA7311D52B2AD802300B7119F4FE

  Higher mother self-reported depression, anxiety, and stress scores contributed to model predictions of lower likelihood of remission from all anxiety disorders. […] In both prediction models, treatment modality made the models more likely to predict a lower likelihood of remission when youth received group child and family CBT. […] Low treatment intensity predicted a lower likelihood of remission in both models. […] The findings of these models have implications for clinical practice. […] Understanding the combination of factors in determining idiographic patterns of outcome will be critical to improving the precision of individualized care for children and their families.

• #16

  https://link.springer.com/article/10.1007/s10802-010-9394-1

  The present study investigated the impact of comorbidity over and above the impact of symptom severity on treatment outcome of Cognitive Behavioral Therapy for children with anxiety disorders. […] Severity contributed to the prediction of (no) Recovery and (more) Reliable Change in parent-reported internalizing and externalizing symptoms and self-reported depressive symptoms. […] Total and non-anxiety comorbidity added to the prediction of diagnostic recovery. […] Non-anxiety comorbidity added to the prediction of Reliable Change in parent reported measures by acting as a suppressor variable. […] The results support the need for further research on mechanisms by which treatment gains in children with higher symptom severity and non-anxiety comorbidity can be achieved. […] Predictors of treatment response are of clinical and theoretical value for identifying the mechanisms that facilitate or hinder treatment recovery.

• #17

  https://link.springer.com/article/10.1007/s10802-010-9394-1

  In previous studies symptom severity has been associated with less favorable treatment outcomes in school-refusing youth. […] Together, these results suggest that initial symptom severity is a good predictor of treatment outcome of CBT for CAD. […] One study found an association between comorbid depression and poor treatment response. […] While studies suggest that CBT is equally effective for anxiety disordered children with or without comorbid disorders, it is premature to assume that a standard brief CBT program used in research trials will work as well for complex cases. […] In addition, comorbidity has repeatedly been suggested to negatively impact treatment processes. […] The purpose of the present study was to investigate the unique impact of pretreatment comorbidity on treatment outcome for childhood anxiety disorders above and beyond the impact of severity.

• #18

  https://link.springer.com/article/10.1007/s10802-010-9394-1

  The study focuses on the impact of total and non-anxious comorbidity and overall severity on outcome. […] Children with a higher level of pretreatment severity need greater decreases in symptoms to reach a subthreshold level of symptoms. […] Severity and comorbidity did not contribute to the prediction of Reliable Change when based on self-reported anxiety symptoms. […] Conversely, severity predicted greater Reliable Change in parent reported internalizing and externalizing symptoms, and child reported depressive symptoms. […] The presence of a non-anxiety comorbid disorder at pretreatment added significantly to predictions of treatment recovery based on a clinical interview and child-reported anxiety symptoms and for Reliable Change on depressive, internalizing and externalizing symptoms. […] Future studies but also clinicians should take into account the potential impact of symptom severity on outcome.

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