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Meta-analysis of the variability in the individual response to pharmacological treatments for mania in bipolar disorder
- G. Anmella, M. De Prisco, V. Oliva, M. Sanabra, L. Fortea, M. Ortuño, G. Fico, A. Murru, E. Vieta, D. Hidalgo-Mazzei, A. Solanes, J. Radua
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- Journal:
- European Psychiatry / Volume 66 / Issue S1 / March 2023
- Published online by Cambridge University Press:
- 19 July 2023, p. S84
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Introduction
Many studies have investigated whether there exist predictors of good response to antimanic drugs in bipolar disorder (BD). However, these factors predict response or only indicate benign illness course.
ObjectivesTo shed some light on the topic, we tested whether the response to antimanic drugs showed any variability beyond that expected by the effects of illness course and placebo.
MethodsWe included all double-blind, placebo-controlled RCTs of oral pharmacotherapies targeting adult patients with acute bipolar mania from 1991 to 2020. The primary outcome was the variance of the improvement in manic symptoms in treated individuals compared to placebo. The effect size was the log variability ratio (logVR). We performed a random-effects meta-analysis, including assessments of heterogeneity, sensitivity/cumulative/subgroup analyses, and meta-regression.
Results42 RCTs (46 comparisons) from a total of 8,438 BD patients with acute mania (53.7% male, mean age=39.3; 5,563 treatment/2,875 control groups) were included in the analysis. Individuals in active treatment groups did not show variability in the response beyond that observed in individuals under placebo (VR=1; 95% C.I.=0.97,1.03; p-value=0.97). No heterogeneity was detected between the studies (I2=0%; tau2=0%; Q=29.21; df=45; p-value=0.97). Results were similar in the leave-one-out/cumulative/subgroup analyses. Meta-regression did not show influences by age, sample size, sex, severity of manic symptoms at baseline, or clinical features (rapid cycling, mixed or psychotic features).
ConclusionsThis meta-analysis shows no evidence of differences in the individual response to treatments. These findings suggest that the average treatment effect is a reasonable assumption for the individual BD patient with acute mania. The presented article adds evidence to the equivalent results in schizophrenia-spectrum disorders, clinical high-risk state for psychosis, and major depressive disorder, not supporting classification in responders vs. non-responders. However, these findings should be balanced with results from other fields supporting such classification.
Disclosure of InterestNone Declared
Vickybot, a chatbot for anxiety-depressive symptoms and work-related burnout
- G. Anmella, M. Sanabra, M. Primé-tous, X. Segú, M. Cavero, R. Navinés, A. Mas, V. Olivé, L. Pujol, S. Quesada, C. Pio, M. Villegas, I. Grande, I. Morilla, A. Martínez-Aran, V. Ruiz, E. Vieta, D. Hidalgo-Mazzei
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- Journal:
- European Psychiatry / Volume 66 / Issue S1 / March 2023
- Published online by Cambridge University Press:
- 19 July 2023, pp. S109-S110
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Introduction
A significant proportion of people attending Primary Care (PC) have anxiety-depressive symptoms and work-related burnout and there is a lack of resources to attend them. The COVID-19 pandemic has worsened this problem, particularly affecting healthcare workers, and digital tools have been proposed as a workaround.
ObjectivesWe present the development, feasibility and effectiveness studies of chatbot (Vickybot) aimed at screening, monitoring, and reducing anxiety-depressive symptoms and work-related burnout in PC patients and healthcare workers.
MethodsUser-centered development strategies were adopted. Main functions included self-assessments, psychological modules, and emergency alerts. (1) Simulation: HCs used Vickybot for 2 weeks to simulate different possible clinical situations and evaluated their experience. (3) Feasibility and effectiveness study: People consulting PC or healthcare workers with mental health problems were offered to use Vickybot for one month. Self-assessments for anxiety (GAD-7) and depression (PHQ-9) symptoms, and work-related burnout (based on the Maslach Burnout Inventory) were administered at baseline and every two weeks. Feasibility was determined based on the combination of both subjective and objective user-engagement Indicators (UEIs). Effectiveness was measured using paired t-tests as the change in self-assessment scores.
Results(1) Simulation: 17 HCs (73% female; mean age=36.5±9.7) simulated different clinical situations. 98.8% of the expected modules were recommended according to each simulation. Suicidal alerts were correctly activated and received by the research team. (2) Feasibility and effectiveness study: 34 patients (15 from PC and 19 healthcare workers; 77% female; mean age=35.3±10.1) completed the first self-assessments, with 34 (100%) presenting anxiety symptoms, 32 (94%) depressive symptoms, and 22 (64.7%) work-related burnout. Nine (26.5%) patients completed the second self-assessments after 2-weeks of use. No significant differences were found for anxiety [t(8) = 1.000, p = 0.347] or depressive [t(8) = 0.400, p = 0.700] symptoms, but work-related burnout was significantly reduced [t(8) = 2.874, p = 0.021] between the means of the first and second self-assessments. Vickybot showed high subjective-UEIs, but low objective-UEIs (completion, adherence, compliance, and engagement).
ConclusionsThe chatbot proved to be useful in screening the presence and severity of anxiety and depressive symptoms, in reducing work-related burnout, and in detecting suicidal risk. Subjective perceptions of use contrasted with low objective-use metrics. Our results are promising, but suggest the need to adapt and enhance the smartphone-based solution in order to improve engagement. Consensus on how to report UEIs and validate digital solutions, especially for chatbots, are required.
Disclosure of InterestNone Declared