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With the increasing application of Natural Language Processing (NLP) in Medicine at large, medical educators are urged to gain an understanding and implement NLP techniques within their own education programs to improve the workflow and make significant and rapid improvements in their programs. This paper aims to provide twelve essential tips inclusive of both conceptual and technical factors to facilitate the successful integration of NLP in medical education program evaluation. These twelve tips range from advising on various stages of planning the evaluation process, considerations for data collection, and reflections on preprocessing of data in preparation for analysis and interpretation of results. Using these twelve tips as a framework, medical researchers, educators, and administrators will have an understanding and reference to navigating applications of NLP and be able to unlock its potential for enhancing the evaluation of their own medical education programs.
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Educação Médica , Processamento de Linguagem Natural , Avaliação de Programas e Projetos de Saúde , Humanos , Educação Médica/métodos , Educação Médica/organização & administraçãoRESUMO
BACKGROUND: Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. OBJECTIVE: This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. METHODS: We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. RESULTS: The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. CONCLUSIONS: In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.
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Saúde Mental/normas , Medição de Risco/normas , Mídias Sociais/normas , HumanosRESUMO
BACKGROUND: Antidepressants (ADs) are commonly prescribed medications, but their long-term health effects are debated. ADs disrupt multiple adaptive processes regulated by evolutionarily ancient biochemicals, potentially increasing mortality. However, many ADs also have anticlotting properties that can be efficacious in treating cardiovascular disease. We conducted a meta-analysis assessing the effects of ADs on all-cause mortality and cardiovascular events in general-population and cardiovascular-patient samples. METHODS: Two reviewers independently assessed articles from PubMed, EMBASE, and Google Scholar for AD-related mortality controlling for depression and other comorbidities. From these articles, we extracted information about cardiovascular events, cardiovascular risk status, and AD class. We conducted mixed-effect meta-analyses testing sample type and AD class as moderators of all-cause mortality and new cardiovascular events. RESULTS: Seventeen studies met our search criteria. Sample type consistently moderated health risks. In general-population samples, AD use increased the risks of mortality (HR = 1.33, 95% CI: 1.14-1.55) and new cardiovascular events (HR = 1.14, 95% CI: 1.08-1.21). In cardiovascular patients, AD use did not significantly affect risks. AD class also moderated mortality, but the serotonin reuptake inhibitors were not significantly different from tricyclic ADs (TCAs) (HR = 1.10, 95% CI: 0.93-1.31, p = 0.27). Only "other ADs" were differentiable from TCAs (HR = 1.35, 95% CI: 1.08-1.69). Mortality risk estimates increased when we analyzed the subset of studies controlling for premedication depression, suggesting the absence of confounding by indication. CONCLUSIONS: The results support the hypothesis that ADs are harmful in the general population but less harmful in cardiovascular patients.
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Antidepressivos Tricíclicos/uso terapêutico , Doenças Cardiovasculares , Transtorno Depressivo/tratamento farmacológico , Mortalidade/tendências , Inibidores Seletivos de Recaptação de Serotonina/uso terapêutico , Comorbidade , Humanos , Fatores de RiscoRESUMO
OBJECTIVE: The authors evaluated whether treatment of late-life depression (LLD) with antidepressants leads to changes in cognitive function. METHODS: A systematic review and meta-analysis of prospective studies of antidepressant pharmacotherapy for adults age 50 or older (or mean age of 65 or older) with LLD was conducted. MEDLINE, EMBASE, and PsycInfo were searched through December 31, 2022. The primary outcome was a change on cognitive test scores from baseline to after treatment. Secondary outcomes included the effects of specific medications and the associations between changes in depressive symptoms and cognitive test scores. Participants with bipolar disorder, psychotic depression, dementia, or neurological disease were excluded. Findings from all eligible studies were synthesized at a descriptive level, and a random-effects model was used to pool the results for meta-analysis. RESULTS: Twenty-two studies were included. Thirteen of 19 studies showed an improvement on at least one cognitive test after antidepressant pharmacotherapy, with the most robust evidence for the memory and learning (nine of 16 studies) and processing speed (seven of 10 studies) domains and for sertraline (all five studies). Improvements in depressive symptoms were associated with improvement in cognitive test scores in six of seven relevant studies. The meta-analysis (eight studies; N=493) revealed a statistically significant overall improvement in memory and learning (five studies: effect size=0.254, 95% CI=0.103-0.404, SE=0.077); no statistically significant changes were seen in other cognitive domains. The evaluated risk of publication bias was low. CONCLUSION: Antidepressant pharmacotherapy of LLD appears to improve certain domains of cognitive function, particularly memory and learning. This effect may be mediated by an improvement in depressive symptoms. Studies comparing individuals receiving pharmacotherapy with untreated control participants are needed.
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Antidepressivos , Depressão , Transtorno Depressivo Maior , Idoso , Humanos , Pessoa de Meia-Idade , Antidepressivos/uso terapêutico , Cognição , Depressão/tratamento farmacológico , Estudos ProspectivosRESUMO
Research investigating whether depression is an adaptation or a disorder has been hindered by the lack of an experimental paradigm that can test causal relationships. Moreover, studies attempting to induce the syndrome often fail to capture the suite of feelings, thoughts, and behaviors that characterize depression. An experimental paradigm for triggering depressive symptoms can improve our etiological understanding of the syndrome. The present study attempts to induce core symptoms of depression, particularly those related to rumination, in a healthy, nonclinical sample through a controlled social experiment. These symptoms are sad or depressed mood, anhedonia, feelings of worthlessness or guilt, and difficulty concentrating. One hundred and thirty-four undergraduate students were randomly assigned to either an exclusion (E) or control (C) group. Participants in the exclusion group were exposed to a modified Cyberball paradigm, designed to make them feel socially excluded, followed by a dual-interference task to assess whether their exclusion interfered with their working memory. Excluded participants: (a) self-reported a significant increase in sadness and decrease in happiness, but not anxiety or calmness; (b) scored significantly higher in four of five variables related to depressive rumination; and (c) performed significantly worse on a dual-interference task, suggesting an impaired ability to concentrate. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Depressão , Humanos , Feminino , Masculino , Adulto Jovem , Depressão/fisiopatologia , Adulto , Ruminação Cognitiva/fisiologia , Memória de Curto Prazo/fisiologia , Anedonia/fisiologia , Adolescente , Transtorno Depressivo/fisiopatologia , Isolamento SocialRESUMO
Advancements in artificial intelligence (AI) are enabling the development of clinical support tools (CSTs) in psychiatry to facilitate the review of patient data and inform clinical care. To promote their successful integration and prevent over-reliance, it is important to understand how psychiatrists will respond to information provided by AI-based CSTs, particularly if it is incorrect. We conducted an experiment to examine psychiatrists' perceptions of AI-based CSTs for treating major depressive disorder (MDD) and to determine whether perceptions interacted with the quality of CST information. Eighty-three psychiatrists read clinical notes about a hypothetical patient with MDD and reviewed two CSTs embedded within a single dashboard: the note's summary and a treatment recommendation. Psychiatrists were randomised to believe the source of CSTs was either AI or another psychiatrist, and across four notes, CSTs provided either correct or incorrect information. Psychiatrists rated the CSTs on various attributes. Ratings for note summaries were less favourable when psychiatrists believed the notes were generated with AI as compared to another psychiatrist, regardless of whether the notes provided correct or incorrect information. A smaller preference for psychiatrist-generated information emerged in ratings of attributes that reflected the summary's accuracy or its inclusion of important information from the full clinical note. Ratings for treatment recommendations were also less favourable when their perceived source was AI, but only when recommendations were correct. There was little evidence that clinical expertise or familiarity with AI impacted results. These findings suggest that psychiatrists prefer human-derived CSTs. This preference was less pronounced for ratings that may have prompted a deeper review of CST information (i.e. a comparison with the full clinical note to evaluate the summary's accuracy or completeness, assessing an incorrect treatment recommendation), suggesting a role of heuristics. Future work should explore other contributing factors and downstream implications for integrating AI into psychiatric care.
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Sistemas de Apoio a Decisões Clínicas , Transtorno Depressivo Maior , Psiquiatria , Humanos , Inteligência Artificial , Depressão , Transtorno Depressivo Maior/tratamento farmacológicoRESUMO
PURPOSE: Caregivers of patients with cancer cope with socioemotional challenges, which can adversely affect their well-being. We developed an intervention, expressive writing and reading (EWR), to promote emotional processing and social connectedness among caregivers. In a single-arm pilot study, we assessed its feasibility and perceived usefulness. METHODS: Caregivers participated in weekly 1.5-hour EWR workshops offered over 20 weeks. After 4 sessions, they completed semistructured interviews, which were analyzed using qualitative descriptive analysis. FINDINGS: Of 65 caregivers approached, 25 were eligible, 18 consented, and 9 (50%) caregivers completed at least 4 workshops and the interview. Their responses revealed 3 themes: "inner processing," "interpersonal learning," and "enhanced processing and preparedness." Perceived benefits of EWR included emotional and cognitive processing (individual and collaborative), learning from the emotions and experiences of other caregivers, and preparing for upcoming challenges. CONCLUSIONS: Expressive writing and reading can be a safe and cost-effective supportive intervention for caregivers of patients with cancer.
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Cuidadores , Neoplasias , Humanos , Cuidadores/psicologia , Projetos Piloto , Leitura , Emoções , Neoplasias/terapia , Neoplasias/psicologia , RedaçãoRESUMO
INTRODUCTION: Managing violence or aggression is an ongoing challenge in emergency psychiatry. Many patients identified as being at risk do not go on to become violent or aggressive. Efforts to automate the assessment of risk involve training machine learning (ML) models on data from electronic health records (EHRs) to predict these behaviours. However, no studies to date have examined which patient groups may be over-represented in false positive predictions, despite evidence of social and clinical biases that may lead to higher perceptions of risk in patients defined by intersecting features (eg, race, gender). Because risk assessment can impact psychiatric care (eg, via coercive measures, such as restraints), it is unclear which patients might be underserved or harmed by the application of ML. METHODS AND ANALYSIS: We pilot a computational ethnography to study how the integration of ML into risk assessment might impact acute psychiatric care, with a focus on how EHR data is compiled and used to predict a risk of violence or aggression. Our objectives include: (1) evaluating an ML model trained on psychiatric EHRs to predict violent or aggressive incidents for intersectional bias; and (2) completing participant observation and qualitative interviews in an emergency psychiatric setting to explore how social, clinical and structural biases are encoded in the training data. Our overall aim is to study the impact of ML applications in acute psychiatry on marginalised and underserved patient groups. ETHICS AND DISSEMINATION: The project was approved by the research ethics board at The Centre for Addiction and Mental Health (053/2021). Study findings will be presented in peer-reviewed journals, conferences and shared with service users and providers.
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Pacientes Internados , Psiquiatria , Humanos , Pacientes Internados/psicologia , Violência/prevenção & controle , Violência/psicologia , Agressão/psicologia , Antropologia CulturalRESUMO
BACKGROUND: The motivational interviewing (MI) approach has been shown to help move ambivalent smokers toward the decision to quit smoking. There have been several attempts to broaden access to MI through text-based chatbots. These typically use scripted responses to client statements, but such nonspecific responses have been shown to reduce effectiveness. Recent advances in natural language processing provide a new way to create responses that are specific to a client's statements, using a generative language model. OBJECTIVE: This study aimed to design, evolve, and measure the effectiveness of a chatbot system that can guide ambivalent people who smoke toward the decision to quit smoking with MI-style generative reflections. METHODS: Over time, 4 different MI chatbot versions were evolved, and each version was tested with a separate group of ambivalent smokers. A total of 349 smokers were recruited through a web-based recruitment platform. The first chatbot version only asked questions without reflections on the answers. The second version asked the questions and provided reflections with an initial version of the reflection generator. The third version used an improved reflection generator, and the fourth version added extended interaction on some of the questions. Participants' readiness to quit was measured before the conversation and 1 week later using an 11-point scale that measured 3 attributes related to smoking cessation: readiness, confidence, and importance. The number of quit attempts made in the week before the conversation and the week after was surveyed; in addition, participants rated the perceived empathy of the chatbot. The main body of the conversation consists of 5 scripted questions, responses from participants, and (for 3 of the 4 versions) generated reflections. A pretrained transformer-based neural network was fine-tuned on examples of high-quality reflections to generate MI reflections. RESULTS: The increase in average confidence using the nongenerative version was 1.0 (SD 2.0; P=.001), whereas for the 3 generative versions, the increases ranged from 1.2 to 1.3 (SD 2.0-2.3; P<.001). The extended conversation with improved generative reflections was the only version associated with a significant increase in average importance (0.7, SD 2.0; P<.001) and readiness (0.4, SD 1.7; P=.01). The enhanced reflection and extended conversations exhibited significantly better perceived empathy than the nongenerative conversation (P=.02 and P=.004, respectively). The number of quit attempts did not significantly change between the week before the conversation and the week after across all 4 conversations. CONCLUSIONS: The results suggest that generative reflections increase the impact of a conversation on readiness to quit smoking 1 week later, although a significant portion of the impact seen so far can be achieved by only asking questions without the reflections. These results support further evolution of the chatbot conversation and can serve as a basis for comparison against more advanced versions.
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Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual's functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.
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Esquizofrenia , Humanos , Esquizofrenia/terapia , Conhecimento , Mortalidade Prematura , Neurobiologia , Exame FísicoRESUMO
Machine learning models are often trained on sociodemographic features to predict mental health outcomes. Biases in the collection of race-related data can limit the development of useful and fair models. To assess the current state of this data in mental health research, we conducted a rapid review guided by Critical Race Theory. Findings reveal limitations in the measurement and reporting of race and ethnicity, potentially leading to models that amplify health inequities.
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Etnicidade , Saúde Mental , Viés , Desigualdades de Saúde , Humanos , Aprendizado de MáquinaRESUMO
OBJECTIVES: Fairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature. METHODS: We conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo). RESULTS: Our synthesis identified 'Three Pillars for Fairness': transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare. DISCUSSION: We apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients. CONCLUSION: We conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.
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Equidade em Saúde , Inteligência Artificial , Atenção à Saúde , Humanos , Aprendizado de Máquina , Medição de RiscoRESUMO
It is common experience for practising psychiatrists that individuals with schizophrenia vary markedly in their symptomatic response to antipsychotic medication. What is not clear, however, is whether this variation reflects variability of medication-specific effects (also called "treatment effect heterogeneity"), as opposed to variability of non-specific effects such as natural symptom fluctuation or placebo response. Previous meta-analyses found no evidence of treatment effect heterogeneity, suggesting that a "one size fits all" approach may be appropriate and that efforts at developing personalized treatment strategies for schizophrenia are unlikely to succeed. Recent advances indicate, however, that earlier approaches may have been unable to accurately quantify treatment effect heterogeneity due to their neglect of a key parameter: the correlation between placebo response and medication-specific effects. In the present paper, we address this shortcoming by using individual patient data and study-level data to estimate that correlation and quantitatively characterize antipsychotic treatment effect heterogeneity in schizophrenia. Individual patient data (on 384 individuals who were administered antipsychotic treatment and 88 who received placebo) were obtained from the Yale University Open Data Access (YODA) database. Study-level data were obtained from a meta-analysis of 66 clinical trials including 17,202 patients. Both individual patient and study-level analyses yielded a negative correlation between placebo response and treatment effect for the total score on the Positive and Negative Syndrome Scale (PANSS) (ρ=-0.32, p=0.002 and ρ=-0.39, p<0.001, respectively). Using the most conservative of these estimates, a meta-analysis of treatment effect heterogeneity provided evidence of a marked variability in antipsychotic-specific effects between individuals with schizophrenia, with the top quartile of patients experiencing beneficial treatment effects of 17.7 points or more on the PANSS total score, while the bottom quartile presented a detrimental effect of treatment relative to placebo. This evidence of clinically meaningful treatment effect heterogeneity suggests that efforts to personalize antipsychotic treatment of schizophrenia have potential for success.
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BACKGROUND: Long-term clinical monitoring in bipolar disorder (BD) is an important therapeutic tool. The availability of smartphones and wearables has sparked the development of automated applications to remotely monitor patients. This systematic review focus on the current state of electronic (e-) monitoring for episode prediction in BD. METHODS: We systematically reviewed the literature on e-monitoring for episode prediction in adult BD patients. The systematic review was done according to the guidelines for reporting of systematic reviews and meta-analyses (PRISMA) and was registered in PROSPERO on April 29, 2020 (CRD42020155795). We conducted a search of Web of Science, MEDLINE, EMBASE, and PsycINFO (all 2000-2020) databases. We identified and extracted data from 17 published reports on 15 relevant studies. RESULTS: Studies were heterogeneous and most had substantial methodological and technical limitations. Models varied widely in their performance. Published metrics were too heterogeneous to lend themselves to a meta-analysis. Four studies reported sensitivity (range: 0.21 - 0.95); and two reported specificity for prediction of mood episodes (range: 0.36 - 0.99). Two studies reported accuracy (range: 0.64 - 0.88) and four reported area under the curve (AUC; range: 0.52-0.95). Overall, models were better in predicting manic or hypomanic episodes, but their performance depended on feature type. LIMITATIONS: Our conclusions are tempered by the lack of appropriate information impeding our ability to synthesize the available evidence. CONCLUSIONS: Given the clinical variability in BD, predicting mood episodes remains a challenging task. Emerging e-monitoring technology for episode prediction in BD requires more development before it can be adopted clinically.
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Transtorno Bipolar , Adulto , Afeto , Transtorno Bipolar/diagnóstico , Eletrônica , Humanos , Smartphone , SoftwareRESUMO
According to the analytical rumination hypothesis, depression is an evolved adaptation (like pain or anxiety) that served in our ancestral past to keep people focused on complex interpersonal problems until they could arrive at a resolution (spontaneous remission). If this is true, then those clinical treatments that most facilitate the functions that depression evolved to serve are likely to be more advantageous in the long run than others that simply relieve distress. For example, antidepressant medications may be efficacious in the treatment of depression but only work for so long as they are taken. They may also have an iatrogenic effect that prolongs the duration of the underlying episode. Cognitive and behavioral interventions are as efficacious as medications in terms of reducing acute distress and also appear to have an enduring effect that protects against the return of subsequent symptoms. However, the bulk of the evidence for this effect comes from comparisons to prior medication treatment and it remains unclear whether these psychosocial interventions are truly preventative, or antidepressant medications iatrogenic. A study is described that could resolve this issue and test evolutionary theory with respect to the purported role of rumination in bringing about spontaneous remission.
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Bass , Animais , Antidepressivos/uso terapêutico , Ansiedade , Decapodiformes , Depressão/tratamento farmacológico , HumanosRESUMO
Importance: Antidepressants are commonly used to treat major depressive disorder (MDD). Antidepressant outcomes can vary based on individual differences; however, it is unclear whether specific factors determine this variability or whether it is at random. Objective: To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether variability is associated with MDD severity, antidepressant class, or study publication year. Data Sources: Data used were updated from a network meta-analysis of treatment with licensed antidepressants in adults with MDD. The Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycInfo were searched from inception to March 21, 2019. Additional sources were international trial registries and sponsors, drug companies and regulatory agencies' websites, and reference lists of published articles. Data were analyzed between June 8, 2020, and June 13, 2020. Study Selection: Analysis was restricted to double-blind, randomized placebo-controlled trials with depression scores available at the study's end point. Data Extraction and Synthesis: Baseline means, number of participants, end point means and SDs of total depression scores, antidepressant type, and publication year were extracted. Main Outcomes and Measures: Log SDs (bln σÌ) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in bln σÌ between treatment groups while controlling for end point mean. Secondary models determined whether differences in variability between groups were associated with baseline MDD severity; antidepressant class (selective serotonin reuptake inhibitors and other related drugs; serotonin and norepinephrine reuptake inhibitors; norepinephrine-dopamine reuptake inhibitors; noradrenergic agents; or other antidepressants); and publication year. Results: In the 91 eligible trials (18â¯965 participants), variability in response did not differ significantly between antidepressants and placebo (bln σÌ, 1.02; 95% CI, 0.99-1.05; P = .19). This finding is consistent with a range of treatment effect SDs (up to 16.10), depending on the association between the antidepressant and placebo effects. Variability was not associated with baseline MDD severity or publication year. Responses to noradrenergic agents were 11% more variable than responses to selective serotonin reuptake inhibitors (bln σÌ, 1.11; 95% CI, 1.01-1.21; P = .02). Conclusions and Relevance: Although this study cannot rule out the possibility of treatment effect heterogeneity, it does not provide empirical support for personalizing antidepressant treatment based solely on total depression scores. Future studies should explore whether individual symptom scores or biomarkers are associated with variability in response to antidepressants.
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Antidepressivos/farmacologia , Variação Biológica da População , Transtorno Depressivo Maior/tratamento farmacológico , Individualidade , Metanálise em Rede , Ensaios Clínicos Controlados Aleatórios como Assunto , HumanosRESUMO
Importance: Antidepressants are commonly used worldwide to treat major depressive disorder. Symptomatic response to antidepressants can vary depending on differences between individuals; however, this variability may reflect nonspecific or random factors. Objectives: To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether this variability is associated with severity of major depressive disorder, antidepressant class, or year of study publication. Data Sources: Data used were from a recent network meta-analysis of acute treatment with licensed antidepressants in adults with major depressive disorder. The following databases were searched from inception to January 8, 2016: the Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycINFO. Additional sources were international trial registries, drug approval agency websites, and key scientific journals. Study Selection: Analysis was restricted to double-blind, randomized placebo-controlled trials with available data at the study's end point. Data Extraction and Synthesis: Baseline and end point means, SDs, number of participants in each group, antidepressant class, and publication year were extracted. The data were analyzed between August 14 and November 18, 2019. Main Outcomes and Measures: With the use of validated methods, coefficients of variation were derived for antidepressants and placebo, and their ratios were calculated to compare outcome variability between antidepressant and placebo. Ratios were entered into a random-effects model, with the expectation that response to antidepressants would be more variable than response to placebo. Analysis was repeated after stratifying by baseline severity of depression, antidepressant class (selective serotonin reuptake inhibitors: citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, and vilazodone; serotonin and norepinephrine reuptake inhibitors: desvenlafaxine and venlafaxine; norepinephrine-dopamine reuptake inhibitor: bupropion; noradrenergic agents: amitriptyline and reboxetine; and other antidepressants: agomelatine, mirtazapine, and trazodone), and publication year. Results: In the 87 eligible randomized placebo-controlled trials (17â¯540 unique participants), there was significantly more variability in response to antidepressants than to placebo (coefficients of variation ratio, 1.14; 95% CI, 1.11-1.17; P < .001). Baseline severity of depression did not moderate variability in response to antidepressants. Variability in response to selective serotonin reuptake inhibitors was lower than variability in response to noradrenergic agents (coefficients of variation ratio, 0.88; 95% CI, 0.80-0.97; P = .01), as was the variability in response to other antidepressants compared with noradrenergic agents (coefficients of variation ratio, 0.87; 95% CI, 0.79-0.97; P = .001). Variability also tended to be lower in studies that were published more recently, with coefficients of variation changing by a value of 0.005 (95% CI, 0.002-0.008; P = .003) for every year a study is more recent. Conclusions and Relevance: Individual differences may be systematically associated with responses to antidepressants in major depressive disorder beyond placebo effects or statistical factors. This study provides empirical support for identifying moderators and personalizing antidepressant treatment.
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Most clinicians view depression as a painful disorder in which motivation to pursue adaptive goals is lacking and cognition is impaired. An alternative hypothesis-grounded in a common evolutionary approach-suggests that depression is inherently motivational and evolved to motivate avoidant learning of harmful situations. Testing these hypotheses requires a clear definition of "disorder". Wakefield's harmful dysfunction evolution-based definition proposes that all unambiguous cases of disorder involve a malfunctioning adaptation. These hypotheses-functional adaptation and malfunctioning adaptation-are mutually exclusive and require a common research strategy. One must identify and map out the relevant adaptation-characterized by a high degree of non-random organization and coordination for promoting a function-which will eventually result in a conceptual blueprint of where and how the adaptation can malfunction. Using inescapable shock in rats and physicians' emotional responses to medical errors to provide context, we show how the symptoms of melancholic depression exhibit signs of adaptation for motivating a time-consuming, attentionally-demanding, energetically-expensive avoidant learning style after experiencing a harmful event. We discuss how this adaptationist approach may provide insight into spontaneous remission and the effects of psychotherapies and antidepressant medications.
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Transtorno Depressivo , Médicos , Psicologia Clínica , Animais , Cognição , Depressão , Humanos , RatosRESUMO
Depression is a mental health condition for which individuals commonly seek treatment. However, depressive episodes often resolve on their own, even without treatment. One evolutionary perspective, the analytical rumination hypothesis (ARH), suggests that depression occurs in response to complex problems. According to this perspective, depressive symptoms promote analytical rumination, i.e., distraction-resistant thoughts about the causes of problems [causal analysis (CA)] and how they can be solved [problem-solving analysis (PSA)]. By helping individuals solve complex problems, analytical rumination may contribute to remission from depression. The aim of this study was to investigate (1) whether clinically-depressed individuals have more complex problems and engage in more CA and PSA than non-depressed and (2) the effects of CA and PSA on decreases in problem complexity, depressive symptoms, and remission from the depression. Samples of 85 patients were treated for depression with antidepressants and psychotherapy, and 49 healthy subjects were assessed three times over a 4-month period (at Weeks 1, 5, and 16). At each assessment, they completed measures of depression, analytical rumination, and problem complexity. Depressed individuals reported having more complex problems and engaging in more CA than non-depressed participants. The two groups engaged in a similar degree of PSA. Findings from a multiple regression suggested that more PSA at Week 1 was related to a decrease in depressive symptoms at Week 5, even after controlling for baseline depression, problem number, and complexity. PSA at Week 1 did not predict the remission after hospitalization or at follow-up; however, having less complex problems at the baseline made it more likely that a patient would later remit. Engaging in more CA or PSA at Week 1 did not affect perceived problem complexity at Week 5 or at follow-up. However, these findings were not statistically significant when influential observations (or outliers) were included in the analysis. Our findings suggest that PSA may contribute to a decrease in symptoms of depression over time. However, alleviations in problem complexity and remission might only be achieved if problems are initially less complex. Future directions involve exploring how PSA might contribute to decreases in depressive symptoms and other mechanisms underlying remission from depression.