Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.

BACKGROUND The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative. METHODS To illustrate the development of a meta-learner, we used a dataset of 187,757 people with depression. Using 31 variables, we aimed to predict two outcomes measured 60 days after initiation of antidepressant treatment: severity of depressive symptoms (continuous) and all-cause dropouts (binary). We fitted a ridge regression and a multi-layer perceptron (MLP) deep neural network as two separate prediction models ("base-learners"). We then developed two "meta-learners", combining predictions from the two base-learners. To compare the performance across the different methods, we calculated mean absolute error (MAE, for continuous outcome) and the area under the receiver operating characteristic curve (AUC, for binary outcome) using bootstrapping. RESULTS Compared to the best performing base-learner (MLP base-learner, MAE at 4.63, AUC at 0.59), the best performing meta-learner showed a 2.49% decrease in MAE at 4.52 for the continuous outcome and a 6.47% increase in AUC at 0.60 for the binary outcome. CONCLUSIONS A meta-learner approach may effectively combine multiple prediction models. Choosing between statistical and machine learning models may not be necessary in practice

Real-world effect of antidepressants for depressive disorder in primary care: protocol of a population-based cohort study

Introduction: Clinical guidelines recommend antidepressants as the first line of treatment for adults with moderate-to-severe depression. Randomised trials provide the best evidence on the comparative effectiveness of antidepressants for depression, but are limited by a short follow-up and a highly selected population. We aim to conduct a cohort study on a large database to assess acceptability, efficacy, safety and tolerability of antidepressant monotherapy in people with depressive disorder in primary care.Methods and analysis: This is a protocol for a cohort study using data from the QResearch primary care research database, which is the largest general practice research database in the UK. We will include patients registered for at least 1 year from 1 January 1998, diagnosed with a new episode of depression and on antidepressant and a comparison group not on antidepressant. The exposure of interest will be treatment with antidepressant medications. Our outcomes will be acceptability (treatment discontinuation due to any cause), efficacy (clinical response and remission); safety (adverse events (AEs) and all-cause mortality); and tolerability (dropouts due to any AE) measured at 2 months, 6 months and 1 year. For each outcome, we will estimate the absolute risks for all antidepressants, and relative effects between antidepressants using Cox’s proportion hazards models. We will calculate HRs and 99.9% CIs for each outcome of interest.Discussion: The main limitation is the observational nature of our study, while the major strengths include the large representative population contained in QResearch and the possibly high generalisability

An online evidence-based dictionary of common adverse events of antidepressants: a new tool to empower patients and clinicians in their shared decision-making process

Background: Adverse events (AEs) are commonly reported in clinical studies using the Medical Dictionary for Regulatory Activities (MedDRA), an international standard for drug safety monitoring. However, the technical language of MedDRA makes it challenging for patients and clinicians to share understanding and therefore to make shared decisions about medical interventions. In this project, people with lived experience of depression and antidepressant treatment worked with clinicians and researchers to co-design an online dictionary of AEs associated with antidepressants, taking into account its ease of use and applicability to real-world settings. Methods: Through a pre-defined literature search, we identified MedDRA-coded AEs from randomised controlled trials of antidepressants used in the treatment of depression. In collaboration with the McPin Foundation, four co-design workshops with a lived experience advisory panel (LEAP) and one independent focus group (FG) were conducted to produce user-friendly translations of AE terms. Guiding principles for translation were co-designed with McPin/LEAP members and defined before the finalisation of Clinical Codes (CCs, or non-technical terms to represent specific AE concepts). FG results were thematically analysed using the Framework Method. Results: Starting from 522 trials identified by the search, 736 MedDRA-coded AE terms were translated into 187 CCs, which balanced key factors identified as important to the LEAP and FG (namely, breadth, specificity, generalisability, patient-understandability and acceptability). Work with the LEAP showed that a user-friendly language of AEs should aim to mitigate stigma, acknowledge the multiple levels of comprehension in ‘lay’ language and balance the need for semantic accuracy with user-friendliness. Guided by these principles, an online dictionary of AEs was co-designed and made freely available (https://thesymptomglossary.com). The digital tool was perceived by the LEAP and FG as a resource which could feasibly improve antidepressant treatment by facilitating the accurate, meaningful expression of preferences about potential harms through a shared decision-making process. Conclusions: This dictionary was developed in English around AEs from antidepressants in depression but it can be adapted to different languages and cultural contexts, and can also become a model for other interventions and disorders (i.e., antipsychotics in schizophrenia). Co-designed digital resources may improve the patient experience by helping to deliver personalised information on potential benefits and harms in an evidence-based, preference-sensitive way

Comparative effects of pharmacological interventions for the acute and long-term management of insomnia disorder in adults: a systematic review and network meta-analysis.

BACKGROUND Behavioural, cognitive, and pharmacological interventions can all be effective for insomnia. However, because of inadequate resources, medications are more frequently used worldwide. We aimed to estimate the comparative effectiveness of pharmacological treatments for the acute and long-term treatment of adults with insomnia disorder. METHODS In this systematic review and network meta-analysis, we searched the Cochrane Central Register of Controlled Trials, MEDLINE, PubMed, Embase, PsycINFO, WHO International Clinical Trials Registry Platform, ClinicalTrials.gov, and websites of regulatory agencies from database inception to Nov 25, 2021, to identify published and unpublished randomised controlled trials. We included studies comparing pharmacological treatments or placebo as monotherapy for the treatment of adults (≥18 year) with insomnia disorder. We assessed the certainty of evidence using the confidence in network meta-analysis (CINeMA) framework. Primary outcomes were efficacy (ie, quality of sleep measured by any self-rated scale), treatment discontinuation for any reason and due to side-effects specifically, and safety (ie, number of patients with at least one adverse event) both for acute and long-term treatment. We estimated summary standardised mean differences (SMDs) and odds ratios (ORs) using pairwise and network meta-analysis with random effects. This study is registered with Open Science Framework, https://doi.org/10.17605/OSF.IO/PU4QJ. FINDINGS We included 170 trials (36 interventions and 47 950 participants) in the systematic review and 154 double-blind, randomised controlled trials (30 interventions and 44 089 participants) were eligible for the network meta-analysis. In terms of acute treatment, benzodiazepines, doxylamine, eszopiclone, lemborexant, seltorexant, zolpidem, and zopiclone were more efficacious than placebo (SMD range: 0·36-0·83 [CINeMA estimates of certainty: high to moderate]). Benzodiazepines, eszopiclone, zolpidem, and zopiclone were more efficacious than melatonin, ramelteon, and zaleplon (SMD 0·27-0·71 [moderate to very low]). Intermediate-acting benzodiazepines, long-acting benzodiazepines, and eszopiclone had fewer discontinuations due to any cause than ramelteon (OR 0·72 [95% CI 0·52-0·99; moderate], 0·70 [0·51-0·95; moderate] and 0·71 [0·52-0·98; moderate], respectively). Zopiclone and zolpidem caused more dropouts due to adverse events than did placebo (zopiclone: OR 2·00 [95% CI 1·28-3·13; very low]; zolpidem: 1·79 [1·25-2·50; moderate]); and zopiclone caused more dropouts than did eszopiclone (OR 1·82 [95% CI 1·01-3·33; low]), daridorexant (3·45 [1·41-8·33; low), and suvorexant (3·13 [1·47-6·67; low]). For the number of individuals with side-effects at study endpoint, benzodiazepines, eszopiclone, zolpidem, and zopiclone were worse than placebo, doxepin, seltorexant, and zaleplon (OR range 1·27-2·78 [high to very low]). For long-term treatment, eszopiclone and lemborexant were more effective than placebo (eszopiclone: SMD 0·63 [95% CI 0·36-0·90; very low]; lemborexant: 0·41 [0·04-0·78; very low]) and eszopiclone was more effective than ramelteon (0.63 [0·16-1·10; very low]) and zolpidem (0·60 [0·00-1·20; very low]). Compared with ramelteon, eszopiclone and zolpidem had a lower rate of all-cause discontinuations (eszopiclone: OR 0·43 [95% CI 0·20-0·93; very low]; zolpidem: 0·43 [0·19-0·95; very low]); however, zolpidem was associated with a higher number of dropouts due to side-effects than placebo (OR 2·00 [95% CI 1·11-3·70; very low]). INTERPRETATION Overall, eszopiclone and lemborexant had a favorable profile, but eszopiclone might cause substantial adverse events and safety data on lemborexant were inconclusive. Doxepin, seltorexant, and zaleplon were well tolerated, but data on efficacy and other important outcomes were scarce and do not allow firm conclusions. Many licensed drugs (including benzodiazepines, daridorexant, suvorexant, and trazodone) can be effective in the acute treatment of insomnia but are associated with poor tolerability, or information about long-term effects is not available. Melatonin, ramelteon, and non-licensed drugs did not show overall material benefits. These results should serve evidence-based clinical practice. FUNDING UK National Institute for Health Research Oxford Health Biomedical Research Centre

Impact of COVID-19 on telepsychiatry at the service and individual patient level across two UK NHS mental health trusts

Background. The effects of COVID-19 on the shift to remote consultations remain to be properly investigated. Objective. To quantify the extent, nature and clinical impact of the use of telepsychiatry during the COVID-19 pandemic and compare it with the data in the same period of the 2 years before the outbreak. Methods. We used deidentified electronic health records routinely collected from two UK mental health Foundation Trusts (Oxford Health (OHFT) and Southern Health (SHFT)) between January and September in 2018, 2019 and 2020. We considered three outcomes: (1) service activity, (2) in-person versus remote modalities of consultation and (3) clinical outcomes using Health of the Nation Outcome Scales (HoNOS) data. HoNOS data were collected from two cohorts of patients (cohort 1: patients with ≥1 HoNOS assessment each year in 2018, 2019 and 2020; cohort 2: patients with ≥1 HoNOS assessment each year in 2019 and 2020), and analysed in clusters using superclasses (namely, psychotic, non-psychotic and organic), which are used to assess overall healthcare complexity in the National Health Service. All statistical analyses were done in Python. Findings. Mental health service activity in 2020 increased in all scheduled community appointments (by 15.4% and 5.6% in OHFT and SHFT, respectively). Remote consultations registered a 3.5-fold to 6-fold increase from February to June 2020 (from 4685 to a peak of 26 245 appointments in OHFT and from 7117 to 24 987 appointments in SHFT), with post-lockdown monthly averages of 23 030 and 22 977 remote appointments/month in OHFT and SHFT, respectively. Video consultations comprised up to one-third of total telepsychiatric services per month from April to September 2020. For patients with dementia, non-attendance rates at in-person appointments were higher than remote appointments (17.2% vs 3.9%). The overall HoNOS cluster value increased only in the organic superclass (clusters 18–21, n=174; p<0.001) from 2019 to 2020, suggesting a specific impact of the COVID-19 pandemic on this population of patients. Conclusions and clinical implications. The rapid shift to remote service delivery has not reached some groups of patients who may require more tailored management with telepsychiatry

Predicting treatment effects in unipolar depression: A meta-review.

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables

Meta-review: network meta-analyses in child and adolescent psychiatry

ObjectiveNetwork meta-analyses (NMAs) are gaining traction as the preferred method for evidence synthesis of intervention studies. This review aimed to summarize the basics of NMAs and conduct a meta-review of available NMAs on the treatment of child and adolescent psychiatric disorders by appraising their quality.MethodPubMed (Medline), PsycInfo, Embase, Ovid Medline, and Web of Knowledge were systematically searched (last update January 9, 2018). The quality of each included NMA was appraised using the AMSTAR-2 tool and the PRISMA-NMA checklist, which includes specific items for NMAs.ResultsEighteen NMAs (6 on attention-deficit/hyperactivity disorder; 4 on psychotic disorders; 2 on depression; 2 on anxiety disorders; 1 on obsessive-compulsive disorder; 1 on disruptive behavior disorder, 1 on bipolar disorder, and 1 on antipsychotics across disorders) were retrieved. Results from the AMSTAR-2 assessment showed that only 27% of appraised NMAs were rated as moderate quality; most were rated as low (33%) or critically low (40%) quality. Only 3 of the appraised NMAs reported on all PRISMA-NMA items specific for NMAs; the network structure was graphically presented in most NMAs (80%), and inconsistency was described in only 47%.ConclusionGiven the paucity of head-to-head trials in child and adolescent psychiatry, NMAs have the potential to contribute to the field, because they provide evidence-based hierarchies for treatment decision making, even in the absence of trials directly comparing at least 2 treatments. However, because of important limitations in the included NMAs, additional methodologically sound NMAs are needed to inform future guidelines and clinical practice in child and adolescent psychiatry

Antidepressants in children and adolescents: meta-review of efficacy, tolerability and suicidality in acute treatment

Antidepressants are prescribed for the treatment of a number of psychiatric disorders in children and adolescents, however there is still controversy about whether they should be used in this population. This meta-review aimed to assess the effects of antidepressants for the acute treatment of attention-deficit/hyperactivity disorder (ADHD), anxiety disorders (ADs), autistic spectrum disorder (ASD), enuresis, major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) in children and adolescents. Efficacy was measured as response to treatment (either as mean overall change in symptoms or as a dichotomous outcome) and tolerability was measured as the proportion of patients discontinuing treatment due to adverse events. Suicidality was measured as suicidal ideation, behavior (including suicide attempts) and completed suicide. PubMed, EMBASE, and Web of Science were systematically searched (until 31 October 2019) for existing systematic reviews and/or meta-analyses of double-blind randomized controlled trials. The quality of the included reviews was appraised using AMSTAR-2. Our meta-review included nine systematic reviews/meta-analyses (2 on ADHD; 1 on AD; 2 on ASD; 1 on enuresis; 1 on MDD, 1 on OCD and 1 on PTSD). In terms of efficacy this review found that, compared to placebo: fluoxetine was more efficacious in the treatment of MDD, fluvoxamine and paroxetine were better in the treatment of AD; fluoxetine and sertraline were more efficacious in the treatment of OCD; bupropion and desipramine improved clinician and teacher-rated ADHD symptoms; clomipramine and tianeptine were superior on some of the core symptoms of ASD; and no antidepressant was more efficacious for PTSD and enuresis. With regard to tolerability: imipramine, venlafaxine, and duloxetine were less well tolerated in MDD; no differences were found for any of the antidepressants in the treatment of anxiety disorders (ADs), ADHD, and PTSD; tianeptine and citalopram, but not clomipramine, were less well tolerated in children and adolescents with ASD. For suicidal behavior/ideation, venlafaxine (in MDD) and paroxetine (in AD) were associated with a significantly increased risk; by contrast, sertraline (in AD) was associated with a reduced risk. The majority of included systematic reviews/meta-analyses were rated as being of high or moderate in quality by the AMSTAR-2 critical appraisal tool (one and five, respectively). One included study was of low quality and two were of critically low quality. Compared to placebo, selected antidepressants can be efficacious in the acute treatment of some common psychiatric disorders, although statistically significant differences do not always translate into clinically significant results. Little information was available about tolerability of antidepressants in RCTs of OCD and in the treatment of ADHD, ASD, MDD, and PTSD. There is a paucity of data on suicidal ideation/behavior, but paroxetine may increase the risk of suicidality in the treatment of AD and venlafaxine for MDD. Findings from this review must be considered in light of potential limitations, such as the lack of comparative information about many antidepressants, the short-term outcomes and the quality of the available evidenc

The Kilim plot: A tool for visualizing network meta-analysis results for multiple outcomes.

Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes are of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a non-trivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. In this paper, we aim to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application