Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Fusar‐Poli, Paolo; Manchia, Mirko; Koutsouleris, Nikolaos; Leslie, David; Woopen, Christiane; Calkins, Monica E.; Dunn, Michael; Tourneau, Christophe Le; Mannikko, Miia; Mollema, Tineke; Oliver, Dominic; Rietschel, Marcella; Reininghaus, Eva; Squassina, Alessio; Valmaggia, Lucia; Kessing, Lars Vedel; Sanchez-Moreno, José; Correll, Christoph U.; Arango, Celso; Andreassen, Ole A.

doi:10.1016/j.euroneuro.2022.08.001

Cited by 46 publications

(12 citation statements)

References 139 publications

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“…However, preventing the onset of mental disorders can become a tantalizing strategy for reducing at the same time the risk of developing phy-sical diseases 143 . Indeed, the cost and risk associated with preventive approaches (e.g., ethical concerns 172 ) can be offset by concurrently reducing the burden of both psychiatric disorders and physical diseases 165,173 . Integrating early detection and prevention of mental health and physical conditions may be particularly costeffective in resource-constrained settings 142 .…”

Section: Discussionmentioning

confidence: 99%

Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction

et al. 2023

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Empirical evidence indicates a significant bidirectional association between mental disorders and physical diseases, but the prospective impact of mental disorders on clinical outcomes of physical diseases has not been comprehensively outlined. In this PRISMA‐ and COSMOS‐E‐compliant umbrella review, we searched PubMed, PsycINFO, Embase, and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, up to March 15, 2022, to identify systematic reviews with meta‐analysis that examined the prospective association between any mental disorder and clinical outcomes of physical diseases. Primary outcomes were disease‐specific mortality and all‐cause mortality. Secondary outcomes were disease‐specific incidence, functioning and/or disability, symptom severity, quality of life, recurrence or progression, major cardiac events, and treatment‐related outcomes. Additional inclusion criteria were further applied to primary studies. Random effect models were employed, along with I2 statistic, 95% prediction intervals, small‐study effects test, excess significance bias test, and risk of bias (ROBIS) assessment. Associations were classified into five credibility classes of evidence (I to IV and non‐significant) according to established criteria, complemented by sensitivity and subgroup analyses to examine the robustness of the main analysis. Statistical analysis was performed using a new package for conducting umbrella reviews (https://metaumbrella.org/). Population attributable fraction (PAF) and generalized impact fraction (GIF) were then calculated for class I‐III associations. Forty‐seven systematic reviews with meta‐analysis, encompassing 251 non‐overlapping primary studies and reporting 74 associations, were included (68% were at low risk of bias at the ROBIS assessment). Altogether, 43 primary outcomes (disease‐specific mortality: n=17; all‐cause mortality: n=26) and 31 secondary outcomes were investigated. Although 72% of associations were statistically significant (p<0.05), only two showed convincing (class I) evidence: that between depressive disorders and all‐cause mortality in patients with heart failure (hazard ratio, HR=1.44, 95% CI: 1.26‐1.65), and that between schizophrenia and cardiovascular mortality in patients with cardiovascular diseases (risk ratio, RR=1.54, 95% CI: 1.36‐1.75). Six associations showed highly suggestive (class II) evidence: those between depressive disorders and all‐cause mortality in patients with diabetes mellitus (HR=2.84, 95% CI: 2.00‐4.03) and with kidney failure (HR=1.41, 95% CI: 1.31‐1.51); that between depressive disorders and major cardiac events in patients with myocardial infarction (odds ratio, OR=1.52, 95% CI: 1.36‐1.70); that between depressive disorders and dementia in patients with diabetes mellitus (HR=2.11, 95% CI: 1.77‐2.52); that between alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C (RR=3.15, 95% CI: 2.87‐3.46); and that between schizophrenia and cancer mortality in patients with cancer (standardized mean ratio...

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Section: Discussionmentioning

confidence: 99%

Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction

et al. 2023

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“…All data were collected and stored in encrypted and secure servers following the guidelines and standards of the 2018 European General Data Protection Regulation and were solely managed by project researchers. The project is in line with the ethical standards of European experts on personalized interventions and precision psychiatry [ 73 ]. All participants provided written informed consent before inclusion in the study.…”

Section: Methodsmentioning

confidence: 99%

Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies

Anmella¹,

Sanabra²,

Primé-Tous³

et al. 2023

J Med Internet Res

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Background Many people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, and digital tools have been proposed as a solution. Objective We aimed to present the development, feasibility, and potential effectiveness of Vickybot, a chatbot aimed at screening, monitoring, and reducing anxiety-depressive symptoms and work-related burnout, and detecting suicide risk in patients from PC and health care workers. Methods Healthy controls (HCs) tested Vickybot for reliability. For the simulation study, HCs used Vickybot for 2 weeks to simulate different clinical situations. For feasibility and effectiveness study, people consulting PC or health care workers with mental health problems used Vickybot for 1 month. Self-assessments for anxiety (Generalized Anxiety Disorder 7-item) and depression (Patient Health Questionnaire-9) symptoms and work-related burnout (based on the Maslach Burnout Inventory) were administered at baseline and every 2 weeks. Feasibility was determined from both subjective and objective user-engagement indicators (UEIs). Potential effectiveness was measured using paired 2-tailed t tests or Wilcoxon signed-rank test for changes in self-assessment scores. Results Overall, 40 HCs tested Vickybot simultaneously, and the data were reliably transmitted and registered. For simulation, 17 HCs (n=13, 76% female; mean age 36.5, SD 9.7 years) received 98.8% of the expected modules. Suicidal alerts were received correctly. For the feasibility and potential effectiveness study, 34 patients (15 from PC and 19 health care workers; 76% [26/34] female; mean age 35.3, SD 10.1 years) completed the first self-assessments, with 100% (34/34) presenting anxiety symptoms, 94% (32/34) depressive symptoms, and 65% (22/34) work-related burnout. In addition, 27% (9/34) of patients completed the second self-assessment after 2 weeks of use. No significant differences were found between the first and second self-assessments for anxiety (t8=1.000; P=.34) or depressive (t8=0.40; P=.70) symptoms. However, work-related burnout scores were moderately reduced (z=−2.07, P=.04, r=0.32). There was a nonsignificant trend toward a greater reduction in anxiety-depressive symptoms and work-related burnout with greater use of the chatbot. Furthermore, 9% (3/34) of patients activated the suicide alert, and the research team promptly intervened with successful outcomes. Vickybot showed high subjective UEI (acceptability, usability, and satisfaction), but low objective UEI (completion, adherence, compliance, and engagement). Vickybot was moderately feasible. Conclusions The chatbot was useful in screening for the presence and severity of anxiety and depressive symptoms, and for detecting suicidal risk. Potential effectiveness was shown to reduce work-related burnout but not anxiety or depressive symptoms. 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 to improve engagement. A consensus on how to report UEIs and validate digital solutions, particularly for chatbots, is required.

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“…Still, even when the prediction tools have obtained clinically relevant accuracy, there are some bottlenecks before they can be implemented in real‐world healthcare. This includes an initial lack of cost‐effectiveness, inadequate levels of education, and training of healthcare personnel as well as patients, and structural barriers of healthcare providers [53]. Still, the recent findings are encouraging, and it is expected that precision medicine will lead to a paradigm change in healthcare with transformative potential for individuals who are at high risk or are diagnosed with a complex disease.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to genetics, several lines of research have focused on prediction modeling building on clinical, cognitive, and brain imaging approaches. With the emerging transformative knowledge in psychiatric genetics, neuroscience, and brain imaging, there is a large potential for implementing precision psychiatry in several areas [53]. Despite this, psychiatry has been slow in implementing precision medicine [54], and potential applications have not yet been considered for real‐world implementation [55], or even been demonstrated to be useful in the clinic [52].…”

Section: Precision Medicine In Psychiatrymentioning

confidence: 99%

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

et al. 2023

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Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic‐based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high‐throughput molecular technologies, together with large collections of health data and novel data‐driven approaches, offers promise toward improved individual health through precision medicine.

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Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Cited by 46 publications

References 139 publications

Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction

Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction

Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

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