New article accepted for publication in Translational Psychiatry

A new article just got accepted for publication in the Translational Psychiatry.

Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data

Authors: John Wallert, Julia Boberg, Viktor Kaldo, David Mataix-Cols, Oskar Flygare, James Crowley, Matthew Halvorsen, Fehmi Ben Abdesslem, Magnus Boman, Evelyn Andersson, Nils Hentati Isacsson, Ekaterina Ivanova and Christian Rück.

Interesting reading ahead!

Two new research assistants needed

Interested in psychiatry research? Want to join the group? We are hiring not one but TWO new research assistants!

The group is certainly expanding. Team leader John Wallert just advanced to Assistant Professor within the group, congratulations! 🎯

We are also hiring two new research assistants for two slightly different employments.

One is for The Joining Forces RCT crew, working with Volen Ivanov and Sofia Jägholm. In this job you will do different tasks related to their RCT on a new combination treatment for hoarding disorder, including screening interviews and home visits with hoarding patients, interviews with housing supporters and managers in the social services and monitoring of data collection as well contact with the project’s various collaboration partners. Read more and apply here. Last day to apply is July 31.

The other one is for John Wallert’s interdisciplinary modeling team, an employment in a project in precision psychiatry. This employment includes assisting John and three others with tasks such as database work, archiving of completed projects, meeting documentation and planning, create and then support a structure for work in the steering group for one of our larger projects and write research information texts. Read more and apply here. Last day to apply is July 31.

Welcoming Olly Kravchenko

We are happy to announce that Olly Kravchenko just joined the Rück research lab as our new PhD student!

Photo: Micke Sandström

Olly has a background in project management, product testing, and user experience research in tech start-ups. She also has a MSc in German and English Language & Literature (Kyiv National Linguistic University) and a MSc in Public Health Sciences (Stockholm University). She recently finished an internship at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, working on a genetically informed research project using data from the Swedish Twin Registry.

Here at the Rück Research Lab, Olly will mainly be working with the PRiSMED project, with John Wallert as main supervisor and Christian Rück as co-supervisor.

About the project
PRiSMED: Predicting health and socioeconomic outcomes in patients with common psychiatric disorders

Depression, social anxiety disorder, panic disorder, and obsessive-compulsive disorder are common mental disorders with a combined point prevalence of 15%. Almost 50% of diagnoses leading to sick-leave in Sweden are psychiatric and their share is increasing. Cognitive behavior therapy (CBT) is first-line treatment for these conditions. Yet, 30-60% of patients will not respond to CBT. Several predictors of CBT outcome have been proposed. Results are mixed and lack predictive acuity to guide clinical decisions. Prediction studies using larger samples and multimodal data (clinical, register, genetic) are urgently needed. Coupled with advanced modelling, we could augment precision psychiatry allowing for tailored intervention and cost-effective resource use.

The project main purpose is improved outcome prediction for common mental disorders in routine clinical care. We will build prediction models for both clinical CBT outcomes (e.g., remission) and long-term outcomes (e.g., poverty) using multimodal data on 5,000 genotyped and register-linked patients treated with CBT – the hitherto largest data collection of its kind.

The project has three aims. Aim 1 uses traditional statistical methods to identify group-level predictors for these outcomes. Aim 2 applies Machine Learning (ML) to identify which individual will experience these outcomes. Aim 3 will plan and initiate a trial comparing a developed ML algorithm versus clinicians at predicting remission in prospective CBT patients.

Doctoral (PhD) student position in Precision Psychiatry

📣 We are looking for a PhD student interested in Precision Psychiatry to join our team! 📣

The overarching purpose of your PhD project is to improve routine-care predictions of post cognitive behaviour therapy (CBT) outcomes for common mental disorders. Our developed prediction models will be focused on avoiding unnecessary treatment failures, minimizing adverse long-term health and socioeconomic events, and promoting effective use of psychiatric resources. Today, there is a general lack of accurate prognostic risk tools implemented in routine psychiatric care, which directly hampers our possibilities for timely and cost-effective tailored intervention and care. The main objective of your PhD project is thus to construct and validate prediction models for both clinical CBT outcomes and long-term register outcomes using the world’s largest multimodal database in the field (clinical, register-linked, and genetic data) including 5,000 individuals treated with CBT.

You will be supervised by John Wallert and co-supervised by Christian Rück.

Click here to read more and apply.

Grant from the Swedish Research Council

A member of our Research Group, John Wallert, recently received a 4.5 million SEK grant from the Swedish Research Council (Vetenskapsrådet, VR) for his project about suicide among compulsory mental care

The project is a collaboration with Oxford University and involves studying risk factors for suicide in a cohort of more than 100 000 compulsory mental Care acts (Lagen om psykiatrisk tvångsvård, LPT, in Swedish), using 40 years of unique national register data.

Congratulations! 

Two new publications:

On iCBT for Symptoms of Anxiety and Depression After Myocardial Infarction, and Cognitive Dedifferentiation in Abnormal Cognitive Decline

This week, John Wallert published not one but two articles! 🎉

  1. Internet-Based Cognitive Behavioral Therapy for Patients Reporting Symptoms of Anxiety and Depression After Myocardial Infarction: U-CARE Heart Randomized Controlled Trial Twelve-Month Follow-up

    The aim of this study was to evaluate the long-term effectiveness of internet-based cognitive behavioral therapy on self-reported symptoms of anxiety and depression in patients 12 months after a myocardial infarction and to explore subsequent occurrences of cardiovascular disease events. Shortly after acute myocardial infarction, 239 patients reporting mild-to-moderate symptoms of anxiety or depression were randomized to 14 weeks of therapist-guided internet-based cognitive behavioral therapy (n=117) or treatment as usual (n=122). 

    The study’s conclusions was that internet-based cognitive behavioral therapy was not superior in reducing self-reported symptoms of depression or anxiety compared to treatment as usual at the 1-year follow-up after myocardial infarction. A reduction in cardiac-related anxiety was observed but was not significant after adjusting for multiple comparisons. There was no difference in risk of cardiovascular events between the treatment groups. Low treatment adherence, which might have affected treatment engagement and outcomes, should be considered when interpreting these results.

  2. Cognitive dedifferentiation as a function of cognitive impairment in the ADNI and MemClin cohorts

    The cause of cognitive dedifferentiation has been suggested as specific to late-life abnormal cognitive decline rather than a general feature of aging. This hypothesis was tested in two large cohorts with different characteristics. The subjects came from two research databases in North America and Sweden. Dedifferentiation was explained by cognitive impairment when controlling for age, sex, and education. This finding replicated across two separate, large cohorts of older individuals. Knowledge that the structure of human cognition becomes less diversified and more dependent on general intelligence as a function of cognitive impairment should inform clinical assessment and care for these patients as their neurodegeneration progresses.