A Useful Support System for Psychiatric Diagnostics and Follow-up in Adult Psychiatry and Primary Care (ASPP)

NCT05827406 | Recruiting

• 1 other identifier: 2022-07160-01
• Study Type: observational
• Target: 2,200
• Locations: 1 country
• Sites: 5

Brief Summary

Psychiatric diagnostics involve collecting information about a patient's symptoms, age of onset, development over time, relation to external stress, and ability to function and experience suffering. This information is classified using ICD (World Health Organisation) and DSM (American diagnosis system). Currently, there is a lack of a scientifically evaluated system to support these diagnostics. This project seeks to develop and evaluate a set of self-assessment scales which collect and classify relevant data and serve as support for clinicians. These scales are made up of questions about typical psychiatric symptoms, which are assessed and evaluated using a statistical method (Item Response Theory). After testing and evaluation, a scale could consist of approximately 10 items or less. The scales are then tested together to see if the number of items and scales can be reduced further.

Conditions

Mental Disorder; Symptoms and Signs

Keywords

Rating Scale; Descriptive Psychopathology

Outcome Measures

Primary Outcomes (1)

• Scalability H

  Scalability coefficient H (ranging from 0 to 1) of each subdimension as assessed with Mokken Item Response Theory. A minimal requirement of H ≥ 0.4 is deemed necessary for inclusion in the DPS.

  Day 1

Study Arms (14)

Group 1: item set 1

A group of patients with psychiatric problems, consecutively recruited from 5 specific sites in Stockholm, are asked to participate in the study. If they agree to participate, they will respond to a group of item sets (item set 1) that measure relevant psychopathological dimensions. Two hundred patients are asked to participate. We assume 50 % participation to receive 100 responses.

Group 2: reduced item set 1

We analyze data from group 1 and optimize item set 1 for the best measurement with the least number of items. Then, we recruit 200 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the reduced item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced item set 1 fails to validate, we may need to change it and recruit additional groups to optimize it.

Group 3: item set 2

A group of patients with psychiatric problems, consecutively recruited from 5 specific sites in Stockholm, are asked to participate in the study. If they agree to participate, they will respond to a group of item sets (item set 2) that measure relevant psychopathological dimensions. Two hundred patients are asked to participate. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment.

Group 4: reduced item set 2

We analyze data from group 3 and optimize item set 2 for the best measurement with the least number of items. Then, we recruit 200 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the reduced item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced item set 2 fails to validate, we may need to change it and recruit additional groups to optimize it

Group 5: item set 3

A group of patients with psychiatric problems, consecutively recruited from 5 specific sites in Stockholm, are asked to participate in the study. If they agree to participate, they will respond to a group of item sets (item set 3) that measure relevant psychopathological dimensions. Two hundred patients are asked to participate. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment.

Group 6: reduced item set 3

We analyze data from group 5 and optimize item set 3 for the best measurement with the least number of items. Then, we recruit 200 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the reduced item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced item set 3 fails to validate, we may need to change it and recruit additional groups to optimize it

Group 7: item set 4

A group of patients with psychiatric problems, consecutively recruited from 5 specific sites in Stockholm, are asked to participate in the study. If they agree to participate, they will respond to a group of item sets (item set 4) that measure relevant psychopathological dimensions. Two hundred patients are asked to participate. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment.

Group 8: reduced item set 4

We analyze data from group 7 and optimize item set 4 for the best measurement with the least number of items. Then, we recruit 200 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the reduced item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced item set 4 fails to validate, we may need to change it and recruit additional groups to optimize it.

Group 9: item set 5

A group of patients with psychiatric problems, consecutively recruited from 5 specific sites in Stockholm, are asked to participate in the study. If they agree to participate, they will respond to a group of item sets (item set 5) that measure relevant psychopathological dimensions. Two hundred patients are asked to participate. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment.

Group 10: reduced item set 5

We analyze data from group 9 and optimize item set 5 for the best measurement with the least number of items. Then, we recruit 200 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the reduced item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced item set 5 fails to validate, we may need to change it and recruit additional groups to optimize it.

Group 11: item set 6

A group of patients with psychiatric problems, consecutively recruited from 5 specific sites in Stockholm, are asked to participate in the study. If they agree to participate, they will respond to a group of item sets (item set 6) that measure relevant psychopathological dimensions. Two hundred patients are asked to participate. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment.

Group 12: reduced item set 6

We analyze data from group 11 and optimize item set 6 for the best measurement with the least number of items. Then, we recruit 200 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the reduced item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced item set 6 fails to validate, we may need to change it and recruit additional groups to optimize it.

Group 13: combined item sets

In this group, we aim to combine the optimized item sets in item sets 1-6. We recruit 1000 new patients from the five sites in Stockholm with psychiatric issues for the psychometric validation of the combined item set. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment.

Group 14: reduced combined item sets

We analyze data from group 13 and optimize the combined item set for the best measurement with the least number of items and the least number of dimensions. We estimate a 50% participation rate, with the potential to modify that number based on the response rate of the previous recruitment. If the reduced combined item set fails to validate, we may need to change it and recruit additional groups to optimize it.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Outpatients with psychiatric problems attend either one of the three adult psychiatric outpatient units or one of the two primary care centers.

You may qualify if:

• Patients with psychiatric problems

You may not qualify if:

• None

Sponsors & Collaborators

• Karolinska Institutet: lead

Study Sites (5)

Wemind

Stockholm, 112 34, Sweden

RECRUITING

Prima vuxen

Stockholm, 117 43, Sweden

RECRUITING

Gustavsbergs vårdcentral

Stockholm, 134 40, Sweden

RECRUITING

Stuvsta vårdcentral

Stockholm, 141 40, Sweden

RECRUITING

Psykiatri Sydväst

Stockholm, 141 86, Sweden

RECRUITING

Related Publications (9)

• Adler M, Liberg B, Andersson S, Isacsson G, Hetta J. Development and validation of the Affective Self Rating Scale for manic, depressive, and mixed affective states. Nord J Psychiatry. 2008;62(2):130-5. doi: 10.1080/08039480801960354.

  PMID: 18569776 RESULT

• Adler M, Brodin U. An IRT validation of the Affective Self Rating Scale. Nord J Psychiatry. 2011 Dec;65(6):396-402. doi: 10.3109/08039488.2011.577187. Epub 2011 May 4.

  PMID: 21539501 RESULT

• Adler M, Backlund L, Edman G, Osby U. Symptoms and treatment of bipolar patients in Sweden. Int J Psychiatry Clin Pract. 2012 Sep;16(3):170-7. doi: 10.3109/13651501.2011.653377. Epub 2012 Mar 21.

  PMID: 22432978 RESULT

• Adler M, Hetta J, Isacsson G, Brodin U. An item response theory evaluation of three depression assessment instruments in a clinical sample. BMC Med Res Methodol. 2012 Jun 21;12:84. doi: 10.1186/1471-2288-12-84.

  PMID: 22721257 RESULT

• Isacsson G, Adler M. Randomized clinical trials underestimate the efficacy of antidepressants in less severe depression. Acta Psychiatr Scand. 2012 Jun;125(6):453-9. doi: 10.1111/j.1600-0447.2011.01815.x. Epub 2011 Dec 19.

  PMID: 22176585 RESULT

• Cameron IM, Scott NW, Adler M, Reid IC. A comparison of three methods of assessing differential item functioning (DIF) in the Hospital Anxiety Depression Scale: ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure. Qual Life Res. 2014 Dec;23(10):2883-8. doi: 10.1007/s11136-014-0719-3. Epub 2014 May 22.

  PMID: 24848597 RESULT

• Abe C, Liberg B, Song J, Bergen SE, Petrovic P, Ekman CJ, Sellgren CM, Ingvar M, Landen M. Longitudinal Cortical Thickness Changes in Bipolar Disorder and the Relationship to Genetic Risk, Mania, and Lithium Use. Biol Psychiatry. 2020 Feb 1;87(3):271-281. doi: 10.1016/j.biopsych.2019.08.015. Epub 2019 Aug 29.

  PMID: 31635761 RESULT

• Liberg B, Rahm C, Panayiotou A, Pantelis C. Brain change trajectories that differentiate the major psychoses. Eur J Clin Invest. 2016 Jul;46(7):658-74. doi: 10.1111/eci.12641. Epub 2016 Jun 15.

  PMID: 27208657 RESULT

• Liberg B, Klauser P, Harding IH, Adler M, Rahm C, Lundberg J, Masterman T, Wachtler C, Jonsson T, Kristoffersen-Wiberg M, Pantelis C, Wahlund B. Functional and structural alterations in the cingulate motor area relate to decreased fronto-striatal coupling in major depressive disorder with psychomotor disturbances. Front Psychiatry. 2014 Dec 4;5:176. doi: 10.3389/fpsyt.2014.00176. eCollection 2014.

  PMID: 25538633 RESULT

MeSH Terms

Conditions

Mental Disorders; Signs and Symptoms

Condition Hierarchy (Ancestors)

Pathological Conditions, Signs and Symptoms

Study Officials

• Mats O Adler, MD

  Karolinska Institutet, Department of Clinical Neuroscience

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Mats O Adler, MD

CONTACT

+46707717977; mats.adler@ki.se

Benny S Liberg, MD

CONTACT

+46722375890; benny.liberg@ki.se

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 23, 2023
• First Posted: April 25, 2023
• Study Start: May 1, 2023
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: October 18, 2023

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share

Locations

SE (5)