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

Karolinska Institute

Status: Enrolling

Conditions: Mental Disorder; Symptoms and Signs

Study type: Observational

Funder types: Other

Identifiers

NCT05827406

2022-07160-01

Details and patient eligibility

About

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.

Full description

Psychiatric diagnostics are based on the systematization of information that can be collected about a patient's symptoms and behavior. The information typically concerns the symptoms a patient exhibits, the age of onset for the symptoms, how they have developed over time, whether they are related to any type of external stress, and whether the symptoms cause suffering and/or functional impairment. The collected information can then be classified using diagnostic manuals. The official one is the WHO's International Classification of Diseases (ICD). The American diagnostic system DSM is also used.

Today, there is a lack of a scientifically evaluated (validated) support system for psychiatric diagnostics. With this project, we aim to develop and scientifically evaluate a set of self-assessment scales that can collect and systematize relevant information about patients' symptom profiles, age of onset, long-term course, any possible relation to external stress, and function and suffering, which can serve as support for clinicians in the diagnostic process. Some of the scales are also intended to be used for follow-up.

Assessment scales consist of sets of questions or statements, which in this context are called "items." They describe deviant psychological symptoms (psychopathology) as gradually increasing phenomena (dimensions). In the project, we develop sets of items based on the most common diagnostic areas within psychiatry and general medicine. These are then tested on patients and evaluated using a modern statistical method specialized for developing scales, called Item Response Theory (IRT). The first sets of items contain between approximately 30 and 60 items. After evaluation, only the best-performing items are retained. Testing is repeated on new patient groups until they function optimally. The goal is for each scale to consist of approximately 10 items. Thereafter, the reduced scales are tested together and evaluated with IRT to, if possible, reduce the number of scales and items.

Enrollment: 2,200 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients with psychiatric problems

Exclusion criteria

• None

Trial design

2,200 participants in 14 patient groups

Group 1: item set 1

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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

Description:

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.