A data-driven approach could revolutionize mental health treatment

A new study from Macquarie University suggests that, rather than relying on the ‘bible’ of mental health diagnosis, we need a data-driven approach, based on the ways in which symptoms naturally occur in real life, and this approach could have enormous implications for future treatments. options.

The work is published in the news Clinical psychological science.

The Diagnostic and Statistical Manual of Mental Health (DSM) has been the standard text for diagnosing mental disorders for almost 75 years.

But when two people can receive the same diagnosis despite not having a single symptom in common, Miri Forbes, associate professor of psychology at Macquarie University, says the system needs to be rethought.

“To be diagnosed with major depressive disorder – also called clinical depression – someone must meet five of the nine criteria in the DSM, including a depressed mood or a loss of interest or pleasure in their usual activities,” she says.

“Because the criteria include examples like ‘weight loss or gain’ and ‘insomnia or hypersomnia,’ there are ultimately thousands of different symptom profiles that meet the diagnostic criteria.

“The root of the problem lies in the fact that DSM diagnoses are based on committee consensus and are not arrived at in a systematic manner.”

For example, the definition of attention deficit and hyperactivity disorder (ADHD) stems from efforts in educational research to describe students with hyperactive, restless, and inattentive behavior. Alzheimer’s disease is based on Dr. Alois Alzheimer with his patient Auguste Deter.

Other diagnoses, such as autism, were based on a doctor’s observations of a series of patients. Some, such as depressive disorders, are based on centuries of clinical observations.

To complicate matters further, the DSM moves slowly. It can take many years for emerging disorders to be formally included, and once a disorder is added it is very difficult to remove or make changes.

Associate Professor Forbes says that because there is so much overlap between conditions, mental health professionals often arrive at different diagnoses for the same patient.

There’s also the confounding problem of comorbidity: disorders occur together far more often than chance, and people often need multiple diagnostic labels to describe the symptoms they experience.

“All of these problems combine to make it difficult not only to accurately diagnose conditions in the first place, but also to figure out what causes them and develop more effective treatments,” she says.

“Many researchers now believe that we have reached the point where the DSM is slowing our progress in these efforts. The evidence shows us that DSM diagnoses do not closely reflect how people experience these symptoms in the real world.”

Exploring the alternatives

Over the past fifteen years, more and more alternatives to the DSM have gained popularity.

One such alternative is the Hierarchical Taxonomy of Psychopathology (HiTOP)a framework that organizes psychopathology using broad dimensions such as ‘detachment’, ‘disinhibition’ and ‘antagonism’. These dimensions can summarize the severity of a person’s symptoms in each domain and, when combined, describe that person’s characteristic profile of thoughts, feelings, and behaviors.

Each broad domain can also be broken down into increasingly narrower dimensions, allowing doctors to get right down to a complete, detailed profile of all the symptoms a person is experiencing.

The HiTOP framework has quickly grown in popularity because it overcomes many of the limitations of the DSM and has strong research support.

However, HiTOP has its limitations, as much of the evidence base is still tied to DSM diagnoses, which remain the focus for most data collection efforts in mental health research.

HiTOP also currently covers only 71 of the diagnoses in the DSM, leaving out important domains such as neurodevelopmental disorders and cognitive functions.

To address the flaws of both the DSM and HiTOP, Associate Professor Forbes and her colleagues started with a blank slate to reconstruct the DSM from the ground up, based on real-world patterns in how people experience symptoms.

To do this, they reduced the DSM to the list of symptoms that make up all of its diagnoses. They recruited almost 15,000 adults from Australia and the United States, including people with and without pre-existing mental health conditions and from diverse backgrounds. Each person completed an extensive survey about the extent to which he or she had experienced symptoms in the past twelve months.

The data was analyzed focusing on points of agreement between analyzes in different groups of people and using various statistical methods to reorganize the symptoms into a new data-driven framework.

The new framework fits well with the higher levels of the HiTOP framework, is elaborated in more detail at the lower levels and can extend the coverage of HiTOP to 167 DSM diagnoses.

Associate Professor Forbes said that while some similarities emerged between the new framework and the diagnoses defined in the DSM, there were also clear differences.

‘It turns out that if you don’t tell the symptoms where they belong, and instead follow the patterns in how people actually experience them, they don’t form the symptom sets of the diagnoses described in the DSM – the disorders. unravel,” she says.

“The symptoms of most anxiety disorders, eating disorders, and sexual dysfunctions remained clustered together in much the same way as in the DSM, suggesting that these symptoms often coexist.

‘But many other DSM disorders fell apart, including some of the oldest and most studied disorders in the field, such as major depressive disorder, post-traumatic stress disorder (PTSD), autism spectrum disorder (ASD) and ADHD.

“These symptom sets split and were spread across our framework.”

Classification can influence treatment

Although the way disorders are categorized may seem largely academic, it is in fact extremely important for developing treatments that work and for understanding why some treatments work well for some people but not at all for others.

Associate Professor Forbes says this supports the idea that some treatments only work on limited, specific sets of symptoms.

“Antidepressants are a good example,” she says. “They are often prescribed for mood and anxiety disorders, but not everyone benefits from them.

‘For example, antidepressants appear to work particularly well for symptoms such as a bad mood, feelings of guilt and suicidal thoughts.

“But because severe depression has such a wide range of possible symptoms, a treatment such as antidepressants that works for one person may not work as well for someone else with different depression symptoms such as insomnia, feeling tense and loss of interest in their usual activities.

‘What we’re seeing is that depressive disorder isn’t just one thing – it’s many different things all under one label, and the next step is being able to separate those things.

“Tracking these patterns in the way people experience symptoms could pave the way for developing new treatments or better targeting the treatments we have, understanding the mechanisms and risk factors for specific symptom sets, and then for connecting people to the treatments they need to get better. .

‘The DSM was a good starting point and has taken us a long way, but we are now ready to go further.

“I’m very excited about the potential here.”

This content was originally published on The Macquarie University Lighthouse.