Lars Klintwall

What could a system for psychiatric diagnostic look like, one that does not assume the existence of discrete and distinct psychiatric diseases?

Most clinicians and researchers in psychiatry are aware of the flaws with the current categorical systems (DSM / ICD): patients often meet the criteria for multiple diagnoses at the same time, and patients with the same diagnosis can be very different from each other. Moreover, a diagnosis does not necessarily say anything about either etiology (with some rare exceptions on the individual level) or treatment (with exceptions for OCD, panic disorder, or phobias who all respond well to exposure therapy).

The method I am exploring, which possibly could solve the above problems, is to analyze a patient's issues as parts of a network. The idea is that the problems (which can be behaviors, emotions, or contextual factors) are largely caused by each other. If problems form causal chains in the form of feedback loops, they can become self-reinforcing: the patient gets stuck in a dysfunctional state. These causal chains need not be limited to our current categories (ADHD, depression, social anxiety, etc.), and can to some extent be idiosyncratic.

Network analysis is a blooming international research field. My humble contribution is to try to link state-of-the-art statistical and theoretical insight with clinical practice. My current approach is to use the patient's and relatives' perception of how the problems are interconnected, which can be mapped through structured questionnaires / interviews, or short daily assessments over about a month's time. Both methods have recently been clinically tested, with promising results.

In the long term, the plan is to develop methods to improve the quality of the individual-specific networks, partly by integrating information from multiple informants (e.g. the patient themselves, together with a parent or partner), and to combine this with more objective time series analysis. We also want to investigate whether the method leads to more accurate individualized interventions (precision medicine).