Quantifying heterogeneity attributable to polythetic diagnostic criteria: theoretical framework and empirical...

Area of Science:

• Psychiatry and Mental Health
• Computational Psychology
• Mathematical Modeling

Background:

• Psychiatric disorders exhibit considerable heterogeneity, where individuals with the same diagnosis may present with vastly different symptoms.
• Polythetic diagnostic criteria contribute to this heterogeneity, lacking a unified theoretical understanding of symptom set coherence.

Purpose of the Study:

• To propose a general framework for analyzing the logical, mathematical, and informational structure of psychiatric diagnostic categories.
• To assess the coherence, diversity, and symptom overlap within diagnostic criteria sets from the Diagnostic and Statistical Manual (DSM).

Main Methods:

• Developed a framework integrating combinatorial mathematics, set theory, and information theory.
• Applied the framework theoretically to 18 DSM diagnostic categories.
• Empirically applied the framework to two large datasets for major depressive disorder and posttraumatic stress disorder.

Main Results:

• Theoretical analysis indicated that many diagnostic categories allow for individuals with the same diagnosis to share no symptoms.
• On average, any two theoretically possible symptom combinations share less than half their symptoms.
• Empirical analysis of major depressive disorder and posttraumatic stress disorder showed patients sharing approximately 60% of symptoms, yet pairs with no shared symptoms were observed.

Conclusions:

• The proposed framework quantifies significant heterogeneity in psychiatric diagnoses, challenging the assumption of shared symptomatology.
• Results have critical implications for etiological research, diagnostic refinement, and the measurement of psychiatric disorders.
• Identified substantial variability in symptom presentation for major depressive disorder and posttraumatic stress disorder.