mail.missouri.edu.2Department of Psychological Science, University of Missouri, United States.3Department of Psychological Science, University of Pittsburg, United States.Abstract

Psychiatric diagnostic systems, such as The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), use expert consensus to determine diagnostic criteria sets and rules (DCSRs), rather than exploiting empirical techniques to arrive at optimal solutions (OS). Our project utilizes complete enumeration (i.e., generating all possible subsets of item combinations A and B with all possible thresholds, T) to evaluate all possible DCSRs given a set of relevant diagnostic data. This method yields the entire population distribution of diagnostic classifications (i.e., diagnosis of the disorder versus no diagnosis) produced by a set of dichotomous predictors (i.e., diagnostic criteria). Once unique sets are enumerated, optimization on some predefined correlate or predictor will maximally separate diagnostic groups on one or more, disorder-specific « outcome » criteria. We used this approach to illustrate how to create a common Substance Use Disorder (SUD) DCSR that is applicable to multiple substances. We demonstrate the utility of this approach with respect to alcohol use disorder and Cannabis Use Disorder (CUD) using DSM-5 criteria as input variables. The optimal SUD solution with a moderate or above severity grading included four criteria (i.e. 1) having a strong urge or craving for the substance (CR), 2) failure to fulfill major role obligations at work school or home (FF), 3) continued use of the substance despite social or interpersonal problems caused by the substance use (SI) and 4) physically hazardous use (HU)) with a diagnostic threshold of two. The derived DCSR was validated with known correlates of SUD and performed as well as DSM-5. Our findings illustrate the value of using an empirical approach to what is typically a subjective process of choosing criteria and algorithms that is prone to bias. The optimization of diagnostic criteria can reduce criteria set sizes, resulting in decreased research, clinician, and patient burden.

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