Introduction The diagnosis of personality disorder (PD) is considered a risk factor for suicidal ideation and behavior [e.g., 1 ] and low quality of life [e.g., 2 ]. While the prevalence rates ranged from 7.3 to 15.7% in the general population [3, 4] , rates turned out to be higher in treatment-seeking individuals, ranging from 31% in outpatients [5] to 51% in inpatients [6] . In addition, PDs frequently co-occur with axis I disorders, in particular affective, anxiety, and substance use

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... s [7] , which might make it more difficult to detect any PD. For individuals with axis I disorders, the presence of a PD seems to be associated with a higher severity of psychopathology, lower levels of global psychosocial functioning, and more interpersonal problems [e.g., 8 ]. When PDs remain unnoticed or untreated, individuals with co-occurring PD benefit considerably less from disorder-related treatments for axis I disorders than patients without PD [7, 9] . Consequently, individuals with PD were considered to be a cause of high medical, financial, and social costs [e.g., 10, 11 ]. Overall, the above-described facets of PDs point out the need for reliable and valid measures to detect PDs economically. Several authors suggested a two-stage approach [e.g., [12] [13] [14] . At the first stage, all incoming indi-Abstract Background: About 30% of outpatients meet the criteria of a personality disorder (PD). When PD remains unnoticed or untreated, individuals with co-occurring PD benefit considerably less from disorder-related treatments for axis I disorders than patients without PD. The present study examines the diagnostic efficiency of the German version of the Standardized Assessment of Personality -Abbreviated Scale Self-Rating Version (SAPAS-SR). Sampling and Methods: Based on the signal detection theory, receiver operating characteristics as well as reliability and validity indicators are determined in a heterogeneous outpatient sample (n = 230). Results: The values of internal consistency and the validity indicators turned out to be in the expected range. The area under the curve was low, at 0.67. Using the cutoff point of 4, sensitivity (80%) and specificity (46%) values were at a level that was only acceptable. Conclusions: Since specificity was lower than in previous studies, using the SAPAS-SR results in a moderate cost efficiency only.