期刊
ARCHIVES OF WOMENS MENTAL HEALTH
卷 23, 期 3, 页码 339-349出版社
SPRINGER WIEN
DOI: 10.1007/s00737-019-01000-3
关键词
Sex; Psychosis; Clinical high risk; Symptoms; Prediction
类别
Sex differences may be important for understanding underlying pathophysiological mechanisms and developing effective preventions and treatments of mental disorders. Despite sex differences in the onset of psychosis, patients at clinical high risk for psychosis (CHR) are underinvestigated for sex effects, especially with respect to models for prediction of conversion to psychosis. We studied psychopathological sex differences in referrals to a German early detection service and in its subgroup of converters and examined sex-specific psychopathological prediction models. In 152 male and 90 female referrals (88% at CHR; 35% converters), symptoms assessed with the Structured Interview for Psychosis-Risk Syndromes were investigated for sex differences using effect sizes. Sex-specific prediction models of psychosis were separately generated using Cox regressions with a LASSO operator. We found different small sex effects (0.10 < Rosenthal's r < 0.30) in the referral and in the converter sample. In the referral sample, exclusively, males showed more pronounced symptoms (all negative symptoms incl. reduced stress tolerance, grandiosity, and disorganized communication); in converters, females experienced more pronounced perceptual abnormalities, bizarre thinking, and odd behaviors, while males expressed and experienced emotions to a lower degree. Furthermore, sexes differed in psychosis-predictive symptoms: suspiciousness and disorganized communication were prominent in prediction of psychosis in males, whereas trouble with focus and attention was so in females. While most sex differences in patients attending an early detection service seem to reflect general differences that are not specifically related to psychosis, others might be psychosis-specific. These results can inform the development of more individualized and effective interventions for CHR patients based on more precise sex-specific prediction models.
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