Disorganization domain as a putative predictor of Treatment Resistant Schizophrenia (TRS) diagnosis: A machine learning approach.

BACKGROUND: Treatment Resistant Schizophrenia (TRS) is the persistence of significant symptoms despite adequate antipsychotic treatment. Although consensus guidelines are available, this condition remains often unrecognized and an average delay of 4-9 years in the initiation of clozapine, the gold standard for the pharmacological treatment of TRS, has been reported. We aimed to determine through a machine learning approach which domain of the Positive and Negative Syndrome Scale (PANSS) 5-factor model was most associated with TRS. METHODS: In a cross-sectional design, 128 schizophrenia patients were classified as TRS (n = 58) or non-TRS (n = 60) after a structured retrospective-prospective analysis of treatment response. The random forest algorithm (RF) was trained to analyze the relationship between the presence/absence of TRS and PANSS-based psychopathological factor scores (positive, negative, disorganization, excitement, and emotional distress). As a complementary strategy to identify the variables most associated with the diagnosis of TRS, we included the variables selected by the RF algorithm in a multivariate logistic regression model. RESULTS: according to the RF model, patients with higher disorganization, positive, and excitement symptom scores were more likely to be classified as TRS. The model showed an accuracy of 67.19%, a sensitivity of 62.07%, and a specificity of 71.43%, with an area under the curve (AUC) of 76.56%. The multivariate model including disorganization, positive, and excitement factors showed that disorganization was the only factor significantly associated with TRS. Therefore, the disorganization factor was the variable most consistently associated with the diagnosis of TRS in our sample.

Relationships between early age at onset of psychotic symptoms and treatment resistant schizophrenia.

AIM: Early age at schizophrenia onset (EOS) has been associated with a worse clinical course, although previous studies reported substantial heterogeneity. Despite the relevance of the subject, the relationship between the age of onset and treatment resistant schizophrenia (TRS) is less clear. METHODS: We screened 197 non-affective psychotic patients. Of these, 99 suffered from schizophrenia and were putative TRS and were included in a prospective 4-to-8-week trial to assess their response to antipsychotics. According to status (TRS/nonTRS) and age-at-onset (early: ≤18 years, EOS; adult: >18 years, adult onset schizophrenia [AOS]) patients were subdivided in EOS-TRS, EOS-nonTRS, AOS-TRS, AOS-nonTRS. Multiple clinical variables were measured and compared by analysis of covariance (ANCOVA), using age as a covariate. Two-way analysis of variance (ANOVA) was used to assess whether significant differences were attributable to TRS status or age-at-onset. RESULTS: The rate of TRS patients was significantly higher in EOS compared to AOS. At the ANCOVA, EOS-TRS had significantly worse clinical, cognitive, and psychosocial outcomes compared to the other groups. Overall, EOS-TRS were more impaired than EOS-nonTRS, while significant differences with AOS-TRS were less consistent, albeit appreciable. Two-way ANOVA demonstrated that, in the majority of the investigated variables, the significant differences among groups were attributable to the TRS status effect rather than to age-at-onset or combined effects. CONCLUSIONS: These results suggest that refractoriness to antipsychotics may be strongly linked to the early onset of psychotic symptoms, possibly as a result of common neurobiology.