Camilla Avagliano

S228. TOWARD EARLY DETECTION OF TREATMENT RESISTANT SCHIZOPHRENIA: PREDICTIVE INFORMATION ON NON-RESPONSE TO ANTIPSYCHOTICS BY EVALUATION OF A FEW CLINICAL FACTORS: A STUDY BY ROC CURVE ANALYSIS AND CONFIRMATORY MULTIVARIATE ANALYSIS

Abstract Background Treatment Resistant Schizophrenia (TRS) is associated to poor prognosis and highly disabling course. Early detection of the condition is crucial to rapidly provide targeted interventions. The aim of this study was to evaluate whether it may be possible to distinguish TRS from Antipsychotic Responder Schizophrenia (ARS) patients on the basis of a limited number of measurable clinical factors. Methods 60 out of 182 eligible patients were included. A multistep diagnostic procedure to separate TRS from ARS was then used. Clinical parameters were recorded. Rating scales were administered, including: the Neurological Evaluation Scale (NES); the Positive and Negative Syndrome Scale (PANSS); the Heinrichs’ Quality of Life Scale (QLS); the UCSD Performance-Based Skills Assessment (UPSA); the Personal and Social Performance (PSP) scale and Specific Level of Functioning (SLOF). We used the Receiver Operating Characteristic (ROC) curves analysis to distinguish between TRS and ARS. Confirmatory logistic regression and discriminant analysis were additionally used. Results Among clinical and demographic parameters, AUCs were significant for previous hospitalizations (AUC=.71; p=.004; SE= .068); antipsychotic dose (AUC=.73; p=.002; SE=.66); duration of illness (AUC=.67; p=.02; SE=.71) and NES score (AUC=.77; p<.0005; SE=.062). Moreover, significant AUCs were found for PANSS Negative subscale score (AUC=.68; p=.013; SE=.068); PANSS total score (AUC=.64; p=.05; SE=.071); QLS score (AUC=.73; p=.003; SE=.067); PSP score (AUC=.69; p=.012; SE=.68); all SLOF areas (AUC ranging from .76 to .68, p<.05), with the exclusion of Area4. A trend toward significance was found for Problem Solving (AUC=.63; p=.08). Among the whole significant variables, the highest specificity for diagnosis was found for NES score and previous hospitalizations (75% and 78.1%, respectively); the highest sensitivity for NES score (71.4%). Accordingly, Odds Ratio of being categorized as TRS were larger for NES score <21.5 (7.5), QLS score <57 (5.49), previous hospitalizations >1.45 and SLOF Area5 <43.5 (4.76 both). Multivariate analysis supported results of ROC curve analysis. Stepwise logistic regression showed that the following variables were significant predictors of TRS/ARS status: previous hospitalizations, NES score, and antipsychotic dose among clinical variables (χ(3)=27.25, p<.0005, Nagelkerke R2=.48); PANSS Negative subscale score among psychopathology variables (χ(1)=7.75, p=.005, Nagelkerke R2=.16); QLS score among quality of life variables (χ(1)=7.91, p=.005, Nagelkerke R2=.16); SLOF Area2 among social functioning variables (χ(1)=18.05, p<.0005, Nagelkerke R2=.34). The descriptive discriminant analysis function was significant for clinical variables, χ(6)=23.84, p=.001. The most relevant discriminator variables in this group were NES score, antipsychotic doses, and previous hospitalizations. Discriminant function was also significant for SLOF variables χ(6)=17.67, p=.007, with Area1 and Area3 scores ensuring the highest discriminative power. Discriminant function was only weakly significant for psychopathology and for quality of life variables (PANSS Negative subscale score and QLS score showed the highest discriminative power, respectively). Discussion Therefore, the evaluation of a few clinical factors may give solid and predictive information about patient potential to be responsive or non-responsive to antipsychotics. A patient exhibiting a combination of 2 or more lifetime hospitalizations; high NSS; high negative symptoms; low quality of life and psychosocial functioning has low possibility (less than approximately 20%, according to our data) to be responsive to antipsychotic agents.

F233. NEGATIVE SYMPTOMS ARE INDEPENDENT MODERATOR FACTORS OF TREATMENT RESISTANT SCHIZOPHRENIA EFFECTS ON MULTIPLE CLINICAL, PSYCHOPATHOLOGICAL, COGNITIVE AND PSYCHOSOCIAL VARIABLES

Abstract Background Negative symptoms (NSs) are more severe in Treatment Resistant Schizophrenia (TRS) than Antipsychotic Responder Schizophrenia (ARS) patients. NSs are predictors of outcomes of neurological soft signs and functional capacity in TRS but not in ARS patients. The scope of this work is to clarify whether NSs effects are integral to or independent from the TRS diagnosis in our sample of patients. Methods 70 out of 206 eligible putative TRS and ARS patients were included (enrollment still ongoing). Patients were tested by the Neurological Evaluation Scale (NES); the CGI-S; the PANSS; the Heinrichs’ Quality of Life Scale (QLS); the UCSD Performance-Based Skills Assessment (UPSA); the Personal and Social Performance (PSP) scale and Specific Level of Functioning (SLOF). Patients were subdivided in NSHigh (severe NSs) and NSLow (mild NSs) based on ROC curve-derived cut-off. Results At the Student’s t test, NSHigh had significantly lower scores than NSLow patients on: Verbal Fluency; QLS score; PSP score; UPSA Financial, Communication, and Family Skills; UPSA total score; all SLOF areas (except Area4). NSHigh patients had significantly higher scores than NSLow patients on CGI-S; PANSS Positive and General Psychopathology Subscale scores; and NES score. Distribution of NS patients was significantly different between TRS/ARS diagnostic groups, as NSHigh patients were significantly more frequent in the TRS group (Pearson chi square: □1=5.51, p=.001). Notably, mean PANSS Negative Subscale scores were significantly higher in TRS compared to ARS patients (Student’s t: F1,58=2.84, p=.006). Since multiple variables found to be significantly different in NSHigh vs. NSLow patients were also significantly different between TRS and ARS patients, the question arises whether the significant differences found between diagnostic groups may depend on the higher percentage of patients with more severe NSs in the TRS group. Therefore, a two-way ANOVA was carried out with dichotomous NS and Diagnosis variables as the independent variables. Outcomes on multiple clinical variables were significantly different among groups. A NS*Diagnosis interaction effect was found for NES score (F1,58=4.32, p=.042, Visuospatial Memory, UPSA Transportation skills, and SLOF Area1. In all these cases, NSHigh/TRS patients performed significantly worse than the other patient groups; in the case of NES score, NSHigh/TRS patients score significantly higher than the other groups. Independent effect of either NSs or Diagnosis were also found for multiple variables, suggesting that NSs and Diagnosis may interact but their effects are not completely overlapping. To have a more deepen comprehension of NS effects on diagnosis, we carried out a moderator regression analysis and an ANCOVA analysis that further confirmed the finding that NSs’ mediate Diagnosis effects on a number of clinical outcomes. Given that NSs largely affect clinical variables, we asked which distinct symptom may exert the greater impact on each of these variables. Therefore, we carried out a including the seven PANSS Negative Subscale items as the independent variables. The items that explained the highest variance in clinical variables were mostly Stereotyped Thinking (N7), Passive Social Withdrawal (N4), and Difficulty in Abstract Thinking (N5). Discussion These data suggest that NSs are both independent determinants and moderators of TRS/ARS diagnosis effect on multiple psychopathology, cognitive, and psychosocial factors. More impaired functions attributed to non-response to antipsychotics may depend on more severe NSs. However, only a subset of NSs appears to exert this action, possibly related to the multidimensional construct of these symptoms.

Evaluation of a few discrete clinical markers may predict categorization of actively symptomatic non-acute schizophrenia patients as treatment resistant or responders: A study by ROC curve analysis and multivariate analyses

Here, we used Receiver Operating Characteristic (ROC) curve analysis to determine whether clinical factors may aid predicting the categorization of schizophrenia patients as Treatment Resistant (TRS) or antipsychotic responsive schizophrenia (ARS). Patients with an established condition of TRS or ARS were assessed for: clinical presentation and course; neurological soft signs (NES); psychopathology by PANSS; cognitive performances; quality of life scale (QLS); functional capacity; social functioning (PSP and SLOF scales). In ROC curve analysis, significance indicated that the Area under curve (AUC) allowed distinguishing between TRS and ARS. Multivariate analyses were additionally used to provide independent predictive analysis. Multiple clinical variables showed significant AUCs. The largest significant AUCs were found for: NES total score; SLOF Area2; QLS subscale; antipsychotic doses. The highest sensitivity was found for NES total score, the highest specificity for previous hospitalizations. The highest Odds Ratio of being included within the TRS category were found for: NES total score (7.5); QLS total score (5.49); and previous hospitalizations (4.76). This same circumscribed group of variables was also found to be predictive of TRS when adopting stepwise logistic regression or discriminant analysis. We concluded that the evaluation of few clinical factors may provide reliable and accurate predictions on whether one schizophrenia patient may be categorized as a TRS.

EPA-1118 - Knowledge of the illness and its relations with quality of life, social functioning, cognitive performances, and adherence in psychotic patients: Toward effectiveness-focused interventions

Introduction Psychosocial factors are often underestimated in psychotic patients, although they may profoundly influence (and be influenced by) clinical presentation and effectiveness of therapeutic interventions in these people. Objectives To investigate relevance, relationship with clinical presentation and overall quality of life of multiple psychosocial factors in psychotic patients. Aims To evaluate whether knowledge about the illness and utilization of health services are defective in psychotic vs. non-psychotic patients and whether these correlates with the type of psychotic symptoms, cognitive performances, global social functioning, quality of life, and acceptance of pharmacotherapy. Methods Approximately 110 patients were enrolled after written informed consent. Patients were administered the Positive and Negative Syndrome Scale (PANSS), the Personal and Social Performance scale (PSP), the Drug Attitude Inventory (DAI), the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q). All patients were also screened for cognitive performances. Patients and relatives completed a questionnaire on knowledge about the illness and on the level of utilization of mental health services. Patients were subdivided in psychotic (cases) and non-psychotic (controls) based on their score on the PANSS. Results Psychotic patients and their relatives showed lower levels of knowledge about the illness. These features were associated with the other variables assessed in a very complex and multidimensional model of reciprocal influences. Conclusions Lack of response to pharmacological treatments and to overall therapeutic interventions in psychotic patients may also depend on multiple psychosocial factors, which may be carefully investigated and become the target of adjunctive, effectiveness-focused interventions.

EPA-1116 - Obesity affects cognitive performances even in the absence of obvious psychopathological alterations. a comparison with schizophrenia subjects and non-affected controls

Introduction Obesity has been associated with cognitive impairment. However, it is not clear whether cognitive impairment may depend on concomitant psychopathology, since several psychiatric conditions, e.g. schizophrenia, include cognitive deficits among their manifestations. Objectives To assess cognitive performances and psychopathology in obese patients, and to compare cognitive alterations in obese patients with those in schizophrenics and controls. Aims To compare cognitive performances in obese patients to normal percentiles. To provide an analysis of correlation with specific psychopathological domains. To evaluate whether cognitive performances in very obese patients were different from those in schizophrenia patients and non-affected controls. Methods 88 obese patients were included. Exclusion criteria were: axis I and II diagnosis; severe medical, neurological, or endocrinology conditions. Patients underwent an extensive battery of cognitive tests and completed the Toronto Alexithymia Scale (TAS-20), the Barratt Impulsiveness Scale (BIS-11), the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory (STAI). In the second part of the study, very obese patients (BMI>40; n=16) were compared for cognitive performances to schizophrenia patients (n=16) and non-affected controls (n=17). Results Obese patients performed at low percentiles ( Discussion Obese patients show cognitive alterations even in the absence of abnormal psychopathology. Very obese patients share cognitive alterations with schizophrenia patients, which may imply common neurobiological basis.