Positive And Negative Syndrome Scale Data Research Articles

Correlations Between Clinical and Metabolic Variables and Smoking among Antipsychotic- Naïve First-Episode and Nonadherent Chronic Patients with Psychosis

An interaction between smoking and antipsychotic medications could potentially affect treatment efficacy and promote metabolic side effects. We investigated the contribution of smoking status towards Positive and Negative Syndrome Scale (PANSS) scores and metabolic syndrome- related parameters (plasma lipid and glucose concentrations, and body mass index) among two groups of unmedicated patients with psychosis from the Croatian population: antipsychotic-naïve first-episode patients and nonadherent chronic patients. Previous data are inconsistent regarding the effects of smoking on clinical psychopathology among antipsychotic-naïve or minimally medicated patients with first-episode psychosis, and no studies have examined the potential influence of smoking on clinical psychopathology and metabolic parameters among nonadherent patients with chronic psychosis. Information about smoking status and antipsychotic nonadherence was obtained via auto-anamnestic and hetero-anamnestic information. PANSS data were obtained while patients were in a psychotic state during the illness requiring hospitalization. Plasma total cholesterol, LDL cholesterol, HDL cholesterol (HDL- c), triglyceride, and glucose levels were determined after a 12-hour fasting period. Compared with non-smoking antipsychotic-naïve first-episode individuals, antipsychotic-naïve smokers exhibited significantly lower depression factor scores, and significantly higher triglyceride levels and triglyceride/HDL- c ratio (p < 0.05). Compared with non-smoking nonadherent chronic individuals, nonadherent smokers exhibited significantly lower negative symptoms and negative factor scores, and lower HDL- c levels. Contributions of smoking to clinical and metabolic parameters ranged from ~ 3.4 % to 10 %. Our present results indicated that smoking may be associated with less severe clinical psychopathology, and with increased risk for metabolic abnormalities, among unmedicated patients with first-episode psychosis and chronic psychosis.

11.1 Toward an Optimized Version of the Positive and Negative Syndrome Scale (PANSS) for Pediatric Trials

11.1 Toward an Optimized Version of the Positive and Negative Syndrome Scale (PANSS) for Pediatric Trials

Antipsychotics for schizophrenia spectrum disorders with catatonic symptoms.

Antipsychotics for schizophrenia spectrum disorders with catatonic symptoms.

Scalability of the Positive and Negative Syndrome Scale in first-episode schizophrenia assessed by Rasch models.

ObjectiveHistorically, assessment of the psychometric properties of the Positive and Negative Syndrome Scale (PANSS) has had several foci: (1) calculation of reliability indexes, (2) extraction of subdimensions from the scale, and (3) assessment of the validity of the total score. In this study, we aimed to examine the scalability and to assess the clinical performance of the 30‐item PANSS total score as well as the scalability of a shorter version (PANSS‐6) of the scale.MethodsA composite data set of 1073 patients with first‐episode schizophrenia or schizophrenia spectrum disorder was subjected to Rasch analysis of PANSS data from baseline and 4–6 weeks follow‐up.ResultsThe central tests of fit of the Rasch model failed to satisfy the statistical requirements behind item homogeneity for the PANSS‐30 as well as the PANSS‐6 total score. For the PANSS‐30, Differential Item Functioning was pronounced both for the 7‐point Likert scale rating categories and when dichotomizing the rating categories. Subsequently, the Rasch structure analysis in the context of dichotomized items was used to isolate and estimate a systematic error because of item inhomogeneity, as well as a random error. The size of the combined sources of error for the PANSS‐30 total score approximated 20% which is often regarded as clinical cut‐off between response versus no‐response.ConclusionThe results demonstrate the operational consequences of a lack of statistical fit of the Rasch model and suggest that the calculated measure of uncertainty needs to be considered when using the PANSS‐30 total score.

Large-scale evaluation of the Positive and Negative Syndrome Scale (PANSS) symptom architecture in schizophrenia.

Although the Positive and Negative Syndrome Scale (PANSS) is widely utilized in schizophrenia research, variability in specific item loading exist, hindering reproducibility and generalizability of findings across schizophrenia samples. We aim to establish a common PANSS factor structure from a large multi-ethnic sample and validate it against a meta-analysis of existing PANSS models. Schizophrenia participants (N = 3511) included in the current study were part of the Singapore Translational and Clinical Research Program (STCRP) and the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE). Exploratory Factor Analysis (EFA) was conducted to identify the factor structure of PANSS and validated with a meta-analysis (N = 16,171) of existing PANSS models. Temporal stability of the PANSS model and generalizability to individuals at ultra-high risk (UHR) of psychosis were evaluated. A five-factor solution best fit the PANSS data. These were the i) Positive, ii) Negative, iii) Cognitive/disorganization, iv) Depression/anxiety and v) Hostility factors. Convergence of PANSS symptom architecture between EFA model and meta-analysis was observed. Modest longitudinal reliability was observed. The schizophrenia derived PANSS factor model fit the UHR population, but not vice versa. We found that two other domains, Social Amotivation (SA) and Diminished Expression (DE), were nested within the negative symptoms factor. Here, we report one of the largest transethnic factorial structures of PANSS symptom domains (N = 19,682). Evidence reported here serves as crucial consolidation of a common PANSS structure that could aid in furthering our understanding of schizophrenia.

Audio-digital recordings to assess ratings reliability in clinical trials of schizophrenia

We examined ratings reliability in 5 clinical trials of subjects with schizophrenia experiencing an acute exacerbation of psychosis. Audio-digital recordings of site-based interviews of the Positive and Negative Syndrome Scale (PANSS) or Brief Psychiatric Rating Scale (BPRS) were used to obtain blinded, site-independent scores to evaluate paired scoring concordance.High intraclass correlations were noted between 1810 paired site-based and site-independent PANSS scores (r = 0.801) and 1837 paired BPRS scores (r = 0.897) with high limits of agreement such that 93.9% of paired scores were within the calculated 95% confidence intervals. In 2 studies where sufficient PANSS data was available at baseline and endpoint, blinded site-independent ratings yielded a predictive value of 84.2% for replicating site-based response/nonresponse treatment outcomes.There was a significant positive correlation between site-based scores and paired scoring deviations (PANSS: r = 0.246; p < 0.0001; BPRS: r = 0.176; p < 0.0001). The magnitude (symptom severity) of PANSS or BPRS scores affected the directionality of paired scoring deviations in each study. Site-based raters scored the most symptomatic subjects higher and less symptomatic subjects lower than the paired site-independent raters on either instrument.This analysis affirms the utility of paired audio-digital scoring of site-based interviews as a surveillance strategy for schizophrenia studies. We noted a high predictive value of blinded site-independent raters to replicate site-based treatment outcomes. The bi-directionality of paired scoring deviations observed for both the PANSS and BPRS is consistent with findings found for depression rating instruments.

S49. EFFICACY OF HIGH-FREQUENCY REPETITIVE TRANSCRANIAL MAGNETIC STIMULATION ON PANSS FACTORS IN SCHIZOPHRENIA WITH PREDOMINANT NEGATIVE SYMPTOMS – RESULTS FROM AN EXPLORATORY RE-ANALYSIS

BackgroundRepetitive transcranial magnetic stimulation (rTMS) applied to the left frontal lobe is discussed to be a promising add-on treatment for negative symptoms in schizophrenia. The Positive and Negative Syndrome Scale (PANSS) has been used as outcome parameter in several previous rTMS trials, but studies focusing on PANSS factor analyses are lacking.For this purpose, we used the available PANSS data of the ‘rTMS for the Treatment of Negative Symptoms in Schizophrenia’ (RESIS) trial to calculate different literature-based PANSS factors and to re-evaluate the impact of rTMS on negative symptoms in this trial.MethodsIn an exploratory re-analysis of published data from the RESIS study (Wobrock et al. 2015), we tested the impact of rTMS applied to the left dorsolateral prefrontal cortex on two PANSS factors for negative symptoms in psychotic disorders as well as on a PANSS five-factor consensus model intending to show that active rTMS treatment improves PANSS negative symptom subscores.ResultsIn accordance to the original analysis, all PANSS factors showed an improvement over time in the active and, to a considerable extent, also in the sham rTMS group. However, comparing the data before and directly after the rTMS intervention, the PANSS excitement factor improved in the active rTMS group significantly more than in the sham group, but this finding did not persist if follow-up data were taken into account. These additional analyses extend the previously reported RESIS trial results showing unspecific improvements in the PANSS positive subscale in the active rTMS group.Our PANSS factor-based approach to investigate the impact of prefrontal rTMS on different negative symptom domains confirmed no overall beneficial effect of the active compared to sham rTMS.DiscussionThis secondary analysis of the RESIS trials has several limitations. First of all, the analysis of the primary endpoint was negative [24] and all subsequent secondary analyses showing a positive effect of the intervention (here: change in PANSS excitement factor) are of limited statistical power and therefore subject to uncertainty. On the other hand, our analyses confirm the negative finding of the original publication extends this finding to a broader negative symptom definition. Moreover, the new analysis provides a possible, but hypothetical explanation for the previously described effect of active rTMS on PANSS positive subscale. Of course, many other PANSS factor models are available and in pharmacological research the Marder factors [23, 35] have particular significance. However, the here used five-factor consensus model [21] includes the Marder factor results and our negative symptom factors overlaps with those factors. Another limitation is that it may be possible that our sham stimulation (coil tilted over one wing at an angle of 45°[24]) may still have been slightly biologically active as discussed elsewhere [24].

Efficacy of high-frequency repetitive transcranial magnetic stimulation on PANSS factors in schizophrenia with predominant negative symptoms – Results from an exploratory re-analysis

Repetitive transcranial magnetic stimulation (rTMS) applied to the left frontal lobe is discussed to be a promising add-on treatment for negative symptoms in schizophrenia. The Positive and Negative Syndrome Scale (PANSS) has been used as outcome parameter in several previous rTMS trials, but studies focusing on PANSS factor analyses are lacking. For this purpose, we used the available PANSS data of the ‘rTMS for the Treatment of Negative Symptoms in Schizophrenia’ (RESIS) trial to calculate different literature-based PANSS factors and to re-evaluate the impact of rTMS on negative symptoms in this trial. In an exploratory re-analysis of published data from the RESIS study (Wobrock et al. 2015), we tested the impact of rTMS applied to the left dorsolateral prefrontal cortex on two PANSS factors for negative symptoms in psychotic disorders as well as on a PANSS five-factor consensus model intending to show that active rTMS treatment improves PANSS negative symptom subscores. In accordance to the original analysis, all PANSS factors showed an improvement over time in the active and, to a considerable extent, also in the sham rTMS group. However, comparing the data before and directly after the rTMS intervention, the PANSS excitement factor improved in the active rTMS group significantly more than in the sham group, but this finding did not persist if follow-up data were taken into account. These additional analyses extend the previously reported RESIS trial results showing unspecific improvements in the PANSS positive subscale in the active rTMS group. Our PANSS factor-based approach to investigate the impact of prefrontal rTMS on different negative symptom domains confirmed no overall beneficial effect of the active compared to sham rTMS.

Transformed PANSS Factors Intended to Reduce Pseudospecificity Among Symptom Domains and Enhance Understanding of Symptom Change in Antipsychotic-Treated Patients With Schizophrenia.

Positive and Negative Syndrome Scale (PANSS) total score is the standard primary efficacy measure in acute treatment studies of schizophrenia. However, PANSS factors that have been derived from factor analytic approaches over the past several decades have uncertain clinical and regulatory status as they are, to varying degrees, intercorrelated. As a consequence of cross-factor correlations, the apparent improvement in key clinical domains (eg, negative symptoms, disorganized thinking/behavior) may largely be attributable to improvement in a related clinical domain, such as positive symptoms, a problem often referred to as pseudospecificity. Here, we analyzed correlations among PANSS items, at baseline and change post-baseline, in a pooled sample of 5 placebo-controlled clinical trials (N = 1710 patients), using clustering and factor analysis to identify an uncorrelated PANSS score matrix (UPSM) that minimized the degree of correlation between each resulting transformed PANSS factor. The transformed PANSS factors corresponded well with discrete symptom domains described by prior factor analyses, but between-factor change-scores correlations were markedly lower. We then used the UPSM to transform PANSS in data from 4657 unique schizophrenia patients included in 12 additional lurasidone clinical trials. The results confirmed that transformed PANSS factors retained a high degree of specificity, thus validating that low between-factor correlations are a reliable property of the USPM when transforming PANSS data from a variety of clinical trial data sets. These results provide a more robust understanding of the structure of symptom change in schizophrenia and suggest a means to evaluate the specificity of antipsychotic treatment effects.

Item Response Theory to Quantify Longitudinal Placebo and Paliperidone Effects on PANSS Scores in Schizophrenia.

As biomarkers are lacking, multi‐item questionnaire‐based tools like the Positive and Negative Syndrome Scale (PANSS) are used to quantify disease severity in schizophrenia. Analyzing composite PANSS scores as continuous data discards information and violates the numerical nature of the scale. Here a longitudinal analysis based on Item Response Theory is presented using PANSS data from phase III clinical trials. Latent disease severity variables were derived from item‐level data on the positive, negative, and general PANSS subscales each. On all subscales, the time course of placebo responses were best described with Weibull models, and dose‐independent functions with exponential models to describe the onset of the full effect were used to describe paliperidone's effect. Placebo and drug effect were most pronounced on the positive subscale. The final model successfully describes the time course of treatment effects on the individual PANSS item‐levels, on all PANSS subscale levels, and on the total score level.

Attacking Heterogeneity in Schizophrenia by Deriving Clinical Subgroups From Widely Available Symptom Data.

Previous research has identified (1) a "deficit" subtype of schizophrenia characterized by enduring negative symptoms and diminished emotionality and (2) a "distress" subtype associated with high emotionality-including anxiety, depression, and stress sensitivity. Individuals in deficit and distress categories differ sharply in development, clinical course and behavior, and show distinct biological markers, perhaps signaling different etiologies. We tested whether deficit and distress subtypes would emerge from a simple but novel data-driven subgrouping analysis, based on Positive and Negative Syndrome Scale (PANSS) negative and distress symptom dimensions, and whether subgrouping was informative regarding other facets of behavior and brain function. PANSS data, and other assessments, were available for 549 people with schizophrenia diagnoses. Negative and distress symptom composite scores were used as indicators in 2-step cluster analyses, which divided the sample into low symptom (n = 301), distress (n = 121), and deficit (n = 127) subgroups. Relative to the low-symptom group, the deficit and distress subgroups had comparably higher total PANSS symptoms (Ps < .001) and were similarly functionally impaired (eg, global functioning [GAF] Ps < .001), but showed markedly different patterns on symptom, cognitive and personality variables, among others. Initial analyses of functional magnetic resonance imaging (fMRI) data from a 182-participant subset of the full sample also suggested distinct patterns of neural recruitment during working memory. The field seeks more neuroscience-based systems for classifying psychiatric conditions, but these are inescapably behavioral disorders. More effective parsing of clinical and behavioral traits could identify homogeneous target groups for further neural system and molecular studies, helping to integrate clinical and neuroscience approaches.

A comparison of the PANSS Pentagonal and Van Der Gaag 5-factor models for assessing change over time

The Positive and Negative Syndrome Scale (PANSS) Pentagonal (PM) and Van der Gaag (VDG) 5-factor models were compared on a range of change-over-time statistical indicators. PANSS data from a randomized, controlled trial for 108 adults diagnosed with schizophrenia were re-analyzed to calculate five factor scores for each model. Random effects regression was used to determine their relative performance in modeling change-over-time, determining covariance structure, and achieving goodness of fit. Performance of the 10 factors in estimating change-over-time was similar, as were significant covariates for the factor dyads: age, gender, chronicity/acuity, and anticholinergic medication. The PM model factors demonstrated the best “goodness of model fit” to the data on all five of the factor dyads. Correlation analyses indicated significantly high, positive correlations between the five factor dyads: Positive and Negative factors for the PM and VDG models, the PM Activation and VDG Excited factors, the PM Dysphoric Mood and the VDG Emotional Distress factors, and the PM Autistic Preoccupation and the VDG Disorganized factors, although the PANSS items on each factor differed somewhat. These results extend previous findings and indicate several important performance differences between the PM and VDG models in their parsimonious representation of the syndromes of schizophrenia and estimating change-over-time.

Is there an optimal factor structure of the Positive and Negative Syndrome Scale in patients with first‐episode psychosis?

The Positive and Negative Syndrome Scale (PANSS) is the most widely used scale to assess a variety of symptoms in patients with schizophrenia and other psychoses. The factor structure of the PANSS has been examined with confirmatory factor analyses in several studies, but not in a well-defined first-episode psychosis sample. The aim of this paper is to examine the statistical fit of five different PANSS models in a first-episode, non-affective psychosis sample. Confirmatory factor analyses were performed on PANSS data (n = 588). A main criterion for best fit was defined as the Expected Cross Validation Index (ECVI). No tested model revealed an optimally satisfactory model fit index. The Wallwork/Fortgang five-factor model demonstrated the most optimal psychometric properties. The corresponding subscales of all evaluated five-factor models were strongly intercorrelated. The Wallwork/Fortgang five-factor model was found to be statistically and clinically ideal among patients with first-episode psychosis. Therefore, we recommend this model in forthcoming studies among patients with first-episode psychosis. However, to prevent the loss of clinically valuable information on an item level, we do not recommend removing any items from the original form. Our study also implies that the specific choice of model will not have a substantial effect on outcome results in studies on the course and outcome in first-episode psychosis.

Modelling and Simulation of the Positive and Negative Syndrome Scale (PANSS) Time Course and Dropout Hazard in Placebo Arms of Schizophrenia Clinical Trials

The likelihood of detecting a therapeutic signal of an effective drug for schizophrenia is impeded by a high placebo effect and by high dropout of patients. Several unsuccessful trials of schizophrenia, at least partly due to highly variable placebo effects, have indicated the necessity for a robust methodology to evaluate such a placebo effect and reasons for dropout. Hence, the objectives of this analysis were to (i) develop a longitudinal placebo model that accounts for dropouts and predictors of the placebo effect, using the Positive and Negative Syndrome Scale (PANSS) score; (ii) compare the performance of empirical and semi-mechanistic placebo models; and (iii) compare different time-to-event (TTE) dropout modelling approaches used to account for dropouts. The PANSS scores from 1436 individual patients were used to develop and validate a placebo model. This pooled dataset included 16 trials (conducted between 1989 and 2009), with different study durations, in both acute and chronic schizophrenic patients. A nonlinear mixed-effects modelling approach was employed, using NONMEM VII software. Among the different tested placebo models, the Weibull model and the indirect response model adequately described the PANSS data. Covariate analysis showed that the disease condition, study duration, study year, geographic region where the trial was conducted, and route of administration were important predictors for the placebo effect. All three parametric TTE dropout models, namely the exponential, Weibull and Gompertz models, described the probability of patients dropping out from a clinical trial equally well. The study duration and trial phase were found to be predictors for high dropout rates. Results of joint modelling of the placebo effect and dropouts indicated that the probability of patients dropping out is associated with an observed high PANSS score. The indirect response model was found to be a slightly better model than the Weibull placebo model to describe the time course of the PANSS score. Our modelling approach was shown to adequately simulate the longitudinal PANSS data and the dropout trends after placebo treatment. Data analyses suggest that the Weibull and indirect response models are more robust than other placebo models to describe the nonlinear trends in the PANSS score. The developed placebo models, accounting for dropouts and predictors of the placebo effect, could be a useful tool in the evaluation of new trial designs and for better quantification of antipsychotic drug effects.

Searching for a consensus five-factor model of the Positive and Negative Syndrome Scale for schizophrenia

Although the developers of the Positive and Negative Syndrome Scale (PANSS) grouped items into three subscales, factor analyses indicate that a five-factor model better characterizes PANSS data. However, lack of consensus on which model to use limits the comparability of PANSS variables across studies. We counted “votes” from published factor analyses to derive consensus models. One of these combined superior fit in our Caucasian sample (n=458, CFI=.970), and in distinct Japanese sample (n=164, CFI=.964), relative to the original three-subscale model, with a sorting of items into factors that was highly consistent across the studies reviewed.

Patient subgroups of schizophrenia based on the Positive and Negative Syndrome Scale: composition and transition between acute and subsided disease states

The present study focuses on schizophrenia patient subgroups with specific symptom pattern using the Positive and Negative Syndrome Scale (PANSS). In this report, we intend to (1) provide a more appropriate analytic method for exploring the subgroups based on PANSS data, (2) validate identified subgroups with external variables, and (3) estimate probabilities of subgroup changes between 2 disease states. The analyzed data include 219 acute-state patients who had completed the PANSS within 1 week of index admission and 225 subsided-state patients who were living in the community and under family care. Regression extension of latent class analysis was performed. We found that acute schizophrenia can be classified into 4 subgroups—whole syndrome, whole syndrome without hostility, partial syndrome with negative symptoms, and partial syndrome with pure reality distortion—and that subsided schizophrenia can be classified into 3 subgroups—florid symptom, marked negative, and remitted. Patients of the whole syndrome, whole syndrome without hostility, partial syndrome with negative symptoms, and partial syndrome with pure reality distortion subgroups at the acute state were most likely to transit to the florid symptom (61%), florid symptom (48%), marked negative (42%), and remitted (56%) subgroups at the subsided state, respectively. Significant relationships of obtained subgroups with sociodemographic variables and neurocognitive variables were identified. These results of different subgroups will provide the background for facilitating current molecular, genetic, and neurobiological studies of schizophrenia.

Revisiting the 5 Dimensions of the Positive and Negative Syndrome Scale

The Positive and Negative Syndrome Scale (PANSS) is a widely used instrument to measure severe psychopathology in schizophrenia. Recently, its widely accepted 5-factor solution is questioned using confirmatory factor analysis (CFA). This article examines the appropriateness of applying CFA to PANSS data and the dimensionality of the PANSS in a large sample of persons with chronic schizophrenia, schizophreniform disorder, and schizoaffective disorder. Data reduction was conducted on PANSS baseline assessments from 2 multicenter clinical trials consisting of 1284 persons' available PANSS data at baseline and Week 8. Supplementary analyses were conducted for separate trials and for noncompleters at baseline (n = 1872). Examination of the data indicated that the statistical requirements of CFA (eg, multivariate normality) were not met by the data. Multidimensional scaling and a scree plot of principal components superimposed on simulated random data converged to indicate that 5 dimensions are generally a parsimonious fit to the PANSS at baseline, whereas 4 dimensions emerged after 8 weeks of treatment. The components included negative symptoms, positive symptoms, disorganized (baseline only), anxiety/depression, and excited. The results demonstrate realistic alternatives to CFA when studying the PANSS, support a 5-component model of PANSS ratings at baseline, and indicate that the structure of symptoms change after 8 weeks of treatment. These results seem to be robust because they generally replicate within trials for completers and at baseline for the entire sample.

The relationship between insight and symptoms in schizophrenia

We examined the relationship between insight and the positive, negative, active, dysphoric, and autistic dimensions of symptoms in patients with a diagnosis of schizophrenia. Ninety-six patients with a diagnosis of schizophrenia were assessed using the Scale to Assess Unawareness of Mental Disorder, Revised Version (SUMD-R) and the Positive and Negative Syndrome Scale (PANSS). The PANSS data were analyzed based on a five-factor model defined by White et al (1997). The percentage of patients having a lack of awareness was 32.7% for illness, 58.2% for symptoms, 18.4% for treatment response, and 41.8% for social consequences. Lack of awareness of symptoms was significantly correlated with all five symptom factors. Lack of awareness of the illness and its social consequences was only correlated with the positive dimension. Lack of awareness of achieved effects of medication was correlated with the autistic preoccupation factor. There was no correlation between current misattributions for symptoms and PANSS factors. We conclude that poor insight is a common feature of schizophrenia and has a complex relationship to other symptoms of the illness. Our results suggest that (1) unawareness of symptoms is related to severity of illness; (2) insight into illness and its social consequences is more closely tied to positive symptoms than other aspects of insight; and (3) insight into the effects of medication is more closely related to cognitive impairment. Treatment studies that measure insight could answer the question of whether these deficits in awareness improve along with positive and cognitive symptoms.

P.2.162 Comparison of quetiapine, olanzapine and risperidone in patients with schizophrenia: Interim results of a randomised, rater-blinded study

Background: We sought to develop and validate an excitement subscale from the Positive and Negative Syndrome Scale (PANSS) to allow the investigation of mania-like excitement symptoms in clinical trials of patients with schizophrenia using the PANSS and to provide clinicians with a short assessment tool for these states. Methods: Baseline PANSS data from six double-blind, randomized registration trials of olanzapine, three in schizophrenia and three in acute bipolar mania, were used in these post-hoc analyses. Schizophrenia study data were pooled and randomly split in half. Exploratory principal component factor analysis was performed on half of the data. Factors were extracted based on minimum eigenvalue criteria (eigenvalue≥1); loadings were determined using an equamax rotation. Confirmatory principal component factor analysis was performed on the other half of the data, retaining the original number of factors. Principal component factor analysis was also done for the pooled bipolar studies. Change in the new mania-like factor scores was then correlated with Young Mania Rating Scale (Y-MRS) scores in each bipolar study. Results: Exploratory principal components analysis on the pooled schizophrenia data extracted five factors: negative, positive, excitement, cognitive, and depressive factors. The mania-like excitement factor was represented by four items (uncooperativeness, poor impulse control, excitement, and hostility), with only moderate loadings by tension and suspiciousness/persecution. Results were similar in the confirmatory analysis and the pooled bipolar studies. Change from baseline to endpoint for the mania-like factor correlated reasonably well (0.64–0.78) with change in Y-MRS scores in the bipolar studies. At baseline, bipolar patients scored higher than patients with schizophrenia on three of four PANSS mania-like factor items: poor impulse control, excitement, and hostility; the converse was true for most other PANSS items. Conclusion: Factor analyses of the PANSS consistently uncovered an excitement factor including uncooperativeness, poor impulse control, excitement, and hostility items. This factor may be useful in examining manic symptoms in studies where the addition of a scale specific to mania would be burdensome and where symptoms of excitement are part of the clinical presentation.

An excitement subscale of the Positive and Negative Syndrome Scale

Background: We sought to develop and validate an excitement subscale from the Positive and Negative Syndrome Scale (PANSS) to allow the investigation of mania-like excitement symptoms in clinical trials of patients with schizophrenia using the PANSS and to provide clinicians with a short assessment tool for these states. Methods: Baseline PANSS data from six double-blind, randomized registration trials of olanzapine, three in schizophrenia and three in acute bipolar mania, were used in these post-hoc analyses. Schizophrenia study data were pooled and randomly split in half. Exploratory principal component factor analysis was performed on half of the data. Factors were extracted based on minimum eigenvalue criteria (eigenvalue≥1); loadings were determined using an equamax rotation. Confirmatory principal component factor analysis was performed on the other half of the data, retaining the original number of factors. Principal component factor analysis was also done for the pooled bipolar studies. Change in the new mania-like factor scores was then correlated with Young Mania Rating Scale (Y-MRS) scores in each bipolar study. Results: Exploratory principal components analysis on the pooled schizophrenia data extracted five factors: negative, positive, excitement, cognitive, and depressive factors. The mania-like excitement factor was represented by four items (uncooperativeness, poor impulse control, excitement, and hostility), with only moderate loadings by tension and suspiciousness/persecution. Results were similar in the confirmatory analysis and the pooled bipolar studies. Change from baseline to endpoint for the mania-like factor correlated reasonably well (0.64–0.78) with change in Y-MRS scores in the bipolar studies. At baseline, bipolar patients scored higher than patients with schizophrenia on three of four PANSS mania-like factor items: poor impulse control, excitement, and hostility; the converse was true for most other PANSS items. Conclusion: Factor analyses of the PANSS consistently uncovered an excitement factor including uncooperativeness, poor impulse control, excitement, and hostility items. This factor may be useful in examining manic symptoms in studies where the addition of a scale specific to mania would be burdensome and where symptoms of excitement are part of the clinical presentation.