Predictors of outcome three years after diagnosis of first episode psychosis

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Psychiatry Research 161 (2008) 11 – 18 www.elsevier.com/locate/psychres

Predictors of outcome three years after diagnosis of first episode psychosis Sara Lucas a,b,⁎, Marie Antoinette Redoblado-Hodge c , Arthur Shores b , John Brennan c , Anthony Harris d,e a

c

Department of Medical Psychology, Westmead Hospital, Westmead NSW, Australia b Department of Psychology, Macquarie University, NSW, Australia Department of Child and Adolescent Psychiatry, Westmead Hospital, Westmead NSW, Australia d Department of Psychiatry, Westmead Hospital, Westmead NSW, Australia e Discipline of Psychological Medicine, University of Sydney, NSW, Australia Received 9 January 2007; received in revised form 24 August 2007; accepted 9 October 2007

Abstract This study aimed to determine which of demographic/premorbid, psychiatric or neuropsychological factors best predict functional outcome at 3 years after a first episode of psychotic illness. This will, it is hoped, identify prognostic indicators of longer term outcomes, as well as targets for rehabilitation. The Western Sydney First Episode Psychosis Project collected data on young people (aged 13 to 25) presenting with newly diagnosed psychosis at baseline and 3-year follow-up (n = 52). Outcome was measured using the Role Functioning Scale (RFS) and the Clinical Global Impression Scale — severity of illness measure (CGI-S). Multiple regression analyses were performed to identify baseline predictors of outcome. The Premorbid Social Adjustment Scale in Adolescence (PSASAdolescent) and the Verbal Comprehension Index from the WAIS-III were found to be the two significant predictors for RFS, with only the former (PSAS-Adolescent) predicting CGI-S. Demographic and neuropsychological measures relating to premorbid functioning were the best predictors of long-term outcome in first episode psychosis, with baseline psychiatric symptoms not contributing. Crown Copyright © 2007 Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Early psychosis; Longitudinal; Neurocognitive; Outcome

1. Introduction Whilst there has been much research looking at the issue of outcome in psychosis, this remains an area of continuing discussion. Much of the literature to date is

⁎ Corresponding author. Department of Medical Psychology, Westmead Hospital, Westmead NSW 2145, Australia. Tel.: +61 2 9572 9260; fax: +61 2 9635 3948. E-mail address: [email protected] (S. Lucas).

based on studies of chronic patients, many of them retrospective. Demographic/premorbid and psychiatric variables that have consistently been identified as predictors of poor outcome in the chronic population include the presence of negative symptoms; poor premorbid adjustment; male gender; younger age of onset; and longer duration of untreated psychosis (Kay and Lindenmayer, 1987; Breier et al., 1992). Green et al. (2004) reviewed 12 studies that have found neuropsychological variables to be good predictors of functional outcome with medium to large

0165-1781/$ - see front matter. Crown Copyright © 2007 Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.psychres.2007.10.004

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S. Lucas et al. / Psychiatry Research 161 (2008) 11–18

effect sizes in chronic schizophrenia (four further studies in this review found negative or mixed results on the issue). However, the actual variables identified have varied substantially from study to study, with no clear consistent variables or cognitive domains emerging across studies. Whilst many studies have examined factors influencing outcome in first episode psychosis (FEP) (Geddes et al., 1994; Lieberman, 2002; Malla and Payne, 2005), only a small number of studies have considered how various domains such as demographic/premorbid, psychiatric and neuropsychological variables directly compare in the prediction of functional outcome in first episode psychosis (FEP). Milev et al. (2005) found that negative symptoms, verbal memory and processing speed/attention were the best predictors of outcome, with only partial overlap of the variance in outcome explained by the cognitive versus psychiatric variables. Their study had the benefits of longitudinal design, 7-year follow-up period, first episode (schizophrenia) participants, good (n = 99) subject numbers and relatively little use of conventional antipsychotics in their sample. However, they did not examine the effect of demographic factors on outcome and their outcome measure appears to lack psychometric data to support its use. Grawe and Levander (2001) were one of the first to examine the relative contribution of psychiatric, neuropsychological and demographic/premorbid variables in predicting outcome, within the same analyses, albeit in a sample of only 20 subjects. Medication regime (whether patients were taking clozapine versus conventional antipsychotic) was found to be strongest predictor, followed by four neuropsychological variables (reaction time, Trail Making Test errors and speed, and Wisconsin Card Sorting Test categories correct). Keshavan et al. (2003) examined all the above factors when predicting 2-year outcome in their larger sample of patients with psychosis (n = 45), and found duration of untreated prodromal illness to be the main predictor of outcome. However, they also found impaired attention and memory at baseline to be predictive of poor outcome, independent of duration of untreated illness. These few studies have not produced any convergent results, possibly due to the small sample sizes, population bias inherent in referrals to academic clinical settings and differing predictor variables. Moreover, only two studies examined the relative contribution of demographic/ premorbid, psychiatric and neuropsychological variables in FEP. Examination of outcome in a first episode sample is important to ensure that confounds such as long term use of conventional antipsychotics and other chronicity factors do not influence the results. The strength of this study is its wide range of baseline predictor variables

(including as many of those as possible from the previous studies discussed above), its pure FEP sample (but not restricted to those with first episode schizophrenia) and the lack of use of conventional antipsychotics, as well as the use of a naturalistic sample to ensure ecologically valid results. In this sample of young people with FEP we asked: 1. Are psychiatric, neuropsychological or demographic/ premorbid variables more important in predicting functional outcome after diagnosis of FEP? 2. Which specific factors within these broader domains are the important ones? 2. Material and methods 2.1. Subjects The subset of participants included in the present study all took part in the larger Western Sydney First Episode Psychosis longitudinal project (total baseline n = 94). The catchment area for this project included approximately 950,000 people, with recruitment taking place between January 1999 and December 2000. Capture rate at baseline was 48%, with the main reasons for exclusion being refusal to take part (40%), refusing all services (9%) and uncontactable/unable to attend the appointments (26%). Those who declined to take part in the initial study were significantly older, but did not differ on gender. At baseline, entry criteria determined that participants were aged between 13 and 25, and had presented to mental health services within three months prior to referral to the study for treatment of a first episode of psychotic (not discounting comorbid affective) symptoms (although many had suffered symptoms for some months prior to presenting, as indicated by the duration of untreated psychosis in Table 1). All subjects had experienced hallucinations, delusions, prominent thought disorder or negative symptoms for at least 3 days. Subjects had been referred to community and mental health hospital services in an area of Western Sydney which could be expected to see the vast majority of presentations of young people with serious mental illness. Individuals with a history of neurological disease, head injury, recent electroconvulsive therapy (within last 6 months) or intellectual delay (IQ b 75) were excluded. For inpatients, consent was obtained and testing performed when participants were voluntary in status (as required by the Ethics Committee). This study was approved by the Western Sydney Area Health Service Human Research Ethics Committee. A full description of recruitment and methodology is reported in Harris et al. (2005).

S. Lucas et al. / Psychiatry Research 161 (2008) 11–18 Table 1 Demographic, psychiatric and neuropsychological information at study entry (baseline) Mean Demographic/premorbid variables Age (years) Education (years) a, b Premorbid IQ (WRAT-3 standard score) a, PSAS-Child a PSAS-Adolescent a, b, c Median duration of untreated psychosis (months) d Chlorpromazine equivalent b, e

b

Psychiatric variables PANSS Positive PANSS Negative a, b PANSS General b Calgary Depression Scale Neuropsychological variables WAIS-III/WISC-III Verbal Comprehension Index a, b WAIS-III/WISC-III Perceptual Organisation Index WAIS-III/WISC-III Working Memory Index a, b WAIS-III/WISC-III Processing Speed Index RCFT — Delayed recall a, b RAVLT — Total learning a, b RAVLT — Delayed recall WMS-III/CMS Logical Memory I a, b WMS-III/CMS Logical Memory II a, b WMS-III/CMS Working Memory Index a, b Trail Making Test Part A Trail Making Test Part B a, b CPT Attentiveness WCST — Categories b COWAT FAS total Outcome measures (at three years) Role Functioning Scale (RFS) Clinical Global Impression Scale — Severity (CGI-S) Social and Occupational Functioning Assessment Scale (SOFAS)

S.D.

18.7 11.0 100.2 11.0 13.3 9.2

2.9 1.9 9.6 4.8 5.7 16.4

220.4

207.8

17.3 17.7 38.6 4.8

5.7 6.6 10.0 4.6

100.3

14.9

98.7 94.0 84.4 14.5 47.5 9.4 7.7 7.9 94.2 31.0 74.3 2.7 4.8 32.8

15.2 12.4 15.3 6.6 11.2 3.5 3.5 4.0 14.1 8.9 32.4 1.1 1.5 9.1

21.4 3.1

4.7 1.6

61.1

18.3

Note. PSAS — Premorbid Social Adjustment Scale; PANSS — Positive and Negative Syndrome Scale; WAIS-III — Wechsler Adult Intelligence Scale Third Edition; WISC-III — Wechsler Intelligence Scale for Children Third Edition; RCFT — Rey Complex Figure Test; RAVLT — Rey Auditory Verbal Learning Test; WMS-III — Wechsler Memory Scale Third Edition; CMS — Children's Memory Scale; CPT — Continuous Performance Test; WCST — Wisconsin Card Sorting Test; COWAT — Controlled Oral Word Association Test. a These variables were found to correlate with RFS (P b 0.20) and thus were used as predictor variables, as described in the Statistics section. b These variables were found to correlate with CGI-S (P b 0.20) and thus were used as predictor variables, as described in Statistics section. c n = 45 data missing for 7 participants as PSAS-Adolescent was only administered if participants were not experiencing the confounding effects of prodromal period during this time. n = 52 for all other variables. d Not normally distributed. e Calculations for conversion based on Lambert et al. (2005).

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This study is based on the 52 (of the original 94) participants who consented to the 3-year follow-up phase of the study (see Table 1), giving a return rate of 56% from baseline. From the baseline sample, 35 either refused or were uncontactable, 3 had moved away and a further 4 dropped out of the follow-up stage after initially consenting to it. During the intervening years, participants received standard clinical care in the community, with most receiving medication during this time (medication was ceased in some cases where participants were considered to be in remission). DSM-IV diagnoses were established by consensus of at least three psychiatrists on the basis of all available community and inpatient information. 2.2. Measures To limit the confounding effects of acute symptomatology and conceptual disorganisation/thought disorder on the neuropsychological tests, participants underwent the following assessments when they obtained a score of less than 5 on the Conceptual Disorganisation item of the Positive and Negative Syndrome Scale (PANSS, Kay et al., 1986), rated by trained psychiatrists who had obtained acceptable inter-rater reliability (r b 0.8) on this scale. Information to assist in the completion of demographic and psychiatric ratings was obtained from either case managers, hospital records and/or family members. Three-year outcome measures included the Role Functioning Scale (RFS, Goodman et al., 1993), which is a scale assessing four areas of functioning producing a score out of 28, and the Clinical Global Impression Index — Severity of illness scale (CGI-S, Guy, 1976), which is a 7point Likert scale. The Social and Occupational Functioning Assessment Scale from DSM-IV (SOFAS) was also included at three-year follow-up. However, the RFS and SOFAS correlated extremely highly (r = 0.94), and since the RFS was used at baseline also (and a parallel paper will be looking at this stage of the study), only the RFS and CGI-S were used in analyses for this paper. Demographic/premorbid measures were chosen to include any factors that were predetermined in the sample on presentation, and therefore not able to be influenced by treatment. These included age, education, sex, premorbid IQ, duration of untreated psychosis, living situation, premorbid adjustment (Premorbid Social Adjustment Scale; PSAS-Child version ages 5–11, Adolescent version ages 12–16) (Cannon et al., 1997) and presence of a substance use disorder (SUD). Psychiatric measures included the PANSS (Positive, Negative and General Psychopathology subscales were used as separate variables), Calgary Depression Scale (CDS, Addington et al.,

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S. Lucas et al. / Psychiatry Research 161 (2008) 11–18

1990), and Young Mania Rating Scale (YMRS, Young et al., 1978). The neuropsychological battery was generally administered within 1–2 days of symptom ratings. All participants indicated that they had abstained from drugs and alcohol for 3 days prior to this assessment (this was not formally checked). Tests were administered by Master's level neuropsychologists and psychologists, or those training towards this degree (with supervision), with rest breaks as needed. An extensive array of neuropsychological tests was administered at baseline in accordance with published standardised instructions including: - Wechsler Adult Intelligence Scale — Third Edition (WAIS-III, Wechsler, 1997a) or the Wechsler Intelligence Scale for Children — Third Edition (WISC-III; Wechsler, 1991) depending on the participant's age. - Wechsler Memory Scale — Third Edition (WMS-III, Wechsler, 1997b). Logical Memory, Faces and the subtests contributing towards the Working Memory Index were administered. For those in the younger age categories, parallel subtests from the Children's Memory Scale (CMS; Cohen, 1997) were utilised. - Wide Range Achievement Test — Third Edition Reading (WRAT-3, Wilkinson, 1993). - Rey Auditory Verbal Learning Test (RAVLT, Spreen and Strauss, 1998). - Rey Complex Figure Test (RCFT; Spreen and Strauss, 1998) with 30 minute memory trial. - Trail Making Test (Spreen and Strauss, 1998). - Controlled Oral Word Association Test (COWAT; Spreen and Strauss, 1998). - Continuous Performance Test (CPT, Conners, 1995). - Wisconsin Card Sorting Test (WCST, Heaton et al., 1993). Measures were included to assess premorbid intellectual ability, current intellectual skills, attention, working memory, processing speed, verbal learning and memory, visual memory, verbal fluency and executive functions. Appropriate tests were administered depending on the age of the participant (age b 16:11 years or N17 years). Their scores were then compared to published normative data for the WAIS-III, WISC-III, WMS-III and CMS to obtain age scaled scores and Index scores for statistic analyses. All other tests were directly comparable, so raw scores were used, as indicated in Table 1. 2.3. Statistics Data were analysed using the Statistical Package for the Social Sciences Version 13 (SPSS). Normality of the

selected dependent variables (SOFAS, CGI-S, RFS) was determined through examination of residual plots from the analyses described below. Independent samples t-tests or non-parametric tests (Mann Whitney U) were performed to examine for selection bias within the sample by comparing baseline measures of those that returned for follow-up versus those who did not. All analyses employed two-tailed P-values and a 0.05 significance level, unless stated otherwise. Correlational analyses were performed with a wide range of potential predictor variables (not all listed in this paper due to space constraints) to identify independent variables that more highly correlated (P b 0.20) with the outcome variables (Hosmer and Lemeshow, 1989) (see Table 1 for the variables that met these criteria). Of those indicated, several (WMS-III Working Memory Index and Logical Memory II) were discarded due to problems of collinearity. Backward multiple regressions were performed for each of the predictor variables (i.e., RFS and CGI-S) with each of the three sets of independent variables (Demographic/ Premorbid, Psychiatric and Neuropsychological IV's). The overall variance (R2) of these models was examined. A further set of analyses was then performed, where the IV's retained from the first set of models were entered into an overall backward multiple regression for each predictor variable. This allowed comparison of the remaining IV's against each other within the same analysis. 3. Results Given the high attrition rate from baseline to followup assessment, extensive analyses were performed to identify any baseline differences between those who returned for assessment at three-year follow-up versus those who did not. Those that returned did not differ from those that returned for follow-up on any of the factors assessed including age, education, premorbid IQ (WRAT-3), home living situation, presence of a SUD, chlorpromazine equivalent, PANSS Positive, Negative and General, CDS, YMRS and various neuropsychological variables including WAIS-III/WISC-III Full Scale IQ, Processing Speed Index, RAVLT Total Learning score, WCST categories and CPT attentiveness. However, the groups did differ on PANSS Total score at baseline (t = − 2.142, df = 92, P = 0.035), with those who returned for follow-up (mean = 74.33, S.D. = 18.10) having higher levels of psychiatric symptomatology at baseline than those who did not return for follow-up (mean = 66.29, S.D. = 18.10). Demographic information for this sample at the time of baseline assessment is indicated in Table 1. All

S. Lucas et al. / Psychiatry Research 161 (2008) 11–18

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Table 2 Multiple regression analyses for each of the three domains Outcome variable RFS

CGI-S

Baseline predictors Demographic/premorbid PSAS-Adolescent Psychiatric Nil Neuropsychological WAIS-III VCI Demographic/premorbid PSAS-Adolescent Psychiatric Neuropsychological WAIS-III VCI

R2

F

0.20

10.63⁎⁎

0.05 0.16

7.06 (ns) 10.16⁎⁎

0.24

13.89⁎⁎

0 0.10

– 5.58⁎

Beta

t-value

Sig. of t

− 0.47

− 3.26

0.002

0.42

3.19

0.003

0.49

3.73

0.001

− 0.31

− 2.63

0.022

Note. ⁎P b 0.05; ⁎⁎P b 0.01, n = 52 for all analyses except the demographic/premorbid analysis, where data were excluded due to missing PSAS-A data. This analysis had n = 45. Note. RFS — Role Functioning Scale; CGI-S — Clinical Global Impressions Scale — Severity; PSAS — Premorbid Social Adjustment Scale; WAIS-III VCI — Wechsler Adult Intelligence Scale Third Edition Verbal Comprehension Index.

were living with family of some kind. Mean follow-up time period was 33 months (S.D. = 6.4) with a range from 21 to 47 months (the range of follow-up times was due to funding and administrative issues). Sixtyfour percent (n = 33) of the total (n = 52) study sample was male. At baseline, 85% of the sample was prescribed an antipsychotic medication (risperidone, n = 28; olanzapine, n = 16 and thioridazine n = 1). Seventeen percent (n = 9) were prescribed a mood stabiliser, 11% (n = 6) were taking an antidepressant, 8% (n = 4) were prescribed a benzodiazepine and 8% (n = 4) were taking anticholinergic medication. At follow-up, 60% (n = 31) of the sample were prescribed antipsychotic medication (olanzapine, n = 13, risperidone, n = 8, clozapine, n = 3, quetiapine, n = 3, amisulpride, n = 3). Mean chlorpromazine (CPZ) dosage at follow-up was 231.9 (S.D. = 270.6), with those not taking antipsychotic medication assigned a CPZ dose of zero. In addition, 19% (n = 10) were taking mood stabilisers, 11% (n = 6) were prescribed an SSRI and 6% (n = 3) were prescribed anxiolytics. Some participants were not administered the PSAS-Adolescent (PSAS-A) due to inadvertently being missed (n = 1), or because the effects of prodrome were deemed to have made the measure invalid (n = 6). Final diagnoses (as determined at three-year followup) included schizophrenia (n = 30); schizoaffective disorder (n = 6); schizophreniform (n = 2); bipolar disorder (n = 8); brief psychotic disorder (n = 1); psychosis not otherwise specified (n = 2); and substance induced psychosis (n = 3). Of those, 19 met criteria for a comorbid diagnosis of substance abuse (SUD); 2 for major depressive disorder; and 1 for an anxiety disorder. Participants all reported abstaining from drug and alcohol

use for three days prior to baseline assessments, in accordance with the study requirements (but this was not formally checked). Of the participants, 18% had a (first degree) family history of psychosis, 23% had a family history of affective disorder and 14% with some other psychiatric diagnosis. With regards to the first aim, inspection of the variance accounted for by each multiple regression analysis (see Table 2) revealed the Demographic/premorbid model to account for the most variance, followed by the neuropsychological model. The second aim sought to determine which specific factors within these broader domains are the important ones for prediction of outcome. Table 2 provides the results of the individual multiple regression analyses performed, and Table 3 shows the results of the final overall regression analysis with the best predictors from Table 2 entered as independent variables. Table 3 Final multiple regression analyses Outcome variable

Baseline predictors

R2

F

Beta

tvalue

Sig. of t

0.27 9.1⁎⁎

RFS PSASAdolescent WAIS-III VCI CGI-S PSASAdolescent

− 0.38 − 2.77 0.008 0.24 13.89⁎

0.30

2.24 0.031

0.49

3.73 0.001

Note. ⁎P b 0.05; ⁎⁎P b 0.01, n = 45 for both analyses. Note. RFS — Role Functioning Scale; CGI-S — Clinical Global Impressions Scale — Severity; PSAS — Premorbid Social Adjustment Scale; WAIS-III VCI — Wechsler Adult Intelligence Scale Third Edition Verbal Comprehension Index.

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S. Lucas et al. / Psychiatry Research 161 (2008) 11–18

Overall, the demographic variable of PSAS-Adolescent was the best predictor of 3-year outcome, for both the RFS and the CGI-S. For RFS, Verbal Comprehension Index (WAIS-III VCI) was also found to significantly predict functional outcome. 4. Discussion The findings of the present study indicate that premorbid function in adolescence and verbal intellectual skills are the best predictors of functional outcome. Demographic/premorbid factors (the included measure being PSAS-Adolescent only) accounted for the most variance in the outcome measures, followed by neuropsychological variables and then psychiatric variables. These results are somewhat consistent with previous research (Grawe and Levander, 2001; Malla et al., 2002; Keshavan et al., 2003; Milev et al., 2005; Ucok et al., 2006), who have found demographic/ premorbid variables (albeit different ones from the present study in some cases) to be the strongest predictors of longer-term outcome. They are consistent with previous reports that psychiatric symptoms (even negative symptoms at baseline) may not be as important in predicting outcome from the time of onset of illness. The findings also indicate the relevance of baseline neuropsychological findings. The finding of premorbid functioning in adolescence being the strongest predictor of outcome is consistent with previous research in both chronic and first episode patients (Kay and Lindenmayer, 1987; Breier et al., 1992; Malla et al., 2002; Ucok et al., 2006). This suggests that the person's level of premorbid adjustment during their adolescent years prior to the onset of prodrome or psychosis will have implications for their outcome after development of psychosis. This further suggests that current attempts to alter long term clinical outcome by intervening earlier in the course of the illness may be frustrated to an extent. Establishing the beginning of the illness prodrome is notoriously difficult. It may be that this result represents the effects of an illness prodrome. However, we tried to minimize this effect by not including subjects with a prodrome within this period of adolescence in this specific analysis. On the other hand, good progress in social and emotional development in adolescence prior to the onset of psychosis may act as a protective factor against poor outcome. That is, the more they have gained during this period, the more they may have to fall back on as their illness begins to affect their lives. Bentall (2003) notes that the time between childhood and settled adulthood is one that seems to involve a

number of developmental tasks including remodeling their relationship with their parents, beginning to explore sexual and emotional relationships with potential partners, and deciding on a career path. He notes that “overarching these tasks, there is a need to establish an identity” (p. 490). Confronted with the huge process of achieving these goals, young people who experience the social and cognitive deficits sometimes observed in those at high risk of psychosis are likely to be poorly equipped to rise to the challenge. This is supported by studies by Cornblatt et al. (1992), which have shown that social skills are related to cognitive deficits in both those with and those at risk of psychotic disorders. Verbal intellectual functioning (WAIS-III VCI) was also found to be a predictor of outcome using the RFS. This may also be considered as an indication of premorbid cognitive functioning, given that verbal skills like this are thought to be relatively resilient to the effects of psychosis and frequently well preserved (Bilginer et al., 2005). Thus, the implication is that higher premorbid verbal intelligence (and also perhaps further brain maturation prior to illness) is associated with better outcome in FEP. Analyses to investigate whether having a substance abuse diagnosis at baseline affected outcome failed to reveal any significant results. Those with such a diagnosis did not differ in their functional outcome from those who did not. However, these analyses did not take into account persistent drug use after initial diagnosis, and only identified those with significant drug use meeting DSM-IV criteria for an abuse disorder. This issue warrants further investigation, as a recent study also found the important variable to be continuing drug use after presentation to mental health services (Lambert et al., 2005). It was surprising that measures of psychiatric functioning at baseline did not predict functional oucome or severity of illness three years after presentation to mental health services. Previous research has found negative symptoms in particular to be related to poorer later outcome in mostly chronic samples (Kay and Lindenmayer, 1987; Breier et al., 1992; Hwu et al., 2002; Milev et al., 2005). However, generally, first episode studies have been less inclined to show this effect (Grawe and Levander, 2001; Ucok et al., 2006). This implies that the severity of symptomatology at presentation to mental health services may have no bearing on the functional outcome of the patient. An important caveat to this is the potential bias of subject drop out. The subjects who were followed up were those with a significantly greater psychopathology at baseline. This suggests that these may have continued to have

S. Lucas et al. / Psychiatry Research 161 (2008) 11–18

more severe psychopathology and have stayed in contact with services, thus being easier to trace and return to the study, and also in reducing the variance of the study group. An additional point about the treatment of these patients is the low dose of antipsychotic medication that subjects were maintained upon. Noting that only three were being treated with clozapine, it is possible that they were under-medicated as a group. However subjects had improved over the period of follow-up and the minimization of side effects helped by low dose strategies remains an important principle in local approach to treatment. The present study has several limitations. Firstly, as is the nature of research involving people with psychotic illness, this study suffered significant attrition from baseline to three-year follow-up. Whilst we could identify relatively little in the way of distinguishing factors of those who returned from those who did not, the possibility of systematic bias of the remaining sample is a concern for the generalisability of these findings. Moreover, the number of participants was low given the overall number of predictor variables to be considered. Whilst we attempted to overcome this problem by reducing the number of predictor variables and using a hierarchical approach, this may have been why the number of predictor variables was relatively low. Secondly, participants underwent the various assessments when they obtained a score of less than 5 on the Conceptual Disorganisation item of the PANSS (i.e. level of thought disorder), in order to ensure validity of the neuropsychological test data. This may have influenced the prediction of outcome using the psychiatric variables (particularly the PANSS scores), producing a ceiling effect on the level of symptomatology at time of testing. Thirdly, the overall variance accounted for by the multiple regression models produced in this study was quite low. Compared to Milev et al. (2005), our variance range of 24–27% was quite respectable, as they found up to 14.2% of variance accounted for by the predictor variables. However, Grawe and Levander (2001) found up to 76% of variance was accounted for in their small sample of 20 participants, and Keshavan et al. (2003) also achieved similarly strong results (67–73% variance). Thus, despite the wide range of variables tested as potential predictor variables, much remains in the present study that may influence outcome from FEP. Finally, no information about adherence to treatment after baseline was examined in relation to outcome. This was because the aim of this project was to only focus on predictors available at baseline, not over the ensuing three-year period. Some readers may also see the inclusion of those with affective FEP as a limitation,

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affecting the generalisability of results to other FEP populations, although the authors felt it important to include them for this very reason, given that those with affective FEP are often not examined in research. These findings have implications for prognostic indicators that can both guide rehabilitation and intervention efforts. Clinically, this is very useful as it also allows the clinician to provide the patient and their family with information about possible prognosis. In terms of intervention, it suggests that assertive monitoring and intervention is required in all young people with psychosis, and not just those with the more severe illness at presentation. However it underlines that much that determines outcome is still already established at the time that the young person arrives for treatment, as indicated by the support for the finding that higher premorbid functioning is associated with better outcome. The issues relating to premorbid adjustment during adolescence raise the possibility that social skills training and cognitive remediation may have an impact on improving outcome in these young people, especially given the growing body of evidence linking cognitive deficits, social skills and outcome. Acknowledgements Thanks are due to all members of the Western Sydney First Episode Psychosis Project for their parts in the project, and Dr Alan Taylor from Macquarie University for his assistance with statistical analyses. This research was funded in part by the Centre for Mental Health, NSW Health. References Addington, D., Addington, J., Schissel, B., 1990. A depression rating scale for schizophrenics. Schizophrenia Research 3, 247–251. Bentall, R.P., 2003. Madness Explained: Psychosis and Human Nature. Penguin Books, Melbourne. Bilginer, L., DeLuca, V., Pogge, D.L., Stokes, J.S., Harvey, P.D., 2005. Intellectual functioning in adolescents with indicators of psychosis: evidence for decline in functioning related to number of psychotic features? Journal of Neuropsychiatry and Clinical Neurosciences 17, 106–113. Breier, A., Schreiber, J.L., Dyer, J., Pickar, D., 1992. Course of illness and predictors of outcome in chronic schizophrenia: implications for pathophysiology. British Journal of Psychiatry 161, 38–43. Cannon, M., Jones, P., Gilvarry, C., Rifkin, L., McKenzie, K., Foerster, A., Murray, R., 1997. Premorbid social functioning in schizophrenia and bipolar disorder: similarities and differences. American Journal of Psychiatry 154, 1544–1550. Cohen, D., 1997. Children's Memory Scale. The Psychological Corporation, San Antonio. Conners, C.K., 1995. Conners Continuous Performance Test, 1st ed. Multi-Health Systems Inc., North Tonawanda.

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