Risk factors for relapse following treatment for first episode psychosis: A systematic review and meta-analysis of longitudinal studies

Schizophrenia Research 139 (2012) 116–128

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Risk factors for relapse following treatment for first episode psychosis: A systematic review and meta-analysis of longitudinal studies M. Alvarez-Jimenez a, b,⁎, A. Priede c, S.E. Hetrick a, b, S. Bendall a, b, E. Killackey a, b, A.G. Parker a, b, e, P.D. McGorry a, b, J.F. Gleeson d a

Centre for Youth Mental Health, The University of Melbourne, Australia Orygen Youth Health Research Centre, Australia University Hospital ‘Marques de Valdecilla’, Santander, Spain d Australian Catholic University, School of Psychology, Melbourne, Australia e Headspace Centre of Excellence, The National Youth Mental Health Foundation, Australia b c

a r t i c l e

i n f o

Article history: Received 31 October 2011 Received in revised form 4 April 2012 Accepted 5 May 2012 Available online 1 June 2012 Keywords: Relapse rates Risk factors First episode psychosis Meta-analysis

a b s t r a c t Background: Preventing relapse is an essential element of early intervention in psychosis, but relevant risk factors and precise relapse rates remain to be clarified. The aim of this study was to systematically compile and analyse risk factors for and rates of relapse in the early course of psychosis. Methods: Systematic review and meta-analysis of English and non-English language, peer-reviewed, longitudinal studies, with a minimum 12-month follow-up and at least 80% of participants diagnosed with a first episode of psychosis (FEP) that reported risk factors for relapse. Results: Of 153 potentially relevant articles, 29 were included in the study. Pooled prevalence of relapse of positive symptoms was 28% (range= 12–47%), 43% (35–54%), 54% (40–63%) at 1, 1.5–2, and 3 years follow-up, in that order. A total of 109 predictors were analysed, with 24 being assessed in at least 3 studies. Of those, 20 predictors could be extracted for meta-analysis. Medication non-adherence, persistent substance use disorder, carers' critical comments (but not overall expressed emotion) and poorer premorbid adjustment, increased the risk for relapse 4-fold, 3-fold, 2.3-fold and 2.2-fold, respectively. Conclusions: Clinical variables and general demographic variables have little impact on relapse rates. Conversely, non-adherence with medication, persistent substance use disorder, carers' criticism and poorer premorbid adjustment significantly increase the risk for relapse in FEP. Future studies need to address the methodological limitations of the extant research (e.g. definition of relapse), focus on the identification of protective factors and evaluate theoretically derived models of relapse. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The majority of first episode psychosis (FEP) patients reach clinical remission on positive psychotic symptoms in response to antipsychotic medication (Emsley et al., 2007; Cassidy et al., 2010). However, the early course of psychosis is characterised by recurrent relapses, and up to 80% of FEP patients will experience a psychotic relapse within 5 years of remission from the initial episode (Wiersma et al., 1998; Robinson et al., 1999). Each new episode significantly increases the risk of chronicity (Wiersma et al., 1998), the burden for carers, and the economic cost of treating psychosis (Almond et al., 2004). For young people relapse means further disconnection with school, work, friends and their community, adversely affecting their longterm psychosocial development (Penn et al., 2005). ⁎ Corresponding author at: Centre for Youth Mental Health, The University of Melbourne, Orygen Youth Health Research Centre, 35, Poplar Road, Parkville 3054, Victoria, Melbourne, Australia. Tel.: + 61 9342 2805, + 61 401772668(mobile). E-mail address: [email protected] (M. Alvarez-Jimenez). 0920-9964/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2012.05.007

While there is broad consensus that preventing relapse is critical in the early phase of psychosis (Alvarez-Jimenez et al., 2011b), results conflict regarding the aetiology and risk factors of relapse. A rigorous examination of the available evidence is overdue and essential both to identify patients at high risk of relapse, and to inform novel approaches to preventive interventions. The aims of this study were to undertake a systematic review and meta-analysis of risk factors for relapse in FEP and examine cumulative relapse risk in early psychosis. 2. Method 2.1. Data sources Searches to retrieve English and non-English language studies were carried out in the following databases: the Cochrane Central Register of Controlled Trials (CENTRAL), Medline, EMBASE, PsycINFO, CINAHL, UMI Proquest Digital Dissertations, Information Science Citation Index Expanded (SCI‐EXPANDED), Information Social Sciences

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Citation Index (SSCI), and Information Arts and Humanities Citation Index (A&HCI), all from inception to December 2010. Conference abstracts from ISI Science and Technology proceedings, and ISI Information Social Science and Humanities proceedings were searched. The abstracts, titles and index terms of studies were searched using combinations of relevant keywords (search terms available upon request). Additional articles were identified by hand-searching the references of retrieved articles and previous reviews. Finally, authors and other experts were contacted for unpublished studies. 2.2. Study selection Considered for inclusion were longitudinal follow-up studies examining socio-demographic, clinical, psychological, biological or treatment predictors of psychotic relapse, with at least 80% of participants diagnosed with a FEP using either DSM (APA, 1994) or ICD (WHO, 2007) criteria. Broad definitions of FEP were considered including both nonaffective (i.e. schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, and psychosis not otherwise specified) and affective psychosis (i.e. bipolar disorder, and major depressive disorder with psychotic features). Retrospective studies and studies with follow-up shorter than 12 months were excluded. Two reviewers (M.A-J. and A.P) independently assessed all potentially relevant articles for inclusion. Disagreements were resolved through discussion with the other reviewers. Relapse was defined according to the criteria employed in the individual studies; specifically, ‘as stated by the authors’ when studies used prespecified relapse criteria, or ‘as admission to hospital’ when relapse was defined as rehospitalisations due to exacerbation of psychotic symptoms. 2.3. Data extraction For each study, data were extracted on all the predictors considered for analysis. Three investigators (M.A-J, A.P, and S.E.H.) independently extracted relevant data, including participant and study characteristics, relapse rate and measurement, clinical remission criteria, and predictors examined. Standardised data extraction forms were developed a priori. Discrepancies in coding were resolved via consensus. 2.4. Assessment of methodological quality Two of the investigators (M.A-J. and A.P.) rated each study on 6 domains of methodological quality (Hackett and Anderson, 2005), including: external validity (i.e., representativeness and generalisability of the predictive model); internal validity (i.e. risk of bias of the model), statistical validity (i.e. ‘events per variable’ ratio), evaluation (i.e. quality of the model's predictions), and relapse measurement (i.e. criteria for psychotic relapse (Gleeson et al., 2010)). 2.5. Data analysis Pooled estimates of the prevalence of relapse were computed for both hospital admissions and relapse of positive symptoms and stratified by years of follow-up. The study-specific relapse rate was weighted by the inverse of its variance to compute a pooled prevalence with 95% confidence intervals (CIs) using random-effects models. Subgroup analyses were conducted according to diagnostic inclusion criteria (affective and non-affective psychosis vs. non-affective psychosis only). Heterogeneity among studies was tested using the τ2 and the I2 heterogeneity statistics (Higgins et al., 2003). The associations of risk factors with relapse were estimated by using odds ratios (ORs). Effect sizes were pooled for predictors analysed in 3 or more studies reporting data in a usable format. If effect sizes were not expressed as ORs, appropriate mathematical transformations were conducted. When conversion was not possible,

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authors were contacted for the provision of ORs. The study-specific OR was weighted by the inverse of its variance to compute pooled ORs using random-effects models. Given the considerable diversity in adjustments for potential confounders across studies, we used unadjusted data, when available, for primary analysis. Sensitivity analyses were conducted to examine the effects of the follow-up length, baseline diagnosis, recruitment strategy (incidence vs. convenience sample), treatment setting (specialised generic treatment settings), relapse measurement and study sample size on significant findings and to explain statistical heterogeneity. In addition, adjusted and unadjusted effect sizes were compared when available. Finally, data from included studies were entered into a funnel graph (a scatterplot of study effect against a measure of study size) in order to investigate the likelihood of publication bias (Egger et al., 1997). In the absence of bias, the plot should resemble a symmetrical inverted funnel. An asymmetric funnel indicates a systematic relationship between reported effects and study size (Egger et al., 1997). Pooled relapse rates were estimated with Comprehensive Meta Analysis, Version 2.2 (Biostat, Englewood, New Jersey). Studies' effect sizes were pooled using Review Manager 5.1, meta-analytic standard software employed by the Cochrane Collaboration. 3. Results eFig. 1 illustrates the study retrieval and selection strategy. The electronic search yielded 2608 citations, 133 were retrieved for eligibility assessment, and a further 20 were identified via manual reference checking, making a total of 153 assessed publications. Of these, 117 were excluded on the basis of method or sampling characteristics, leaving a total of 29 included in the study (Fig. 1). Full reference list is available upon request. Characteristics of the included studies are presented in eTable 1. Twenty-nine studies involving 3978 participants were included. Participants' mean age ranged from 21.2 to 32.2 years. Five studies included clinically remitted patients and 24 followed-up responders from acute phase treatments. Fourteen studies reported follow-up periods ranging from 12 to 18 months, 12 included follow-up periods of 2 to 3 years, and 3 included follow-ups of 5 to 7 years. With respect to the assessment of relapse, 19 studies defined relapse as a significant exacerbation of positive symptoms and 10 as rehospitalisations. Of those studies that used specified relapse criteria, 5 employed a previously proposed set of criteria and 14 used an individual definition of relapse (eTable 1). Twelve trials were conducted in Europe (N= 1550), 5 in Asia (N= 636), 8 in North America (N= 1147), and 4 in Australia (N =645). 3.1. Methodological quality The quality of the reviewed studies is summarised in eTable 2. There was variation in the external and internal validity across studies, making it difficult to assess quality. The main differences were the inclusion of affective psychosis (15 of 32 studies) vs. only nonaffective psychosis, and the recruitment strategy, with 14 studies recruiting incidence samples. In addition, 14 of the included studies reported specific details on treatments provided. Statistical methods and description of methodology and results were poor in many studies. The control variables included in the analyses varied considerably across studies, and potentially important predictors of outcome such as premorbid adjustment, diagnosis, sex, age or negative symptoms were rarely included in the multivariate models. Furthermore, most of the statistical models only described the association of predictors with relapse rather than providing predictive models (i.e. sensitivity/specificity of the model, and probability of relapses in the presence vs. absence of the risk factors). Sixteen studies did not include enough events per variable ratio for the model to be stable, and

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only 1 clearly stated that collinearity (i.e. high correlation between predictor variables) was examined. No models were externally validated on another dataset to determine the stability of predictions, or in clinical settings to establish whether their predictions were better than clinical judgement. Four studies provided the percentage of variance explained by the model (i.e. goodness of fit) and were able to explain 21% to 54% of variance of relapse. The definition and quality of relapse criteria varied substantially across studies. Ten studies defined relapse as hospital readmissions. Of those employing specific relapse criteria, 8 studies included detailed symptom severity criteria, 5 provided duration criteria for relapse and remission, 8 measured relapse at appropriate follow-up intervals (i.e. every 2 months (Gleeson et al., 2010)), and 5 undertook interreliability checks. Only 2 studies combined objective ratings with clinical judgement, and another 2 included negative symptoms or functioning measures in the assessment of relapse. 3.2. Prevalence of relapse over follow-up Figs. 1 and 2 depict the pooled prevalence of relapse of positive symptoms and hospital admission stratified by follow-up duration. Data on prevalence of relapse of positive psychotic symptoms were available from 19 studies. The pooled prevalence of relapse of positive symptoms at 1 year follow-up was 28% (range = 12–47%); 43% (34–50%) at 1.5 years follow-up, 43% (33–54%) at 2 years, and 54% (40–63%) at 3 years. There was evidence of statistical heterogeneity at all time points (I 2 = 79%, p b 0.01, I 2 = 86%, p b 0.01, I 2 = 66%, p = 0.01, and I 2 = 83%, p b 0.01, at 1, 1.5, 2 and 3 years follow-up respectively). Stratification by baseline diagnostic criteria did not account for heterogeneity across studies. Conversely, stratification by incidence vs. convenience samples eliminated heterogeneity across studies at 2 and 3 years follow-up, with studies recruiting convenience samples reporting higher relapse rates (i.e. 30% vs. 26%; 49% vs. 33%; and 62% vs. 40%, at 1, 2 and 3 years follow-up, respectively). Similarly, exploratory analysis revealed lower relapse rates in studies conducted within specialised FEP services vs. those carried out in generic treatment settings (20% vs. 29%; 34% vs. 50%; 29% vs. 43% at 1, 1.5 and 2 years follow-up, respectively), with no evidence of heterogeneity in relapse rates across studies conducted in specialised treatment settings. Data on the prevalence of hospital admissions were available for 13 studies and in 4 studies additional data were obtained from the authors (Verdoux et al., 2000; Coldham et al., 2002; Norman et al., 2005; Wood et al., 2006). The pooled prevalence of hospital admissions at 1 year follow-up was 26% (range = 12–56%); 31% (24–36%) at 1.5 years follow-up, 50% (41–52%) at 2 years, 34% (12–58%) at 3 years, and 83% (82–83%) at 7.5 years. There was evidence of statistical heterogeneity at 1 year (I2 = 90%, p b 0.01) and 3 years follow-up (I2 = 96%, p b 0.01). Exclusion of one study which included placebo treatment (Owens et al., 2010) accounted for 35% of the variance at 1 year follow-up (I2 = 55%, p = 0.06) and yielded a pooled prevalence of 21% (95% CI 14% to 30%). Conversely, one study carried out in India reported markedly low rates of hospital admission at 3 year follow-up in relation to relapse rates (13% vs. 59%, respectively) as many families preferred to keep the patients at home in the event of a psychotic relapse (Rajkumar and Thara, 1989). 3.3. Predictors of relapse Table 1 shows that 109 predictors were analysed across the studies, although only 24 (22%) were assessed in 3 or more studies. Of those, data could be extracted and pooled in meta-analysis for 10 predictors. We obtained additional or usable data from authors that enabled another 10 predictors to be pooled (Verdoux et al., 2000; Coldham et al., 2002; Norman et al., 2005; Wood et al., 2006; Wolwer et al., 2008;

Alvarez-Jimenez et al., 2011a), making a total of 20 included in the meta-analysis. 3.3.1. Index clinical variables Thirty-four clinical variables were examined, with 10 being assessed in 3 or more studies. Of those, substance use (4 of 6; i.e. a significant association in 4 out of 6 studies examining this variable) and medication non-adherence (5 of 7) showed a consistently positive association with relapse. Conversely, duration of untreated illness (DUI) (i.e. time from emergence of the first psychiatric symptom to initiation of adequate treatment) (3 of 6), duration of untreated psychosis (DUP) (i.e. time from manifestation of the first psychotic symptom to initiation of adequate treatment) (3 of 10), and affective symptoms (2 of 5) provided conflicting associations with relapse. Among the remaining variables considered, few studies showed an association between a diagnosis of non-affective psychosis (1 of 3), lower insight (1 of 4), positive psychotic symptoms (1 of 6), negative symptoms (2 of 8) and alcohol abuse (0 of 3) and relapse (Table 1). Summary ORs were estimated for 10 baseline clinical predictors (Fig. 3). There was a significant association between relapse and poorer medication adherence (7 of 7; i.e. data was pooled from 7 out of 7 studies reporting on this variable; OR = 4.09, 95% CI 2.55–6.56, p b 0.01), with no significant heterogeneity across studies (I 2 = 18%, p = 0.29). One study reported the adjusted association controlling for the effects of age, gender and diagnosis (Verdoux et al., 2000). Replacing the OR did not affect the resulting summary estimate which remained unchanged and significant (OR = 3.72, 95% CI 2.21–6.25, p b 0.01). Substance use disorder (i.e. comorbid diagnosis of substance abuse or dependence according to DSM criteria) was significantly associated with risk for relapse (6 of 6; OR= 2.27, 95% CI 1.37–3.76, p b 0.01), with no evidence of statistically significant heterogeneity (I 2 = 49%, p = 0.08) (Fig. 3). Two studies provided adjusted estimates controlling for the effects of age of onset and gender (Wade et al., 2006; Malla et al., 2008), diagnosis, DUP and medication adherence (Wade et al., 2006). Replacement of adjusted ORs did not modify the summary effect (OR = 2.12, 95% CI 1.32–3.41, p b 0.01). Conversely, positive psychotic symptoms were not significantly associated with relapse (6 of 6; OR = 1.01, 95% CI 0.99–1.03, p = 0.54), nor were negative symptoms (6 of 8; OR= 1.03, 95% CI 0.98–1.07, p = 0.26), or affective symptoms (3 of 5; OR= 1.31, 95% CI 0.73–2.35, p = 0.37). One study provided the adjusted association of positive and negative symptoms with relapse while accounting for the effects of age of onset, gender and premorbid adjustment (Norman et al., 2005). Replacing the ORs did not influence the results (OR = 1.01, 95% CI 0.99–1.03, p = 0.54, for positive symptoms; and OR= 1.03, 95% CI 0.98–1.07, p = 0.26, for negative symptoms). Evidence of significant heterogeneity was noted for the association between negative symptoms and relapse (I2 = 60%, p = 0.03). Exclusion of one study (Ucok et al., 2006) which included inpatients with schizophrenia eliminated statistical heterogeneity (I 2 = 0%, p = 0.62), while results remained unchanged. Diagnosis was not significantly associated with relapse (3 of 3; OR= 1.43, 95% CI 0.43–4.73, p =0.56) (Fig. 3). Evidence of statistical heterogeneity was noted (I2 = 77%, p =0.01). One study provided adjusted estimates controlling for the effects of age of onset, gender, DUP, premorbid adjustment and psychotic symptoms (Alvarez-Jimenez et al., 2011a). The resulting summary effect remained unchanged after replacing the OR (OR =1.21, 95% CI 0.61–2.39, p = 0.59), although statistical heterogeneity across studies was no longer significant (I2 =29%, p =0.25). There was no significant association between DUP and relapse (6 of 10; OR = 1.11, 95% CI 0.43–2.89, p = 0.82) (Fig. 3). Significant heterogeneity across studies was noted (I 2 = 100%, p b 0.01). In addition to unadjusted data, two studies provided adjusted associations between relapse and DUP controlling for the effects of age, gender, premorbid adjustment (Norman et al., 2005; Alvarez-Jimenez et al.,

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Fig. 1. Pooled prevalence of relapse of positive symptoms in first episode psychosis stratified by follow-up.

2011a), baseline psychotic symptoms and suddenness of onset (Alvarez-Jimenez et al., 2011a). Substituting the ORs did not modify the summary effect (OR = 1.03, 95% CI 0.74–1.43, p = 0.86), although heterogeneity was significantly reduced (I 2 = 48%, p = 0.09). Heterogeneity was eliminated (I 2 = 0%, p = 0.66) after excluding one study with 7.5 years follow-up which found DUP to be a significant predictor of relapse (Alvarez-Jimenez et al., 2011a) in contrast to 4 studies with non-significant findings and follow-up periods ranging from 1 to 3 years (Craig et al., 2000; Norman et al., 2005; Cougnard et al., 2006; Wood et al., 2006). There was no significant association between DUI and relapse (4 of 6; OR = 1.81, 95% CI 0.73–4.48, p = 0.20) (Fig. 3). Significant heterogeneity across studies was noted (I 2 = 80%, p b 0.01), with two studies reporting a significant association (Owens et al., 2010;

Alvarez-Jimenez et al., 2011a), and two studies finding no association (Norman et al., 2005; Wood et al., 2006). Two studies provided adjusted ORs controlling for the effects age, gender, premorbid adjustment (Norman et al., 2005; Alvarez-Jimenez et al., 2011a), baseline psychotic symptoms and suddenness of onset (Alvarez-Jimenez et al., 2011a). While adjusted individual estimates were somewhat smaller, replacement of ORs modified the resulting summary OR only marginally (OR = 1.52, 95% CI 0.64–3.60, p = 0.34). Finally, there was no statistically significant association between relapse and lower insight (4 of 4; OR = 1.46, 95% CI 0.95–2.25, p = 0.09) (Fig. 3). Insight was measured via either semi-structured clinical interviews (Wood et al., 2006; Alvarez-Jimenez et al., 2011a) or through specific insight measures (Drake et al., 2007; Saravanan et al., 2010). While statistical heterogeneity was noted across studies (I2 = 68%, p = 0.02)

Fig. 2. Pooled prevalence of hospital readmissions in first episode psychosis stratified by follow-up.

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Socio-demographic Age Older Younger Sex Female Male Marital status (never married) Education Employment Living arrangements Urbanicity Social contact/support Economic difficulties Ethnicity Parental loss Family history of psychiatric illness Expressed Emotion Critical Comments Emotional over-involvement Hostility Warmth Index clinical variables Diagnosis (non-affective vs. affective) Suddenness Acute Gradual Duration of illness DUP DUI Duration of prodromal symptoms Number of contacts before admission Type of contacts before 1st admission Prior psychiatric admission Duration of hospital admission Involuntary 1st admission Lower insight Traditional healer Psychosocial stress prior onset General psychopathology (BPRS/PANSS) Positive psychotic symptoms Disorganization/Thought disorder Negative psychotic symptoms Catatonic symptoms Clouding or stupor Perplexity Disturbed behaviour (DBR)b Extrapyramidal symptoms Affective/ depressive symptoms Special features of depression Depressive delusions and hallucinations* Self-neglect General anxiety/tension Substance use disorder (illicit substances) Heavy substance abuse Alcohol abuse Medication non-adherence Clinical global impression scale (CGI) Global assessment of functioning Premorbid variables Childhood PAS score Early Adolescence PAS score Late Adolescence PAS score Adult PAS score PAS average/total/premorbid adjustment Premorbid asociality Violent history Obstetric complications Neurotic symptoms in childhood Personality variables Neuroticism (N) Agreeableness (A) Trust, Altruism

Leff et al

Craig et al

Rund et al Novak-Grubic and Tavcar Drake et al

Saravanan et al

Wood et al

Owens et al

MacMillan et al

Holthausen et al

DeLisi et al

Stirling et al

Wolwer et al

Alvarez-Jimenez et al Norman et al

Turkington et al

Malla et al

Wade et al

Ucok et al

Cougnard et al

Apiquian-Guitart et al

Gleeson et al

Rabiner et al

Coldham et al

Chen et al

Sorbara et al

Robinson et al

Geddes et al

Rajkumar and Thara

Verdoux et al

Table 1 Variables associated with, or predictive of, psychotic relapse following a first episode of psychosis.

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Cognitive Attention CPT Wais DSSTa Motor Global neuropsychological treatment after treatment response Neurological motor soft signs Information/general verbal intelligence Battery of Memory Efficiency (BEM-84) California Verbal Learning Test Forward digit span Letter-number-span History delayed recall/logical memory Verbal learningd List of words delayed recall Delayed visual reproduction Verbal/semantic fluency Working memory/fluencyd Stroop Test WCST (% perseverative errors) Executive functioningd Impulsivityd TMT TMT-B Biological measures Homovanillic acid level Growth hormone Psychotic symptom activation to methylphenidate Neuro-imaging measures Whole-brain volume Lateral ventricular volume Caudate volume Superior temporal gyrus volume Hippocampal volume Presence of cavum septum Frontal NAA/Cr ratioc Course variables Time to treatment response Symptomatic improvement* Insight improvement* Change in lateral ventricular volume* GAF score at discharge Treatment discontinuation Interepisode residual symptoms Extrapyramidal symptoms during the first 16 wk of treatment Affective symptoms Life events Life events in parents Life events in siblings Dangerous behaviour Decreased religious activity Degree of social contacts Irregularity of follow-up Variable significant in multivariate analysis. Variable only significant in univariate analysis. Variable not significant in univariate modelling. Associated with less risk for relapse. No symbol indicates variable was no assessed. At 1-year follow-up. aWais DSST= digit symbol substitution test. bDBR = Disturbed Behaviour Rating. cNo other sprectrostopic measures (NAA/Cr ratios in the left hippocampus) were predictive relapse. dNeurocognitive dimensions.

Leff et al

Craig et al

Rund et al Novak-Grubic and Tavcar Drake et al

Saravanan et al

Wood et al

Owens et al

MacMillan et al

Holthausen et al

DeLisi et al

Stirling et al

Wolwer et al

Alvarez-Jimenez et al Norman et al

Turkington et al

Malla et al

Wade et al

Ucok et al

Cougnard et al

Apiquian-Guitart et al

Gleeson et al

Rabiner et al

Coldham et al

Chen et al

Sorbara et al

Robinson et al

Geddes et al

Rajkumar and Thara

Verdoux et al

Table 1 (continued)

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all estimates were in the same direction. Heterogeneity was explained by one study carried out in India recruiting patients with schizophrenia (Saravanan et al., 2010) in contrast to the remaining studies that included a wider range of FEP diagnosis and were undertaken in Australia (Wood et al., 2006; Alvarez-Jimenez et al., 2011a) and the UK (Drake et al., 2007). Exclusion of this study yielded a significant pooled OR of 1.85 (95% CI 1.29–2.67, p b 0.01) and eliminated heterogeneity across studies (I2 = 0%, p = 0.98). 3.3.2. Socio-demographic and family variables A total of 17 socio-demographic and family variables were assessed across studies, with 6 being examined in three or more studies. None of those showed consistent, positive associations with relapse, including

age of onset (1 of 8), sex (0 of 10), marital status (0 of 5), and employment (1 of 4). Education (2 of 6) and expressed emotion (EE) showed conflicting associations with relapse, with critical comments significantly predicting relapse in 1 of 3 studies (Table 1). Pooled analyses for the association between EE variables (measured via either the Camberwell Family Interview (CFI) or the abbreviated version of the CFI (Vaughn and Leff, 1976)) and relapse were derived from 3 of 3 studies (i.e. suitable data extracted from 3 out of 3 studies) (Fig. 4). Subgroup analysis was performed in order to examine the differential effects of critical comments (CC) vs. overall EE on risk for relapse. While overall EE was not significantly associated with increased risk of relapse (OR = 1.08, 95% CI 0.28–4.24, p = 0.91), there was a significant association between CC and relapse (OR = 2.35, 95% CI 1.16–

Fig. 3. Effects of index clinical predictors on relapse rates in first episode psychosis patients1.

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Fig. 3 (continued).

4.77, p = 0.02). There was no evidence of statistically significant heterogeneity across studies in either subgroup. Pooled ORs were estimated for the association between relapse and 5 socio-demographic variables (Fig. 4). There was no significant association between relapse and gender (7 of 10; OR = 1.42, 95% CI 0.96–2.10, p = 0.08), age of onset (7 of 8; OR= 1.00, 95% CI 0.97–1.02, p = 0.74), unmarried status (3 of 5; OR = 1.34, 95% CI 0.77–2.35, p = 0.30), lower education (5 of 6; OR= 1.20, 95% CI 0.86–1.68, p = 0.28) or unemployment (3 of 4; OR= 1.59, 95% CI 0.73–3.44, p = 0.24). There was no evidence of statistically significant heterogeneity across studies examining each predictive variable. In addition to the unadjusted data, one study provided the adjusted association between gender and relapse controlling for the effects of age of onset and substance use diagnosis (Malla et al., 2008). When the adjusted OR was fitted, the resulting summary OR for the association between relapse and gender remained unchanged and non-significant (OR = 1.33, 95% CI 0.88– 2.03, p = 0.18). 3.4. Premorbid variables and personality variables A total of 11 premorbid or personality variables were evaluated of which 4 were considered in 3 or more studies. Of those, poorer late adolescence premorbid adjustment (2 of 3) and overall premorbid adjustment (4 of 6) showed a consistent association with relapse. Conversely, childhood premorbid adjustment (1 of 3), and early adolescence premorbid adjustment (2 of 4) provided conflicting associations with relapse (Table 1). Summary estimates for the association between premorbid adjustment and relapse were derived from 5 studies, with ORs ranging from 1.05 to 8.08 (Fig. 5). Premorbid adjustment was measured by either the premorbid adjustment scale (PAS; Cannon-spoor et al., 1982) (Gleeson et al., 2005; Norman et al., 2005; Ucok et al., 2006; Alvarez-

Jimenez et al., 2011a) or through the premorbid Global Assessment of Functioning (GAF; APA, 1994)) score (Wood et al., 2006). When available, childhood and early adolescence periods were extracted to avoid a potential overlap with onset of early symptoms. Given that previous reports have raised concerns regarding the usefulness of the PAS general scale in FEP samples (van Mastrigt and Addington, 2002), subgroup analysis was conducted in order to analyse separately the effects of childhood and adolescence vs. general premorbid adjustment on relapse. Poorer premorbid adjustment (as measured through the PAS) was significantly associated with relapse (OR = 2.25, 95% CI 1.37–3.69, p b 0.01), with no evidence of either statistical heterogeneity or significant differences across subgroups. 3.5. Cognitive variables A total of 23 cognitive variables were assessed across the studies, although only 3 were measured in 3 or more studies. None of those (i.e. general verbal intelligence, logical memory, and verbal/semantic fluency) showed significant associations with relapse. Summary ORs were estimated for 3 cognitive predictors (eFigure 2). General verbal intelligence was not significantly associated with relapse (3 of 4; OR= 1.01, 95% CI 0.96–1.06, p = 0.69), nor was logical memory (4 of 4; OR = 0.99, 95% CI 0.96–1.02, p = 0.58), or verbal fluency (3 of 4; OR= 0.97, 95% CI 0.63–1.48, p = 0.88). There was no evidence of statistical heterogeneity across studies. 3.6. Biological and neuro-imaging measures While 10 biological or neuro-imaging variables were assessed, none were measured in three or more studies, making it difficult to extract relevant data or draw definite conclusions from the included studies (Table 1).

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Fig. 4. Effects of socio-demographic predictors on relapse rates in first episode psychosis patients1.

3.7. Course variables

3.8. Publication bias

Of 14 course variables assessed, only time to treatment response was evaluated across 3 studies, with significant associations found in 2 of those. Meta-analysis could not be conducted as data was not in a usable format (Table 1).

Visual inspection of funnel plots showed evidence of asymmetry for medication adherence, with the smaller studies providing larger effect sizes (Novak-Grubic and Tavcar, 2002; Ucok et al., 2006). This suggests that small studies showing no association between

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Fig. 5. Effects of premorbid adjustment variables on relapse rates in first episode psychosis patients1.

medication adherence and relapse may have not been published. Exclusion of the two smallest studies provided a smaller but significant overall estimate (OR = 2.76, 95% CI 1.70 to 4.49, p b 0.01). 4. Comment To our knowledge, this is the first systematic review and metaanalysis examining rates and predictors of relapse in FEP patients. This study showed high rates of relapse in the early course of psychosis. Pooled cumulative risk for relapse was found to be 28–54% at 1– 3 years follow-up for relapse of positive psychotic symptoms, and 26–83% at 1–7.5 years follow-up for hospital readmissions. In addition, the results of this study showed that, while a wide range of risk factors were considered across studies, only non-adherence with medication, substance use disorder, carers' critical comments (CC) and poor premorbid adjustment provided consistent and significant associations with relapse after a FEP. It was interesting that carers' CC and hostility, rather than overall expressed emotion, emerged as significant risk factors for psychotic relapse. This is consistent with growing evidence that the components of EE reflect differential constructs and are related to different variables (Alvarez-Jimenez et al., 2010a), with criticism being specifically associated with recurrent psychotic episodes (van Os et al., 2001). Further, extant research has shown high levels of criticism to be robust predictors of relapse in patients with chronic schizophrenia (Bebbington and Kuipers, 1994) possibly through autonomic hyperarousal and activation of the dorsolateral prefrontal cortex (Hooley, 2007). Conversely, social support, examined by two of the included studies, was found to be independently associated with a reduced risk for relapse (Norman et al., 2005). When taken together, these findings lend support to the stress–vulnerability model by showing that psychosocial stress can lead to relapse of psychotic symptoms in the early course of psychosis, while protective factors such as social support may buffer against environmental stress. This meta-analysis showed baseline clinical characteristics, including initial symptom presentation, were of limited value as predictors of further psychotic episodes. Conversely, a comorbid diagnosis of substance use disorder increased the risk of relapse 2.2-fold even when controlling for potential confounders (Wade et al., 2006; Malla et al., 2008; Turkington et al., 2009), or in the context of high rates of adherence to medication (Malla et al., 2008). Interestingly, the two studies that found no association between substance use disorder and psychotic relapse analysed the effects of substance use disorder diagnosed at initial presentation, rather than during the follow-up period (Wood et al., 2006; Alvarez-Jimenez et al., 2011a). Exclusion of these studies provided

an overall 3-fold increase in risk of relapse. These results indicate that persistent substance use disorder, but not necessarily at index presentation, is associated with an increased risk of relapse in FEP patients. Consistent with previous reports in patients with schizophrenia (Leucht et al., 2003), medication non-adherence was found to be associated with a 4-fold increase in risk of relapse in FEP patients. Additionally, recent evidence suggests that long-acting injectable antipsychotics are more effective than oral antipsychotics in preventing relapse in patients with schizophrenia (Gaebel et al., 2011). While these results clearly support the role of antipsychotic medications in preventing psychotic relapse, they need to be weighed against evidence that antipsychotic medication is associated with significant side effects including metabolic syndrome (Patel et al., 2009; Alvarez-Jimenez et al., 2010b) and may adversely affect vocational outcomes in FEP (Johnstone et al., 1990). Importantly, medication non-adherence should be differentiated from controlled discontinuation of antipsychotic medication. Previous trials have shown medication withdrawal with close regular clinical monitoring may not lead to worse outcomes in terms of hospital admissions and functional recovery in selected subgroups of FEP patients (Gitlin et al., 2001; Wunderink et al., 2007). The present study confirmed poorer premorbid adjustment as a predictor of relapse after FEP. This finding is unlikely to be an epiphenomenon of onset of early symptoms as we found no evidence of a consistent association between DUP, DUI and risk for relapse. This raises the possibility that premorbid social isolation or disadvantage, which has been shown to increase risk for psychosis (Selten and CantorGraae, 2005; Morgan et al., 2008), generates an ongoing vulnerability to experiencing psychotic relapses, perhaps via biased cognitive schemata (Bentall and Fernyhough, 2008) and a sensitisation of the dopaminergic system (Moutoussis et al., 2007). Alternatively, there may be a subgroup of FEP characterised by poorer early adjustment and a relapsing course (Robinson et al., 1999). Interestingly, pooled analysis showed that cognitive variables were not significantly associated with risk for relapse in FEP. This finding contrasts with previous evidence that verbal and working memory variables may be markers for early symptomatic remission in FEP patients (Bodnar et al., 2008). Further research is needed to establish the role of cognitive dimensions as potential predictors of psychotic relapse after clinical remission. 4.1. Methodological issues and future research A number of important methodological limitations in the literature were identified by this review. Firstly, relapse criteria employed by

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the studies varied greatly. Readmission to hospital was the most common definition of psychotic relapse. However, it has been argued that relapse operationalised as readmission to hospital can be influenced by either treatment factors including readmissions which result from comorbid conditions such as suicidal risk (Gleeson et al., 2010), or contextual factors, for example, treatment setting or how willing the family is to manage the patient at home (Hooley, 2007). Conversely, of those studies defining relapse as a recurrence of positive psychotic symptoms, 8 did not use any standardised or validated observer-rated instruments and the majority did not specify a duration criteria, which raises concerns regarding internal and external validity of their findings (Gleeson et al., 2010). We performed sensitivity analysis in order to determine the effect of relapse definitions on both relapse rates and factors associated with psychotic relapse. It is noteworthy that heterogeneity across individual estimates was not generally accounted for by the definition of relapse. These findings suggest that hospital admissions and recurrence of positive symptoms were generally correlated. Regardless, there is an urgent need for a standardised, universally adopted set of criteria to measure relapse in order to make further progress in this area (Gleeson et al., 2010). Interestingly, the significant heterogeneity in relapse of positive psychotic symptoms across studies was partly explained by the recruitment strategy, with studies recruiting incidence samples reporting lower relapse rates. This indicates that studies which included convenience samples may have recruited more severe patients and thus with an increased risk for relapse. Alternatively, treatment setting may account for this finding as studies carried out within specialist FEP services generally recruited incidence samples. Exploratory analysis provided support to this explanation by revealing lower relapse rates in studies conducted in the context of specialised treatment. Future studies should consider the nature of treatment provided as well as the representativeness of the sample when examining risk factors for relapse. Secondly, the findings on the relationship between EE and risk for relapse were derived from relatively old studies which recruited solely first-admitted patients with schizophrenia and provided pharmacological-based interventions. Further research should investigate the role of expressed emotion in predicting clinical outcomes in FEP samples and in the context of psychosocial interventions. Additionally, the included studies examined level of premorbid adjustment cross-sectionally in different periods of development. However, it may be that the longitudinal course of premorbid adjustment predicts psychotic relapse more accurately (Haas and Sweeney, 1992). Thirdly, predictors examined across the studies explained a relatively small proportion of variance of psychotic relapse, 11 studies provided only univariate associations, control variables varied considerably across multivariate studies, only 3 studies provided information on the sensitivity/specificity of the predictions, and no model was validated in another population. Future studies should include relevant confounders and evaluate the usefulness of the models by including the confidence intervals around risk estimates and the amount of variance explained by the model. Ultimately, clinically meaningful models should be validated both internally and externally to establish whether the predictive model is superior to unstructured clinical assessments. Finally, while several theoretical models of relapse have been proposed (Nuechterlein et al., 1994; Birchwood and Spencer, 2001; Garety et al., 2007), no study tested their validity in the early course of psychosis or investigated the interplay between environmental, biological and cognitive variables. Similarly, protective factors, either social or individual, were rarely considered by the literature. For example, as noted above, social support was found to be a protective factor against relapse (Norman et al., 2005). In addition, psychological variables including self-efficacy (Ventura et al., 2004), problem solving skills (Lukoff et al., 1984), or personality factors such as agreeableness (Gleeson et al., 2005) are likely to buffer the impact of

environmental stress, thereby reducing risk for relapse. Future research should focus on protective and modifiable factors and test theoretically derived models to both improve the prediction and prevention of relapse and to increase our understanding of its aetiological risk factors. 4.2. Limitations This study has some limitations. Firstly, studies included in this meta-analysis varied substantially in duration of follow-ups and characteristics of the participants. For example, some studies included only non-affective psychosis whereas others included both affective and non-affective psychosis. However, there was a notable consistency across study estimates for the majority of pooled risk factors and, when present, efforts (i.e. sensitivity analysis) were made to explain heterogeneity. Specifically, the significant risk factors identified by this study showed a consistent association with relapse which supports the robustness of the findings. Secondly, as noted above, the definition of relapse varied greatly across the included studies, with some studies reporting on hospital admissions while others defining relapse as exacerbation of positive psychotic symptoms. While efforts were made to identify studies which examined hospital admissions due to a psychotic exacerbation, the possibility of some readmissions being related to comorbid conditions cannot be ruled out. Thirdly, although pooled estimates did not show a significant association between baseline diagnosis (affective vs. non-affective psychosis) and psychotic relapse, it should be noted that only 3 studies provided such an analysis and they varied in the proportion of patients with affective psychosis relative to those with non-affective psychosis. Fourthly, we were not able to broadly adjust for potential covariates as studies reported univariate associations and controlled for different confounders. That said, significant findings remained unchanged when available adjusted effect sizes were pooled. Moreover, there is the possibility that reporting bias (i.e. studies selectively reporting significant associations) is operating in the literature on risk factors for relapse. Although significant efforts were made in order to minimise this issue, this limitation should be noted. Finally, as with all systematic reviews, publication bias is a potential source of error. Again, whereas attempts were made to identify unpublished studies and unpublished data was used when made available by the authors, this limitation should be considered when interpreting the present findings. 5. Summary In conclusion, based on the current available evidence, the results of this study demonstrated that medication non-adherence, persistent drug use, carers' criticism, and poorer premorbid adjustment increased the risk of relapse 4-fold, 3-fold, 2.3-fold and 2.2-fold, respectively, in FEP patients. Conversely, baseline clinical variables and general demographic variables showed little impact on relapse rates. These findings have direct clinical implications for preventing relapse during the early course of psychosis, when intervention may improve long-term outcomes. Future research should focus on the identification of protective and modifiable factors, address the significant methodological shortcomings of previous studies, and evaluate theoretically derived models which integrate the bio-psycho-social factors involved in the aetiology of relapse. Supplementary data related to this article can be found online at http://dx.doi.org/10.1016/j.schres.2012.05.007 Role of funding source This study was supported by generous funding from the Colonial Foundation to Orygen Youth Health Research Centre. Furthermore, support was provided by Marques de Valdecilla Public Foundation-Research Institute, Santander, Spain (Dr Priede). The sponsors did not participate in the design or conduct of this study; in the collection, management, analysis, or interpretation of data; in the writing of the manuscript; or

M. Alvarez-Jimenez et al. / Schizophrenia Research 139 (2012) 116–128 in the preparation, review, approval, or decision to submit this manuscript for publication. Contributors MA-J and A-P performed the literature search, extracted data from the selected articles and wrote the first draft of the manuscript. SE-H independently extracted data from the selected studies. SE-H, S-B, E-K, A-P, PD-M and JF-G contributed to the design of the study, participated in the consensus process, and critically revised the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest The authors report no additional financial or other affiliation relevant to the subject of this article. Acknowledgements The authors wish to thank investigators who provided additional information including Prof Stephen Wood, Dr Helene Verdoux, Prof Ross Norman, Prof Jean Addington, Prof Ashok Malla, Prof Wolfgang Wolwer, Dr Attila Sipos and Prof Swaran Singh.

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Further Reading Sorbara, F., Liraud, F., Assens, F., Abalan, F., Verdoux, H., 2003. Substance use and the course of early psychosis: a 2-year follow-up of first-admitted subjects. Eur. Psychiatry 18 (3), 133–136. Verdoux, H., Liraud, F., Assens, F., Abalan, F., van Os, J., 2002. Social and clinical consequences of cognitive deficits in early psychosis: a two-year follow-up study of firstadmitted patients. Schizophr. Res. 56 (1–2), 149–159.