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BackgroundSubstance use may increase the risk of non-adherence to antipsychotics, resulting in negative outcomes in patients with psychosis.MethodWe aimed to quantitatively summarize evidence regarding the effect of cannabis use, the most commonly used illicit drug amongst those with psychosis, on adherence to antipsychotic medication. Studies were identified through a systematic database search. Adopting random-effects models, pooled odds ratios (OR) for risk of non-adherence to antipsychotic medications were calculated comparing: cannabis-users at baseline v. non-users at baseline; non users v. continued cannabis users at follow-up; non-users v. former users at follow-up; former users v. current users.ResultsFifteen observational studies (n = 3678) were included. Increased risk of non-adherence was observed for cannabis users compared to non-users (OR 2.46, n = 3055). At follow-up, increased risk of non-adherence was observed for current users compared to non-users (OR 5.79, n = 175) and former users (OR 5.5, n = 192), while there was no difference between former users and non-users (OR 1.12, n = 187).ConclusionsCannabis use increases the risk of non-adherence and quitting cannabis use may help adherence to antipsychotics. Thus, cannabis use may represent a potential target for intervention to improve medication adherence in those with psychosis. [ABSTRACT FROM AUTHOR]

BackgroundThere is evidence that a range of socio-environmental exposures is associated with an increased risk of psychosis. However, despite the fact that such factors probably combine in complex ways to increase risk, the majority of studies have tended to consider each exposure separately. In light of this, we sought to extend previous analyses of data from the AESOP (Aetiology and Ethnicity in Schizophrenia and Other Psychoses) study on childhood and adult markers of disadvantage to examine how they combine to increase risk of psychosis, testing both mediation (path) models and synergistic effects.MethodAll patients with a first episode of psychosis who made contact with psychiatric services in defined catchment areas in London and Nottingham, UK (n = 390) and a series of community controls (n = 391) were included in the AESOP study. Data relating to clinical and social variables, including parental separation and loss, education and adult disadvantage, were collected from cases and controls.ResultsThere was evidence that the effect of separation from, but not death of, a parent in childhood on risk of psychosis was partially mediated through subsequent poor educational attainment (no qualifications), adult social disadvantage and, to a lesser degree, low self-esteem. In addition, there was strong evidence that separation from, but not death of, a parent combined synergistically with subsequent disadvantage to increase risk. These effects held for all ethnic groups in the sample.ConclusionsExposure to childhood and adult disadvantage may combine in complex ways to push some individuals along a predominantly sociodevelopmental pathway to psychosis. [ABSTRACT FROM PUBLISHER]

BackgroundTo date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.MethodOne hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls.ResultsAt baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035).ConclusionsWe provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data. [ABSTRACT FROM PUBLISHER]