Time to All-cause Treatment Discontinuation as the Primary Outcome in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Study

Response rates to sequential trials of antipsychotic medications according to algorithms or treatment guidelines in psychotic disorders. A systematic review and meta-analysis

BACKGROUND

There is a relative lack of research evaluating the outcomes when treatment guidelines or algorithms for psychotic disorders are followed. This systematic review and meta-analysis determined the response rates to antipsychotic medications at different stages of these algorithms and whether these response rates differ in first episode cohorts.

METHODS

Data sources: A systematic search strategy was conducted across four databases PubMed, EMBASE, PsycINFO (Ovid) and CINAHL. Studies that had sequential trials of different antipsychotic medications were included. A meta-analysis of proportions was performed using random effects models and sub-group analysis in first episode psychosis studies.

RESULTS

Of the 4078 unique articles screened, fourteen articles, from nine unique studies, were eligible and included 2522 participants. The proportion who experienced a response to any antipsychotic in the first stage of an algorithm was 0.53 (95 % C.I.:0.38,0.68) and this decreased to 0.26 (95 % C.I.:0.15,0.39) in the second stage. When clozapine was used in the third stage, the proportion that achieved a response was 0.43 (95 % C.I. 0.19, 0.69) compared to 0.26 (95 % C.I.:0.05,0.54) if a different antipsychotic was used. Four studies included 907 participants with a first episode of psychosis and the proportions that achieved a response were: 1st stage: 0.63 (95 % C.I.: 0.45, 0.79); 2nd stage: 0.34 (95 % C.I.:0.16,0.55); clozapine 3rd stage: 0.45 (95 % C.I.:0.0,0.97), different antipsychotic 3rd stage: 0.15 (95 % C.I.,0.01,0.37).

DISCUSSION

These findings support the recommendation to have a trial of clozapine after two other antipsychotic medications have been found to be ineffective.

Shared decision-making interventions for people with mental health conditions

BACKGROUND

One person in every four will suffer from a diagnosable mental health condition during their life. Such conditions can have a devastating impact on the lives of the individual and their family, as well as society.  International healthcare policy makers have increasingly advocated and enshrined partnership models of mental health care. Shared decision-making (SDM) is one such partnership approach. Shared decision-making is a form of service user-provider communication where both parties are acknowledged to bring expertise to the process and work in partnership to make a decision.  This review assesses whether SDM interventions improve a range of outcomes. This is the first update of this Cochrane Review, first published in 2010.

OBJECTIVES

To assess the effects of SDM interventions for people of all ages with mental health conditions, directed at people with mental health conditions, carers, or healthcare professionals, on a range of outcomes including: clinical outcomes, participation/involvement in decision-making process (observations on the process of SDM; user-reported, SDM-specific outcomes of encounters), recovery, satisfaction, knowledge, treatment/medication continuation, health service outcomes, and adverse outcomes.

SEARCH METHODS

We ran searches in January 2020 in CENTRAL, MEDLINE, Embase, and PsycINFO (2009 to January 2020). We also searched trial registers and the bibliographies of relevant papers, and contacted authors of included studies.  We updated the searches in February 2022. When we identified studies as potentially relevant, we labelled these as studies awaiting classification.

SELECTION CRITERIA

Randomised controlled trials (RCTs), including cluster-randomised controlled trials, of SDM interventions in people with mental health conditions (by Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD) criteria).

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. We used GRADE to assess the certainty of the evidence.

MAIN RESULTS

This updated review included 13 new studies, for a total of 15 RCTs. Most participants were adults with severe mental illnesses such as schizophrenia, depression, and bipolar disorder, in higher-income countries. None of the studies included children or adolescents.  Primary outcomes We are uncertain whether SDM interventions improve clinical outcomes, such as psychiatric symptoms, depression, anxiety, and readmission, compared with control due to very low-certainty evidence. For readmission, we conducted subgroup analysis between studies that used usual care and those that used cognitive training in the control group. There were no subgroup differences. Regarding participation (by the person with the mental health condition) or level of involvement in the decision-making process, we are uncertain if SDM interventions improve observations on the process of SDM compared with no intervention due to very low-certainty evidence. On the other hand, SDM interventions may improve SDM-specific user-reported outcomes from encounters immediately after intervention compared with no intervention (standardised mean difference (SMD) 0.63, 95% confidence interval (CI) 0.26 to 1.01; 3 studies, 534 participants; low-certainty evidence). However, there was insufficient evidence for sustained participation or involvement in the decision-making processes. Secondary outcomes We are uncertain whether SDM interventions improve recovery compared with no intervention due to very low-certainty evidence. We are uncertain if SDM interventions improve users' overall satisfaction. However, one study (241 participants) showed that SDM interventions probably improve some aspects of users' satisfaction with received information compared with no intervention: information given was rated as helpful (risk ratio (RR) 1.33, 95% CI 1.08 to 1.65); participants expressed a strong desire to receive information this way for other treatment decisions (RR 1.35, 95% CI 1.08 to 1.68); and strongly recommended the information be shared with others in this way (RR 1.32, 95% CI 1.11 to 1.58). The evidence was of moderate certainty for these outcomes. However, this same study reported there may be little or no effect on amount or clarity of information, while another small study reported there may be little or no change in carer satisfaction with the SDM intervention. The effects of healthcare professional satisfaction were mixed: SDM interventions may have little or no effect on healthcare professional satisfaction when measured continuously, but probably improve healthcare professional satisfaction when assessed categorically. We are uncertain whether SDM interventions improve knowledge, treatment continuation assessed through clinic visits, medication continuation, carer participation, and the relationship between users and healthcare professionals because of very low-certainty evidence. Regarding length of consultation, SDM interventions probably have little or no effect compared with no intervention (SDM 0.09, 95% CI -0.24 to 0.41; 2 studies, 282 participants; moderate-certainty evidence). On the other hand, we are uncertain whether SDM interventions improve length of hospital stay due to very low-certainty evidence. There were no adverse effects on health outcomes and no other adverse events reported.

AUTHORS' CONCLUSIONS

This review update suggests that people exposed to SDM interventions may perceive greater levels of involvement immediately after an encounter compared with those in control groups. Moreover, SDM interventions probably have little or no effect on the length of consultations.  Overall we found that most evidence was of low or very low certainty, meaning there is a generally low level of certainty about the effects of SDM interventions based on the studies assembled thus far. There is a need for further research in this area.

Impact of Selected Initial Titration Schedules on Safety and Long-Term Effectiveness of Lamotrigine for the Treatment of Mood Disorders

PURPOSE

Lamotrigine (LTG) is used for treatment of mood disorders, but it is associated with the risk of rash occurrence in the initial administration phase. Although slow titration reduces this risk, its effectiveness in the treatment of mood disorders has not been verified. The effects of titration method on the safety and effectiveness of LTG for the treatment of mood disorders were examined in this study.

METHODS

This retrospective cohort study included 312 patients with mood disorders who underwent initiation of LTG therapy. Data regarding baseline demographics, titration schedules, concomitant medications, and time to and cause of discontinuation of LTG were collected. A multivariate analysis was used to evaluate the effects of the titration schedules. The 12-month effectiveness was also evaluated.

RESULTS

The 12-month discontinuation rate of LTG was 16.7%. The most frequent cause of discontinuation was development of a rash (47.7%, n = 312). Fast titration (adjusted odds ratio, 8.15) significantly increased the risk of rash development, and slow titration (adjusted odds ratio, 0.29) significantly decreased this risk. The time to all-cause discontinuation was not significantly different between the slow and standard titration groups (n = 303). After 12 months of treatment, the condition of 46.7% patients were rated much or very much improved using CGI-C.

CONCLUSIONS

Although slow titration of LTG reduces the occurrence of a rash, it is not more effective than standard titration in the long term. Optimizing the initial LTG titration schedule for patients with mood disorders is challenging.

Predictors of clozapine discontinuation at 2 years in treatment-resistant schizophrenia

INTRODUCTION

Little is known about predictors of clinical response to clozapine treatment in treatment-resistant psychosis. Most published cohorts are small, providing inconsistent results. We aimed to identify baseline clinical predictors of future clinical response in patients who initiate clozapine treatment, mainly focusing on the effect of age, duration of illness, baseline clinical symptoms and homelessness.

METHODOLOGY

Retrospective cohort of patients with treatment-resistant schizophrenia, aged between 15 and 60 years, that initiated clozapine between 2014 and 2017. Sociodemographic characteristics, years from illness diagnosis, and clinical presentation before the initiation of clozapine were collected and analyzed. All-cause discontinuation at two years follow-up was used as the primary measure of clozapine response.

RESULTS

261 patients were included with a median age at illness diagnosis of 23 years old (IQR 19-29) and a median age at clozapine initiation of 25 (IQR: 21-33). 72.33% (183/253) continued clozapine after two years follow-up. Being homeless was associated to higher clozapine non-adherence, with an OR of 2.78 (95%CI 1.051-7.38) (p = 0.039, controlled by gender). Older age at clozapine initiation and longer delay from first schizophrenia diagnosis to clozapine initiation were also associated with higher clozapine non-adherence, with each year increasing the odds of discontinuation by 1.043 (95%CI 1.02-1.07; p = 0.001) and OR 1.092 (95%CI 1.01-1.18;p = 0.032) respectively.

CONCLUSION

Starting clozapine in younger patients or shortly after schizophrenia diagnosis were associated with better adherence.

Enhancing clarity of clinical trial safety reports for data monitoring committees

A Data Monitoring Committee (DMC) evaluates patient safety in a clinical trial of an investigational intervention through periodic review of adverse events (AEs) and clinical safety assessments. Our aim was to construct DMC report displays to enhance the DMC safety review through use of graphics and clear identification and adjustment for missing data caused by early discontinuations and ongoing study participation. Suggested displays include a study snapshot graph, enhanced adverse event incidence tables including the incidence density and plotted incidence proportions, line graphs in place of by-patient listings, and trend plots in place of tables for continuous assessments.

Predictors of Time to Discontinuation of Levodopa-Carbidopa Intestinal Gel Infusion: A Retrospective Cohort Study

Background:

Continuous intra-duodenal infusion of levodopa-carbidopa intestinal gel (LCIG) is a well-established therapy for patients with advanced Parkinson’s disease (PD) suffering from motor complications despite optimized treatment with oral dopaminomimetics. However, time to discontinuation of treatment with LCIG varies considerably between patients, ranging from a few months to more than ten years. To improve the selection of candidates for LCIG, knowledge of prognostic factors is of paramount importance.

Objective:

To explore baseline predictors of time to discontinuation of LCIG.

Methods:

In this two-center retrospective cohort study, we reviewed the medical files of 98 PD patients treated with LCIG between April 2006 and December 2015 (53% male; mean age: 66.2 years; mean disease duration: 12.3 years). Baseline patient characteristics were used as covariates in Cox regression models.

Results:

During follow-up (mean observation time: 2.6 years; range: 0.1–9.3) eighteen patients discontinued treatment (18.4%), while seven patients died (7.1%). Median duration of treatment with LCIG, estimated with Kaplan-Meier analysis, was 7.8 years (95% CI: 6.7–9.0). Disease duration (in years) at baseline was a statistically significant predictor of time to discontinuation of LCIG (HR: 0.85; 95% CI: 0.75–0.96, p = 0.006). All other characteristics studied, e.g. age >70 years, did not show statistically significant associations with the total duration of treatment with LCIG.

Conclusion:

Our findings show a low overall rate of discontinuation of LCIG infusion, with a median duration of treatment of 7.8 years. Shorter disease duration at baseline appeared to be a predictor of earlier discontinuation of LCIG.

Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules

Precision medicine seeks to provide treatment only if, when, to whom, and at the dose it is needed. Thus, precision medicine is a vehicle by which healthcare can be made both more effective and efficient. Individualized treatment rules operationalize precision medicine as a map from current patient information to a recommended treatment. An optimal individualized treatment rule is defined as maximizing the mean of a pre-specified scalar outcome. However, in settings with multiple outcomes, choosing a scalar composite outcome by which to define optimality is difficult. Furthermore, when there is heterogeneity across patient preferences for these outcomes, it may not be possible to construct a single composite outcome that leads to high-quality treatment recommendations for all patients. We simultaneously estimate the optimal individualized treatment rule for all composite outcomes representable as a convex combination of the (suitably transformed) outcomes. For each patient, we use a preference elicitation questionnaire and item response theory to derive the posterior distribution over preferences for these composite outcomes and subsequently derive an estimator of an optimal individualized treatment rule tailored to patient preferences. We prove that as the number of subjects and items on the questionnaire diverge, our estimator is consistent for an oracle optimal individualized treatment rule wherein each patient's preference is known a priori. We illustrate the proposed method using data from a clinical trial on antipsychotic medications for schizophrenia.

Noninferiority of perphenazine vs. three second-generation antipsychotics in chronic schizophrenia

Noninferiority analysis is a statistical method of growing importance in comparative effectiveness research that has rarely been used in psychopharmacology. This method is used here to evaluate whether first-generation antipsychotics are clinically not inferior to second-generation antipsychotics (SGAs) using data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). A conservative noninferiority margin (NIM) on the Positive and Negative Syndrome Scale (PANSS) was derived from the smallest published value for the minimal clinically important difference, further reduced by 25%. This NIM was used to assess whether perphenazine is noninferior to olanzapine, risperidone, and quetiapine on the basis of the 95% confidence intervals of differences in mean PANSS outcomes (N = 1049). Perphenazine was noninferior to all three SGAs during 18 months of intention-to-treat analysis and in several subanalyses. Noninferiority can be evaluated from studies designed as superiority trials. Power was available in the CATIE to conduct noninferiority analysis.