Faculty Bibliography

OBJECTIVE:Cognitive challenges are prominent features of individuals diagnosed with schizophrenia, impairing occupational, social, and economic functioning. These challenges are predictive of social and work outcomes. Cognitive remediation has been shown to be effective in improving both cognitive and social functions. However, cognitive remediation does not produce improvement in all participants. We investigated demographic, neurocognitive, and psychopathological predictors associated with improvement following cognitive remediation interventions in service recipients with severe mental illnesses. METHOD/METHODS:One hundred thirty-seven adult participants with a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.) were enrolled in 12-week cognitive remediation programs. Assessments of demographic and illness variables, together with baseline and end point assessment of psychopathology (Positive and Negative Syndrome Scale [PANSS]), neurocognition (Measurement and Treatment Research to Improve Cognition in Schizophrenia [MATRICS] Consensus Cognitive Battery [MCBB]), and social functions (Personal and Social Performance Scale [PSP]) were conducted. Change in cognitive domains was calculated using the reliable change index. Logistic regression analysis was used to assess predictors of cognitive improvement after the intervention. RESULTS:Sixty-two percent of participants improved on at least 1 of the MCCB domains. Higher baseline speed of processing, attention or vigilance, and working memory predicted a positive response to cognitive remediation. Younger age, higher education level, shorter length of stay, and lower PANSS Negative and Disorganized factors were additional predictors. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE/CONCLUSIONS:Our results indicate the clinical usefulness of cognitive remediation and identified a pattern of clinical and cognitive predictors of good response to the intervention. Identification of these predictive factors by clinicians may enhance the outcome and aid in the development of individualized rehabilitative cognitive remediation treatment plans. (PsycINFO Database Record

Objectives: Tardive dyskinesia (TD) is a persistent movement disorder that is often induced by chronic exposure to dopamine receptor blocking agents (e.g., antipsychotics). Valbenazine (VBZ; NBI-98854) is a novel, highly selective vesicular monoamine transporter 2 (VMAT2) inhibitor currently in development for TD treatment. VBZ has exhibited a favorable efficacy and safety profile in multiple clinical studies including the KINECT 2 (NCT01733121) trial, a dose-escalating Phase 2 trial in which treatment with VBZ vs placebo (PBO) resulted in significant and clinically meaningful improvements in TD symptoms. The present analysis evaluated the psychiatric status of subjects in this study. METHODS: KINECT 2 was a 6-week, randomized, doubleblind, PBO-controlled trial in subjects with schizophrenia, mood disorder, or gastrointestinal disorder and moderateto-severe TD. Subjects were randomized (1:1) to once-daily PBO or VBZ, which was initiated at 25mg and escalated in 25mg increments every 2 weeks to a maximum of 75mg. The primary efficacy endpoint was defined as the change from baseline at Week 6 in the Abnormal Involuntary Movement Scale (AIMS) score for VBZ vs PBO, as scored by two blinded central raters. Safety assessments were analyzed descriptively and included the following psychiatric scales: Positive andNegative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), Young Mania Rating Scale (YMRS), Montgomery-Asberg Depression Rating Scale (MADRS), and Columbia Suicide Severity Rating Scale (C-SSRS). RESULTS: The safety population included 51 VBZ and 49 PBO subjects; 76% of the VBZ subjects reached the maximum dose of 75 mg. Antipsychotics, antidepressants, and anxiolytics were the most common concomitant medications, taken by.40% of subjects in each group. The Week 6 change from baseline in AIMS score (primary endpoint) was significantly greater for VBZ vs PBO (P=0.0005). Psychiatric status remained stable or improved from baseline in both groups, as shown by the following mean score changes at Week 6: PANSS Positive (VBZ.0.6, PBO-1.0), PANSS Negative (VBZ 0.5, PBO.0.9), and PANSS General Psychopathology (VBZ.0.5, PBO.0.7); CDSS (VBZ.0.9, PBO.0.7); YMRS (VBZ-1.1, PBO.0.3); and MADRS (VBZ-1.5, PBO.0.2). The percentage of subjects with suicidal ideation or behavior as measured by the C-SSRS for VBZ vs PBO was 5.9% vs 2.0% (screening) and 5.9% vs 0% (Weeks 2-8). CONCLUSIONS: There was no apparent increase in psychopathology, depression, or suicidality with VBZ. Psychiatric status remained stable or improved in subjects with TD who had underlying schizophrenia, schizoaffective disorder, depression or bipolar disorder

OBJECTIVE: Overestimating patients' medication adherence diminishes the ability of psychiatric care providers to prescribe the most effective treatment and to identify the root causes of treatment resistance in schizophrenia. This study was conducted to determine how credible patient drug adherence information (PDAI) might change prescribers' treatment decisions. METHODS: In an online survey containing 8 clinical case vignettes describing patients with schizophrenia, health care practitioners who prescribe antipsychotics to patients with schizophrenia were instructed to choose a preferred treatment recommendation from a set of predefined pharmacologic and non-pharmacologic options. The prescribers were randomly assigned to an experimental or a control group, with only the experimental group receiving PDAI. The primary outcome was the prescribers' treatment choice for each case. Between-group differences were analyzed using multinomial logistic regression. RESULTS: A convenience sample (n=219) of prescribers completed the survey. For 3 nonadherent patient vignettes, respondents in the experimental group were more likely to choose a long-acting injectable antipsychotic compared with those in the control group (77.7% experimental vs 25.8% control; P<0.001). For 2 adherent but poorly controlled patient vignettes, prescribers who received PDAI were more likely to increase the antipsychotic dose compared with the control group (49.1% vs 39.1%; P<0.001). For the adherent and well-controlled patient vignette, respondents in both groups made similar treatment recommendations across all choices (P=0.099), but respondents in the experimental arm were more likely to recommend monitoring clinical stability (87.2% experimental vs 75.5% control, reference group). CONCLUSION: The results illustrate how credible PDAI can facilitate more appropriate clinical decisions for patients with schizophrenia.

BACKGROUND: Poor medication adherence contributes to negative treatment response, symptom relapse, and hospitalizations in schizophrenia. Many health plans use claims-based measures like medication possession ratios or proportion of days covered (PDC) to measure patient adherence to antipsychotics. Classifying patients solely on the basis of a single average PDC measure, however, may mask clinically meaningful variations over time in how patients arrive at an average PDC level. OBJECTIVE: To model patterns of medication adherence evolving over time for patients with schizophrenia who initiated treatment with an oral atypical antipsychotic and, based on these patterns, to identify groups of patients with different adherence behaviors. METHODS: We analyzed health insurance claims for patients aged >/= 18 years with schizophrenia and newly prescribed oral atypical antipsychotics in 2007-2013 from 3 U.S. insurance claims databases: Truven MarketScan (Medicaid and commercial) and Humana (Medicare). Group-based trajectory modeling (GBTM) was used to stratify patients into groups with distinct trends in adherence and to estimate trends for each group. The response variable was the probability of adherence (defined as PDC >/= 80%) in each 30-day period after the patient initiated antipsychotic therapy. GBTM proceeds from the premise that there are multiple distinct adherence groups. Patient demographics, health status characteristics, and health care resource use metrics were used to identify differences in patient populations across adherence trajectory groups. RESULTS: Among the 29,607 patients who met the inclusion criteria, 6 distinct adherence trajectory groups emerged from the data: adherent (33%); gradual discontinuation after 3 months (15%), 6 months (7%), and 9 months (5%); stop-start after 6 months (15%); and immediate discontinuation (25%). Compared to patients 18-24 years of age in the adherent group, patients displaying a stop-start pattern after 6 months had greater odds of having a history of drug abuse (OR = 1.46; 95% CI = 1.26-1.66; P < 0.001), alcohol abuse (OR = 1.34; 95% CI = 1.14-1.53; P< 0.001), and a codiagnosis of major depressive disorder (OR = 1.24; 95% CI = 1.05-1.44; P < 0.001) and were less likely to be aged 35-54 years (OR = 0.66; 95% CI = 0.46-0.85; P < 0.001). CONCLUSIONS: Longitudinal medication adherence patterns can be expressed as distinct trajectories associated with specific patient characteristics and health care utilization patterns. We found 6 distinct patterns of adherence to antipsychotics over 12 months. Patients in different groups may warrant different types of clinical interventions to prevent hospitalizations, longer hospital stays, and increased clinical complexity. For example, clinicians may consider regular home visits, assertive community treatment, and other related interventions for patients at high risk of immediate discontinuation. Health plans should consider supplementing claims-based adherence measures with new technologies that are able to track patient adherence patterns over time. DISCLOSURES: Otsuka Pharmaceutical Development & Commercialization provided support for this research. MacEwan and Shafrin are employees of Precision Health Economics, which was contracted by Otsuka Pharmaceutical Development & Commercialization to conduct this study. Lakdawalla is the Chief Scientific Officer and a founding partner of Precision Health Economics. Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. Hatch is a former employee of Otsuka Pharmaceutical Development & Commercialization and is a current employee of ODH, Inc. Lindenmayer has received grant/research support from Janssen, Lilly, AstraZeneca, Johnson & Johnson, Pfizer, BMS, Otsuka, Dainippon, and Roche and is a consultant for Janssen, Lilly, Merck, Shire, and Lundbeck. Portions of this study were presented as a poster at the American Society of Clinical Psychopharmacology Annual Meeting in Miami Beach, Florida; June 23, 2015; and at the 28th Annual U.S. Psychiatric and Mental Health Congress; San Diego, California; September 12, 2015. Study concept and design were contributed by Forma, Ladkawalla, MacEwan, and Shafrin, along with Hatch and Lindenmayer. MacEwan, Shafrin, Forma, and Lakdawalla collected the data, along with Hatch and Lindenmayer. Data interpretation was performed by Hatch, Lindenmayer, MacEwan, and Shafrin, assisted by Forma and Lakdawalla. The manuscript was written and revised by MacEwan, Forma, and Shafrin, along with Hatch Lakdawalla, and Lindenmayer.