Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis.

Objective: Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse. Materials and Methods: Anonymized records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analyzed. To characterize the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders. Results: Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.6 times higher than non-affective psychotic disorders [OR 2.6, 95% CI (1.8-3.7), p < 0.001]. Discussion: The results imply that the odds of a patient with affective disorder exhibiting a predictable trend in time to relapse were much higher than a patient with recurrent non-affective psychotic disorder. In other words, within recurrent non-affective psychosis group, time to relapse is random. Conclusion: This study is an initial attempt to develop a longitudinal trajectory-based approach to investigate relapse trend differences in mental health patients. Further investigations using this approach may reflect differences in underlying biological processes between illnesses.

Using algorithms to initiate needs-based interventions for people on antipsychotic medication: implementation protocol.

INTRODUCTION: Non-adherence to antipsychotic medications for individuals with serious mental illness increases risk of relapse and hospitalisation. Real time monitoring of adherence would allow for early intervention. AI2 is a both a personal nudging system and a clinical decision support tool that applies machine learning on Medicare prescription and benefits data to raise alerts when patients have discontinued antipsychotic medications without supervision, or when essential routine health checks have not been performed. METHODS AND ANALYSIS: We outline two intervention models using AI2. In the first use-case, the personal nudging system, patients receive text messages when an alert of a missed medication or routine health check is detected by AI2. In the second use-case, as a clinical decision support tool, AI2 generated alerts are presented as flags through a dashboard to the community mental health professionals. Implementation protocols for different scenarios of AI2, along with a mixed-methods evaluation, are planned to identify pragmatic issues necessary to inform a larger randomised control trial, as well as improve the application. ETHICS AND DISSEMINATION: This study protocol has been approved by The Southern Adelaide Clinical Human Research Ethics Committee. The dissemination of this trial will serve to inform further implementation of the AI2 into daily personal and clinical practice.

Monte Carlo Simulations Demonstrate Algorithmic Interventions Over Time Reduce Hospitalisation in Patients With Schizophrenia and Bipolar Disorder.

Non-adherence with pharmacologic treatment is associated with increased rates of relapse and rehospitalisation among patients with schizophrenia and bipolar disorder. To improve treatment response, remission, and recovery, research efforts are still needed to elucidate how to effectively map patient's response to medication treatment including both therapeutic and adverse effects, compliance, and satisfaction in the prodromal phase of illness (ie, the time period in between direct clinical consultation and relapse). The Actionable Intime Insights (AI2) application draws information from Australian Medicare administrative claims records in real time when compliance with treatment does not meet best practice guidelines for managing chronic severe mental illness. Subsequently, the AI2 application alerts clinicians and patients when patients do not adhere to guidelines for treatment. The aim of this study was to evaluate the impact of the AI2 application on the risk of hospitalisation among simulated patients with schizophrenia and bipolar disorder. Monte Carlo simulation methodology was used to estimate the impact of the AI2 intervention on the probability of hospitalisation over a 2-year period. Results indicated that when the AI2 algorithmic intervention had an efficacy level of (>0.6), over 80% of actioned alerts were contributing to reduced hospitalisation risk among the simulated patients. Such findings indicate the potential utility of the AI2 application should replication studies validate its methodologic and ecological rigour in real-world settings.

Prevalence of victimization, posttraumatic stress disorder and violent behaviour in the seriously mentally ill.

AIMS: There is evidence that individuals with a mental illness are more likely to report a history of victimization and to be at an increased risk for future victimization. The aims of the current study are to determine lifetime rates of different types of victimization in a population of psychiatric inpatients and to examine the associations between a history of victimization and measures of adverse outcome and rates of posttraumatic stress disorder (PTSD). METHOD: A total of 130 psychiatric inpatients with a range of psychiatric diagnoses were surveyed. Information collected included history of victimization, aggression and violence levels, suicidal ideation, PTSD symptomatology, rates of hospitalization and pension status. RESULTS: A lifetime history of victimization was reported in 87.7% of patients with 46% having lifetime and 32% current PTSD. Most clinicians did not identify the high rates of comorbid PTSD in these patients. Victimization was associated significantly with receipt of the disability support pension and number of previous psychiatric hospitalizations, both measures of more adverse outcome. CONCLUSION: Victimization may have a negative impact on outcome and may further disadvantage an already vulnerable population. These findings have both clinical and policy implications for the long-term management of people with mental illness.

Predictors of depression 12 months after cardiac hospitalization: the Identifying Depression as a Comorbid Condition study.

OBJECTIVE: To determine characteristics which predict depression at 12 months after cardiac hospitalization, and track the natural history of depression. METHOD: Depressive symptoms were monitored at baseline, 3 and 12 months in a cohort of 785 patients, using the self-report Center for Epidemiological Studies Depression Scale. Multinomial regression analyses of baseline clinical and demographic variables identified characteristics associated with depression at 12 months. RESULTS: Three baseline variables predicted moderate to severe depression at 12 months: depression during index admission, past history of emotional health problems and current smoking. For those who were depressed during cardiac hospitalization, 51% remained depressed at both 3 and 12 months. Persistence was more evident in patients who had moderate to severe depressive symptoms when hospitalized. Mild depression was as likely to persist as to remit. CONCLUSIONS: Three clinically accessible characteristics at the time of cardiac hospitalization can assist in predicting depression at 12 months and may aid treatment decisions. Depressive symptoms persist in a substantial proportion of cardiac patients up to 12 months after hospitalization.

Depression after cardiac hospitalisation--the Identifying Depression as a Comorbid Condition (IDACC) study.

BACKGROUND: The Identifying Depression as a Comorbid Condition (IDACC) study aimed to identify depressive symptoms in hospitalised cardiac patients and support management of depression in general practice. OBJECTIVE: This post hoc analysis of the IDACC trial examines the effectiveness and practicality of different forms of communication between hospital psychiatric services and general practitioners. METHODS: We randomised 669 cardiac inpatients with depressive symptoms, identified with the Center for Epidemiological Studies Depression Scale (CES-D), to an intervention or usual care control group. Individual depression scores and depression management guidelines were sent to GPs of all intervention patients. Where possible, psychiatric advice was provided to the GP either by multidisciplinary enhanced primary care case conference or one-to-one telephone advice. RESULTS: Multidisciplinary case conferences were implemented for only 24% of intervention patients. General practitioners received individual telephone advice in 40% of cases, and 36% received written information only. The psychiatrist telephone advice resulted in a significant reduction in the proportion of patients with moderate to severe depression 12 months after cardiac hospitalisation (19% vs. 35%). DISCUSSION: Screening, combined with psychiatrist telephone advice to GPs, was simple to organise and effective in reducing depression severity after cardiac admission.

Effect of psychiatry liaison with general practitioners on depression severity in recently hospitalised cardiac patients: a randomised controlled trial.

OBJECTIVE: To evaluate the effect on depressive symptoms in cardiac patients of patient-specific advice to general practitioners regarding management of comorbid depression. DESIGN AND SETTING: A randomised controlled trial in four general hospitals in Adelaide, South Australia. PARTICIPANTS: Patients (n = 669) admitted to cardiology units for a range of cardiovascular conditions who were screened and assessed as being depressed according to the Center for Epidemiological Studies Depression Scale (CES-D). INTERVENTION: Inpatient psychiatric review, followed by telephone case conferencing between specialist hospital staff and GPs to provide patient-specific information about the patient's depression and its management, educational material, and ongoing clinical support. MAIN OUTCOME MEASURES: Level of depression severity at 12 months post-hospitalisation. RESULTS: On the basis of intention to treat, intervention patients had lower rates of moderate to severe depression (CES-D > or = 27) after 12 months (25% v 35%, relative risk, 0.72; 95% CI, 0.54-0.96, number needed to treat for benefit, 11). The intervention was most effective in preventing progression from mild depression to moderate to severe depression. The multidisciplinary telephone case conferencing was difficult to implement and, in a post hoc analysis, brief phone advice from a psychiatrist was found to be effective. CONCLUSIONS: Screening hospitalised cardiac patients for depression and providing targeted advice to their GPs reduces depression severity 12 months after hospitalisation.