Evolving Adult ADHD Care: Preparatory Evaluation of a Prototype Digital Service Model Innovation for ADHD Care.

BACKGROUND: Given the prevalence of ADHD and the gaps in ADHD care in Australia, this study investigates the critical barriers and driving forces for innovation. It does so by conducting a preparatory evaluation of an ADHD prototype digital service innovation designed to help streamline ADHD care and empower individual self-management. METHODS: Semi-structured interviews with ADHD care consumers/participants and practitioners explored their experiences and provided feedback on a mobile self-monitoring app and related service innovations. Interview transcripts were double coded to explore thematic barriers and the enablers for better ADHD care. RESULTS: Fifteen interviews (9 consumers, 6 practitioners) revealed barriers to better ADHD care for consumers (ignorance and prejudice, trust, impatience) and for practitioners (complexity, sustainability). Enablers for consumers included validation/empowerment, privacy, and security frameworks, tailoring, and access. Practitioners highlighted the value of transparency, privacy and security frameworks, streamlined content, connected care between services, and the tailoring of broader metrics. CONCLUSIONS: A consumer-centred approach to digital health service innovation, featuring streamlined, private, and secure solutions with enhanced mobile tools proves instrumental in bridging gaps in ADHD care in Australia. These innovations should help to address the gaps in ADHD care in Australia. These innovations should encompass integrated care, targeted treatment outcome data, and additional lifestyle support, whilst recognising the tensions between customised functionalities and streamlined displays.

Molecular Mechanism Biomarkers Predict Diagnosis in Schizophrenia and Schizoaffective Psychosis, with Implications for Treatment.

Diagnostic uncertainty and relapse rates in schizophrenia and schizoaffective disorder are relatively high, indicating the potential involvement of other pathological mechanisms that could serve as diagnostic indicators to be targeted for adjunctive treatment. This study aimed to seek objective evidence of methylenetetrahydrofolate reductase MTHFR C677T genotype-related bio markers in blood and urine. Vitamin and mineral cofactors related to methylation and indolamine-catecholamine metabolism were investigated. Biomarker status for 67 symptomatically well-defined cases and 67 asymptomatic control participants was determined using receiver operating characteristics, Spearman's correlation, and logistic regression. The 5.2%-prevalent MTHFR 677 TT genotype demonstrated a 100% sensitive and specific case-predictive biomarkers of increased riboflavin (vitamin B2) excretion. This was accompanied by low plasma zinc and indicators of a shift from low methylation to high methylation state. The 48.5% prevalent MTHFR 677 CC genotype model demonstrated a low-methylation phenotype with 93% sensitivity and 92% specificity and a negative predictive value of 100%. This model related to lower vitamin cofactors, high histamine, and HPLC urine indicators of lower vitamin B2 and restricted indole-catecholamine metabolism. The 46.3%-prevalent CT genotype achieved high predictive strength for a mixed methylation phenotype. Determination of MTHFR C677T genotype dependent functional biomarker phenotypes can advance diagnostic certainty and inform therapeutic intervention.

Molecular Mechanisms Provide a Landscape for Biomarker Selection for Schizophrenia and Schizoaffective Psychosis.

Research evaluating the role of the 5,10-methylenetetrahydrofolate reductase (MTHFR C677T) gene in schizophrenia has not yet provided an extended understanding of the proximal pathways contributing to the 5-10-methylenetetrahydrofolate reductase (MTHFR) enzyme's activity and the distal pathways being affected by its activity. This review investigates these pathways, describing mechanisms relevant to riboflavin availability, trace mineral interactions, and the 5-methyltetrahydrofolate (5-MTHF) product of the MTHFR enzyme. These factors remotely influence vitamin cofactor activation, histamine metabolism, catecholamine metabolism, serotonin metabolism, the oxidative stress response, DNA methylation, and nicotinamide synthesis. These biochemical components form a broad interactive landscape from which candidate markers can be drawn for research inquiry into schizophrenia and other forms of mental illness. Candidate markers drawn from this functional biochemical background have been found to have biomarker status with greater than 90% specificity and sensitivity for achieving diagnostic certainty in schizophrenia and schizoaffective psychosis. This has implications for achieving targeted treatments for serious mental illness.

UPRITE: Promoting Positive Posture in Children and Adolescents.

Technology use associated with habitual posture is linked with the decline in mental well-being. The objective of this study was to evaluate the potential of posture improvement through game play. 73 children and adolescents were recruited, and accelerometer data collected through game play was analyzed. The data analysis reveals that the game/app affects and encourages upright/vertical posture.

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems.

BACKGROUND: Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events ("flags") through suggesting evidence-based courses of action. However, extant literature shows multiple barriers-perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.-to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic. METHODS: The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI2). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach-starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories' relationships with client and medication-subtype characteristics were tested. RESULTS: The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors-the clients' environment, their clinical relationships, and medical needs-mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ2 = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up0 and Not-followed up1 flags (M0 = 31.78; M1 = 45.55; U = 12,119; p < 0.001; η2 = .05). CONCLUSION: These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.

Automated Decision Support For Community Mental Health Services Using National Electronic Health Records: Qualitative Implementation Case Study.

BACKGROUND: A high proportion of patients with severe mental illness relapse due to nonadherence to psychotropic medication. In this paper, we use the normalization process theory (NPT) to describe the implementation of a web-based clinical decision support system (CDSS) for Community Mental Health Services (CMHS) called Actionable Intime Insights or AI2. AI2 has two distinct functions: (1) it provides an overview of medication and treatment history to assist in reviewing patient adherence and (2) gives alerts indicating nonadherence to support early intervention. OBJECTIVE: Our objective is to evaluate the pilot implementation of the AI2 application to better understand the challenges of implementing a web-based CDSS to support medication adherence and early intervention in CMHS. METHODS: The NPT and participatory action framework were used to both explore and support implementation. Qualitative data were collected over the course of the 14-month implementation, in which researchers were active participants. Data were analyzed and coded using the NPT framework. Qualitative data included discussions, meetings, and work products, including emails and documents. RESULTS: This study explores the barriers and enablers of implementing a CDSS to support early intervention within CMHS using Medicare data from Australia's national electronic record system, My Health Record (MyHR). The implementation was a series of ongoing negotiations, which resulted in a staged implementation with compromises on both sides. Clinicians were initially hesitant about using a CDSS based on MyHR data and expressed concerns about the changes to their work practice required to support early intervention. Substantial workarounds were required to move the implementation forward. This pilot implementation allowed us to better understand the challenges of implementation and the resources and support required to implement and sustain a model of care based on automated alerts to support early intervention. CONCLUSIONS: The use of decision support based on electronic health records is growing, and while implementation is challenging, the potential benefits of early intervention to prevent relapse and hospitalization and ensure increased efficiency of the health care system are worth pursuing.

Demonstration of automated non-adherence and service disengagement risk monitoring with active follow-up for severe mental illness.

AIMS: Medication cessation and service disengagement often precedes relapse in people with severe mental illnesses but currently specialist mental health services only become involved after a relapse. Early detection of non-adherence is needed to enable intervention to avert relapse. This paper aims to demonstrate how digitally automated non-adherence risk monitoring from Medicare data with active follow-up can work and perform in practice in a real-world mental health service setting. METHODS: AI2 software is an automated risk monitoring tool to detect non-adherence using Medicare data. It was implemented prospectively in a cohort of 354 registered patients of a community mental health clinic between July 2019 and February 2020. Patients flagged as at risk by the software were reviewed by two clinicians. We describe the risks automatically flagged for non-adherence and the clinical responses. We examine differences in clinical and demographic factors in patients flagged at increased risk of non-adherence. RESULTS: In total, 46.7% (142/304) were flagged by the software as at risk of non-adherence, and 22% (31/142) received an intervention following clinician review of their case notes. Patients flagged by the software were older in age and had more prior mental health treatment episodes. More alerts were associated with patients who had been transferred from the mental health service to the care of their general practitioners, and those with more alerts were more likely to receive a follow-up intervention. CONCLUSION: Digitally automated monitoring for non-adherence risk is feasible and can be integrated into clinical workflows in community psychiatric and primary care settings. The technology may assist clinicians and services to detect non-adherence behaviour early, thereby triggering interventions that have the potential to reduce rates of mental health deterioration and acute illness relapse.

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.

Health timeline: an insight-based study of a timeline visualization of clinical data.

BACKGROUND: The increasing complexity and volume of clinical data poses a challenge in the decision-making process. Data visualizations can assist in this process by speeding up the time required to analyze and understand clinical data. Even though empirical experiments show that visualizations facilitate clinical data understanding, a consistent method to assess their effectiveness is still missing. METHODS: The insight-based methodology determines the quality of insights a user acquires from the visualization. Insights receive a value from one to five points based on a domain-specific criteria. Five professional psychiatrists took part in the study using real de-identified clinical data spanning 4 years of medical history. RESULTS: A total of 50 assessments were transcribed and analyzed. Comparing a total of 558 insights using Health Timeline and 576 without, the mean value using the Timeline (1.7) was higher than without (1.26; p<0.01), similarly the cumulative value with the Timeline (11.87) was higher than without (10.96: p<0.01). The average time required to formulate the first insight with the Timeline was higher (13.16 s) than without (7 s; p<0.01). Seven insights achieved the highest possible value using Health Timeline while none were obtained without it. CONCLUSIONS: The Health Timeline effectively improved understanding of clinical data and helped participants recognize complex patterns from the data. By applying the insight-based methodology, the effectiveness of the Health Timeline was quantified, documented and demonstrated. As an outcome of this exercise, we propose the use of such methodologies to measure the effectiveness of visualizations that assist the clinical decision-making process.

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.

Characteristics of patients seen by visiting psychiatrists through Medicare in a rural community mental health service with an established telemedicine service.

OBJECTIVE: We aimed to determine characteristics of patients receiving Medicare-funded face-to-face consultations by psychiatrists in a rural service with an established telemedicine service. METHODS: For this study, 55 case-managed patients from four rural community mental health teams in South Australia provided information through questionnaires regarding psychological symptoms, quality of life, body mass index (BMI) and gave access to their Medicare data. RESULTS: In a logistic regression, it was found that being younger was more likely associated with a Medicare psychiatric consultation ( p<0.05). Participants with at least one recorded visit to a Medicare psychiatrist consultation also tended to have lower levels of psychological distress at the end of the 4-year period, have a higher BMI, and more general practitioner consultations. CONCLUSION: The study provides clinicians and policy makers with preliminary information on the subtle differences in clinical profile of patients seen by Medicare-funded psychiatrists within an established state-managed telepsychiatry service.

Biomarker Case-Detection and Prediction with Potential for Functional Psychosis Screening: Development and Validation of a Model Related to Biochemistry, Sensory Neural Timing and End Organ Performance.

The Mental Health Biomarker Project aimed to discover case-predictive biomarkers for functional psychosis. In a retrospective, cross-sectional study, candidate marker results from 67 highly characterized symptomatic participants were compared with results from 67 gender- and age-matched controls. Urine samples were analyzed for catecholamines, their metabolites, and hydroxylpyrolline-2-one, an oxidative stress marker. Blood samples were analyzed for vitamin and trace element cofactors of enzymes in catecholamine synthesis and metabolism pathways. Cognitive, auditory, and visual processing measures were assessed using a simple 45-min, office-based procedure. Receiver operating curve (ROC) and odds ratio analysis discovered biomarkers for deficits in folate, vitamin D and B6 and elevations in free copper to zinc ratio, catecholamines and the oxidative stress marker. Deficits were discovered in peripheral visual and auditory end-organ function, intracerebral auditory and visual processing speed and dichotic listening performance. Fifteen ROC biomarker variables were divided into five functional domains. Through a repeated ROC process, individual ROC variables, followed by domains and finally the overall 15 set model, were dichotomously scored and tallied for abnormal results upon which it was found that ≥3 out of 5 abnormal domains achieved an area under the ROC curve of 0.952 with a sensitivity of 84% and a specificity of 90%. Six additional middle ear biomarkers in a 21 biomarker set increased sensitivity to 94%. Fivefold cross-validation yielded a mean sensitivity of 85% for the 15 biomarker set. Non-parametric regression analysis confirmed that ≥3 out of 5 abnormally scored domains predicted >50% risk of caseness while 4 abnormally scored domains predicted 88% risk of caseness; 100% diagnostic certainty was reached when all 5 domains were abnormally scored. These findings require validation in prospective cohorts and other mental illness states. They have potential for case-detection, -screening, -monitoring, and -targeted personalized management. The findings unmask unmet needs within the functional psychosis condition and suggest new biological understandings of psychosis phenomenology.

Geography of primary mental health care through the Better Access initiative in South Australia 2006-2010.

OBJECTIVE: To examine how the rates of the use of particular face-to-face primary mental health care services changed in the first 4 years (2006-2010) of the Better Access initiative in both urban and rural regions of South Australia. DESIGN: Time-series analysis of the number of psychology session, psychiatry assessment and general practitioner care plan services recorded in Medicare Australia data. SETTING: South Australia. Pre-existing data set of South Australian residents who accessed Medicare between 2006 and 2010 MAIN OBJECTIVE MEASURE: Number of services per 100 000 population (service rate). RESULTS: Psychology session service rates increased in all regions, but continued to follow a 'location gradient', being higher in areas closer to Adelaide and lower in areas more distant from Adelaide. Psychiatry assessment service rates increased in Adelaide but did not change in other regions. Rates in remote areas were subject to substantial variation over time. General practitioner care plan service rates increased in Adelaide and in the Riverland, but declined in the Murray Mallee region. CONCLUSIONS: Overall, service rates increased in Adelaide and nearby regions, but the results for rural and remote regions were mixed. Possible explanations for the geographical variability include population characteristics (such as socio-economic status), methods of service delivery (visiting practitioners, telepsych), the relative proportion of total health services provided by general practitioners versus other practitioners, or real variations in the need for primary mental health services.

Changes in monthly unemployment rates may predict changes in the number of psychiatric presentations to emergency services in South Australia.

BACKGROUND: To determine the extent to which variations in monthly Mental Health Emergency Department (MHED) presentations in South Australian Public Hospitals are associated with the Australian Bureau of Statistics (ABS) monthly unemployment rates. METHODS: Times series modelling of relationships between monthly MHED presentations to South Australian Public Hospitals derived from the Integrated South Australian Activity Collection (ISAAC) data base and the ABS monthly unemployment rates in South Australia between January 2004-June 2011. RESULTS: Time series modelling using monthly unemployment rates from ABS as a predictor variable explains 69% of the variation in monthly MHED presentations across public hospitals in South Australia. Thirty-two percent of the variation in current month's male MHED presentations can be predicted by using the 2 months' prior male unemployment rate. Over 63% of the variation in monthly female MHED presentations can be predicted by either male or female prior monthly unemployment rates. CONCLUSIONS: The findings of this study highlight that even with the relatively favourable economic conditions, small shifts in monthly unemployment rates can predict variations in monthly MHED presentations, particularly for women. Monthly ABS unemployment rates may be a useful metric for predicting demand for emergency mental health services.

Biomarkers of a five-domain translational substrate for schizophrenia and schizoaffective psychosis.

BACKGROUND: The Mental Health Biomarker Project (2010-2014) selected commercial biochemistry markers related to monoamine synthesis and metabolism and measures of visual and auditory processing performance. Within a case-control discovery design with exclusion criteria designed to produce a highly characterised sample, results from 67 independently DSM IV-R-diagnosed cases of schizophrenia and schizoaffective disorder were compared with those from 67 control participants selected from a local hospital, clinic and community catchment area. Participants underwent protocol-based diagnostic-checking, functional-rating, biological sample-collection for thirty candidate markers and sensory-processing assessment. RESULTS: Fifteen biomarkers were identified on ROC analysis. Using these biomarkers, odds ratios, adjusted for a case-control design, indicated that schizophrenia and schizoaffective disorder were highly associated with dichotic listening disorder, delayed visual processing, low visual span, delayed auditory speed of processing, low reverse digit span as a measure of auditory working memory and elevated levels of catecholamines. Other nutritional and biochemical biomarkers were identified as elevated hydroxyl pyrroline-2-one as a marker of oxidative stress, vitamin D, B6 and folate deficits with elevation of serum B12 and free serum copper to zinc ratio. When individual biomarkers were ranked by odds ratio and correlated with clinical severity, five functional domains of visual processing, auditory processing, oxidative stress, catecholamines and nutritional-biochemical variables were formed. When the strengths of their inter-domain relationships were predicted by Lowess (non-parametric) regression, predominant bidirectional relationships were found between visual processing and catecholamine domains. At a cellular level, the nutritional-biochemical domain exerted a pervasive influence on the auditory domain as well as on all other domains. CONCLUSIONS: The findings of this biomarker research point towards a much-required advance in Psychiatry: quantification of some theoretically-understandable, translationally-informative, treatment-relevant underpinnings of serious mental illness. This evidence reveals schizophrenia and schizoaffective disorder in a somewhat different manner, as a conglomerate of several disorders many of which are not currently being assessed-for or treated in clinical settings. Currently available remediation techniques for these underlying conditions have potential to reduce treatment-resistance, relapse-prevention, cost burden and social stigma in these conditions. If replicated and validated in prospective trials, such findings will improve progress-monitoring and treatment-response for schizophrenia and schizoaffective disorder.

Impact of a psychiatric unit's daily discharge rates on emergency department flow.

OBJECTIVE: To investigate relationships between time spent in the emergency department (ED) in patients requiring admission to the psychiatric ward, the day of the week of presentation and the daily number of discharges from the psychiatric ward. METHOD: Retrospective analysis of patient flow as a function of day of week, time of day (a.m., p.m.), number of patients requiring admission and number of ward discharges over a one-year period, for all mental health related presentations to the ED of the Queen Elizabeth Hospital in Adelaide, South Australia, before their admission to the psychiatric inpatient facility. RESULTS: The time spent by patients in the ED waiting for admission to the psychiatric ward was significantly greater on weekends. There were significantly fewer discharges from the psychiatric ward during weekends compared with weekdays. The average time spent by patients in the ED requiring admission to the psychiatric ward for those days when there were vacant beds was 17.9 hours (SD=14.5). More people presented to the ED with a psychiatric diagnosis in the afternoons. There was a significant inverse correlation between the time spent by patients in the ED requiring admission to the psychiatric ward per day and the number of discharges from the psychiatric ward per day. CONCLUSION: These findings demonstrate that patient flow is significantly slower on weekends because of fewer discharges from the ward, leading to longer times spent in the ED before ward transfer. Waiting times in the ED were very substantially greater than the proposed 4-hour target even when vacant beds were available, raising considerable doubt about that target being realistic for psychiatric patients.

Implications of bed reduction in an acute psychiatric service.

OBJECTIVE: To evaluate the impact of psychiatric inpatient bed closures, accompanied by a training program aimed at enhancing team effectiveness and incorporating data-driven practices, in a mental health service. DESIGN AND SETTING: Retrospective comparison of the changes in services within three consecutive financial years: baseline period - before bed reduction (2006-07); observation period - after bed reduction (2007-08); and intervention period - second year after bed reduction (2008-09). The study was conducted at Cramond Clinic, Queen Elizabeth Hospital, Adelaide. MAIN OUTCOME MEASURES: Length of stay, 28-day readmission rates, discharges, bed occupancy rates, emergency department (ED) presentations, ED waiting time, seclusions, locality of treatment, and follow-up in the community within 7days. RESULTS: Reduced bed numbers were associated with reduced length of stay, fewer referrals from the community and subsequently shorter waiting times in the ED, without significant change in readmission rates. A higher proportion of patients was treated in the local catchment area, with improved community follow-up and a significant reduction in inpatient seclusions. CONCLUSION: Our findings should reassure clinicians concerned about psychiatric bed numbers that service redesign with planned bed reductions will not necessarily affect clinical care, provided data literacy and team training programs are in place to ensure smooth transition of patients across ED, inpatient and community services.

Estimulaçäo DDD com eletrodo único usando estimulaçäo atrial bifásica simultânea: primeiros resultados clínicos / Single lead DDD pacing with overlapping biphasing atrial stimulation: first clinical results

A estimulaçäo de dupla câmara (DDD) com eletrodo único usando eletrodo atrial flutuante está limitada em funçäo dos altos limiares encontrados para captura atrial. Avaliamos um sistema novo de cabo eletrodo para estimulaçao atrial que utiliza dois anéis atriais com pulsos de onda quadrada simultâneos unipolares lançados com polaridade oposta. O primeiro pulso é aplicado ao pólo distal do eletrodo e é positivo, o segundo pulso é aplicado ao pólo proximal do eletrodo e é negativo, ambos em relaçäo à carcaça do gerador. O atraso entre os dois pulsos é programável entre 0,0 ms e 1,0 ms. A distância entre os anéis a nível atrial é de 10 mm e a distância entre o pólo distal atrial e o eletrodo ventricular unipolar pode ser selecionado entre 11,13 e 15 cm. O posicionamento dos anéis a nével atrial é selecionado de acordo com a medida do limiar de estimulaçäo do pulso bifásico simultâneo, incluindo manobras respiratóris para confirmar a captura/sense contínuos. O gerador de pulso tem uma única conexäo para o eletrodo e a capacidade de aplicar os pulsos programáveis de onda quadrada com polaridade opostas, com atrasos programáveis de 0,0 a 1,0 ms. O gerador pode ser programado para a estimulaçäo VDD com eletrodo único. Este sistema foi implantado em 4 pacientes com bloqueio AV total e funçäo sinusal normal. Os limiares de estimulaçäo atrial e diafragmático foram medidos com várias configuraçöes do pulso, larguras e atrasos, intra e pós-operatórios. A porçäo média do átrio direito foi selecionada como a melhor posiçäo para os anéis atriais com captura contínua durante inspiraçäo profunda. Os pacientes tiveram alta com o gerador programado no modo VDD. A evoluçäo de 30 dias após o implante mostrou perda de captura atrial transitória durante inspiraçäo em 1 paciente, apesar da ausência de outras variaçöes no limiar, comparado com a evoluçäo de 48 horas pós implante. Em conclusäo, a estimulaçäo bifásica simultânea reduz o limiar de estimulaçäo atrial, ao mesmo tempo em que mantém o sense atrial adequado com uma margem de segurança em relaçäo à estimulaçäo diafragmática.