Symptom trajectories in patients with panic disorder in a primary care intervention: Results from a randomized controlled trial (PARADISE)

This analysis aims to identify and characterize symptom trajectories in primary care patients with panic disorder with/without agoraphobia (PD/AG) who participated in a primary care team based training involving elements of cognitive behavioural therapy (CBT). Growth Mixture Modeling was used to identify different latent classes of change in patients with PD/AG (N = 176) who underwent treatment including CBT elements. We identified three patient classes with distinct similar trajectories. Class 1 (n = 58, mean age: 46.2 years +/- 13.4 years, 81% women) consisted of patients with an initially high symptom burden, but symptoms declined constantly over the intervention period. Symptoms of patients in class 2 (n = 89, mean age: 44.2 years +/- 14.5 years, 67.4% women) declined rapidly at the beginning, then patients went into a plateau-phase. The third class (n = 29, mean age: 47.0 years +/- 12.4 years, 65.5% women) was characterized by an unstable course and had the worse outcome. Our findings show that only a minority did not respond to the treatment. To identify this minority and refer to a specialist would help patients to get intensive care in time

Flexible Data Access in a Cloud based on Freshness Requirements

Data clouds are newly emerging environments in which commercial providers manage large volumes of data with individual quality of service (QoS) guarantees per customer. These guarantees mainly include keeping several replicas of each data item in different distributed data centers for availability purposes. However, as the cost of maintaining several updateable replicas per data object is very high, cloud providers usually offer a limited number of synchronously updated replicas and several read-only replicas that are updated in a lazy way and thus might hold stale data. QoS agreements may also include the maintenance of dedicated archives, i.e., copies of data which are frozen at some point in time. In this paper we present Re:FRESHiT, our novel approach to freshness-aware cloud data management that allows providers to offer a variety of read operations with different semantics. These read operations can be supported by a read-only site using a stale replica, or by exploiting one of several locally managed archives. We discuss in detail how the replicas are organized based on their freshness and how refresh and defresh of individual replicas is provided in a completely distributed way. Finally, we present the results of a performance evaluation of Re:FRESHiT in a data cloud setting

Replicated Data Management in the Grid: The Re:GRIDiT Approach

Grid environments more and more target novel domains suchas eScience, eHealth or digital libraries that feature a varietyof data-intensive applications. Consequently, issues relatedto data management in Grids are becoming increasingly important.In terms of data management, the Grid allowskeeping a large number of replicas of data objects, possiblywith different versions or levels of freshness, to allow for ahigh degree of availability, reliability and performance so asto best meet the needs of users and applications. At thesame time, the seamless integration of replication managementinto the Grid while taking into account its special characteristics,needs to be done without any central componentfor managing data or metadata. In this paper, we report onthe ongoing Re:GRIDiT project which aims at addressingall the above requirements. Re:GRIDiT distinguishes betweenpotentially many updateable and read-only replicaswhich can be distributed across a Grid environment. First,Re:GRIDiT provides new protocols for the correct synchronizationof concurrent updates to different updateable replicasand their subsequent propagation in a completely distributedway. Second, Re:GRIDiT takes into account thesemantics of the data which is managed in the Grid: mutabledata can be subject to updates; immutable data, in turn,cannot be changed once created, but may be subject to versioncontrol. Third, Re:GRIDiT will be dynamic in a waythat according to the current load, new replicas (updateableor read-only) can be created or removed on demand. Fourth, Re:GRIDiT will provide read-only transactions the full flexibilityto specify the freshness (for mutable data) or versionnumber (for immutable data) – which is particularly usefulin order to trade accuracy for performance in the access todata in the Grid

Fine-Grained Lazy Replication with Strict Freshness and Correctness Guarantees

Eager replication management is known to generate unacceptable performance as soon as the update rate or the number of replicas increases. Lazy replication protocols tackle this problem by decoupling transaction execution from the propagation of new values to replica sites while guaranteeing a correct and more efficient transaction processing and replica maintenance. However, they impose several restrictions on transaction models that are often not valid in practical database settings, e.g., they require that each transaction executes at its initiation site and/or are restricted to full replication schemes. Also, the protocols cannot guarantee that the transactions will always see the freshest available replicas. This paper presents a new lazy replication protocol called PDBREP that is free of these restrictions while ensuring one copy serializable executions. The protocol exploits the distinction between read-only and general transactions and works with arbitrary physical data organizations such as partitioning and striping as well as different replica granularities. It does not require that each read-only transaction executes entirely at its initiation site. Hence, each read-only site need not contain a fully replicated database. PDBREP furthermore generalizes the notion of freshness to finer data granules than entire databases. Beside its architectural advantages, experiments revealed that PDBREP outperforms related lazy replication techniques

Re:GRIDiT – Coordinating Distributed Update Transactions on Replicated Data in the Grid

The recent proliferation of Grid environments foreScience applications led to common computing infrastructureswith nearly unlimited storage capabilities. Interms of data management, the Grid allows keeping alarge number of replicas of data objects to allow for ahigh degree of availability, reliability and performance.Due to the particular characteristics of the Grid,especially due to the absence of a global coordinator,dealing with many updateable replicas per data objecturgently requires new protocols for the synchronizationof updates and their subsequent propagation. Currentlythere is no protocol which can be seamlessly applied toa data Grid environment without impacting correctnessand/or overall performance. In this paper we addressthe problem of replication in the Data Grid in thepresence of updates. We have designed the Re:GRIDiTprotocol that focuses on the correct synchronization ofupdates to several replicas in the Grid in a completelydistributed way, extending well-established databasereplication techniques. Globally correct execution isprovided by communication between transactions andsites. Re:GRIDiT takes into account the special characteristicsof eScience applications such as the distinctionbetween mutable objects, that can be updated byusers and immutable objects. Finally, we provide a detailedevaluation of the performance of the Re:GRIDiTprotocol when being applied at Grid scale

Cost-effectiveness of Practice Team-Supported Exposure Training for Panic Disorder and Agoraphobia in Primary Care: a Cluster-Randomized Trial

Background: Primary care is the main treatment setting for panic disorder and should be supplemented by collaborative care programs. However, shortage of mental health professionals prevents collaborative care programs from being effectively implemented. The PARADISE study showed the efficacy of a self-managed, cognitive-behavioural therapy (CBT)-oriented exposure training for patients with panic disorder with or without agoraphobia in primary care delivered by the family practice team. Objective: To assess the cost-effectiveness of the PARADISE intervention. Design: Cost-effectiveness analysis from the societal perspective based on data from a cluster-randomized controlled trial over a time horizon of 12 months. Participants: Four hundred nineteen adult panic disorder patients with or without agoraphobia. Interventions: A self-managed, CBT-oriented exposure training for patients with panic disorder with or without agoraphobia in primary care delivered by the primary care practice team in comparison to routine care.Main Measures: Total costs from the societal perspective. Direct costs and disease-specific costs. Quality-adjusted life years based on the EQ-5D-3L. Incremental cost-effectiveness ratios and cost-effectiveness acceptability curves.Key Results: Patients in the intervention group caused lower costs (mean, Euro1017; 95% confidence interval [-Euro3306; Euro1272]; p=0.38) and gained on average more QALY (mean, 0.034 QALY (95% confidence interval [0.005; 0.062]; p=0.02). Therefore, the intervention dominated the control treatment. The probability of cost-effectiveness of the intervention at a willingness-to-pay margin of Euro 50,000 per QALY was 96%. Results from supplementary analyses considering direct or disease-specific costs instead of total costs showed comparable results. Conclusion: The PARADISE intervention is cost effective. This conclusion is valid for total costs, generic health care (direct) costs, disease-specific health care costs.Trial Registration: German Clinical Trials Register: DRKS00004386 Current Controlled Trials: ISRCTN6466929

Patient characteristics and changes in anxiety symptoms in patients with panic disorder: Post-hoc analysis of the PARADIES cluster randomised trial

Anxiety disorders are among the most common mental health problems in primary care. The PARADIES (Patient Activation foR Anxiety DIsordErS) intervention combined elements of cognitive behavioural therapy with case management and has demonstrated efficacy. Our aim was to explore patient characteristics, which may influence the course of anxiety symptoms over a 12 months period. Multiple linear regression was used to quantify associations of baseline characteristics (demographics, clinical parameters, medication use) with changes in anxiety symptoms as measured by the Beck anxiety inventory. Treatment modalities (e.g. adherence to appointment schedules) were considered as confounders. We examined univariate associations between dependent and independent variables before considering all independent variables in a multivariate final model. To find the best model to explain BAI score changes, we performed step-wise selection of independent variables based on Akaike information criteria. We tested for interaction terms between treatment allocation (intervention vs control) and independent variables using the multivariate model. We repeated these analyses in control vs intervention groups separately. From the original trial (N = 419), 236 patients (56.3%) were included. In the multivariate model, receiving the intervention (p<0.001), higher anxiety symptom severity (p<0.001) and longer illness duration at baseline (p = 0.033) were significantly associated with changes in anxiety symptom severity to the better while depression severity at baseline (p<0.001) was significantly associated with changes in anxiety symptoms to the worse. In stratified analyses, the control group showed significant associations between depression symptom severity and illness duration with anxiety symptom changes while baseline severity of anxiety symptoms remained significantly associated with anxiety symptom changes in both groups. A brief primary-care-based exposure training combined with case management is effective in a broad range of patients with panic disorder with/without agoraphobia, including those with longer illness duration and co-existing symptoms of depression at baseline