Deep Brain Stimulation for Parkinson's Disease with Early Motor Complications:A UK Cost-Effectiveness Analysis

International audienceBackground: Parkinson’s disease (PD) is a debilitating illness associated with considerable impairment of quality of life and substantial costs to health care systems. Deep brain stimulation (DBS) is an established surgical treatment option for some patients with advanced PD. The EARLYSTIM trial has recently demonstrated its clinical benefit also in patients with early motor complications. We sought to evaluate the cost-effectiveness of DBS, compared to best medical therapy (BMT), among PD patients with early onset of motor complications, from a United Kingdom (UK) payer perspective.Methods: We developed a Markov model to represent the progression of PD as rated using the Unified Parkinson's Disease Rating Scale (UPDRS) over time in patients with early PD. Evidence sources were a systematic review of clinical evidence; data from the EARLYSTIM study; and a UK Clinical Practice Research Datalink (CPRD) dataset including DBS patients. A mapping algorithm was developed to generate utility values based on UPDRS data for each intervention. The cost-effectiveness was expressed as the incremental cost per quality-adjusted life-year (QALY). One-way and probabilistic sensitivity analyses were undertaken to explore the effect of parameter uncertainty.Results: Over a 15-year time horizon, DBS was predicted to lead to additional mean cost per patient of £26,799 compared with BMT (£73,077/patient versus £46,278/patient) and an additional mean 1.35 QALYs (6.69 QALYs versus 5.35 QALYs), resulting in an incremental cost-effectiveness ratio of £19,887 per QALY gained with a 99% probability of DBS being cost-effective at a threshold of £30,000/QALY. One-way sensitivity analyses suggested that the results were not significantly impacted by plausible changes in the input parameter values.Conclusion: These results indicate that DBS is a cost-effective intervention in PD patients with early motor complications when compared with existing interventions, offering additional health benefits at acceptable incremental cost. This supports the extended use of DBS among patients with early onset of motor complications

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Unification of temporary storage in the nodekernel architecture

Efficiently exchanging temporary data between tasks is critical to the end-to-end performance of many data processing frameworks and applications. Unfortunately, the diverse nature of temporary data creates storage demands that often fall between the sweet spots of traditional storage platforms, such as file systems or key-value stores. We present NodeKernel, a novel distributed storage architecture that offers a convenient new point in the design space by fusing file system and key-value semantics in a common storage kernel while leveraging modern networking and storage hardware to achieve high performance and cost-efficiency. NodeKernel provides hierarchical naming, high scalability, and close to bare-metal performance for a wide range of data sizes and access patterns that are characteristic of temporary data. We show that storing temporary data in Crail, our concrete implementation of the NodeKernel architecture which uses RDMA networking with tiered DRAM/NVMe-Flash storage, improves NoSQL workload performance by up to 4.8× and Spark application performance by up to 3.4×. Furthermore, by storing data across NVMe Flash and DRAM storage tiers, Crail reduces storage cost by up to 8× compared to DRAM-only storage systems

Unification of temporary storage in the nodekernel architecture

Efficiently exchanging temporary data between tasks is critical to the end-to-end performance of many data processing frameworks and applications. Unfortunately, the diverse nature of temporary data creates storage demands that often fall between the sweet spots of traditional storage platforms, such as file systems or key-value stores. We present NodeKernel, a novel distributed storage architecture that offers a convenient new point in the design space by fusing file system and key-value semantics in a common storage kernel while leveraging modern networking and storage hardware to achieve high performance and cost-efficiency. NodeKernel provides hierarchical naming, high scalability, and close to bare-metal performance for a wide range of data sizes and access patterns that are characteristic of temporary data. We show that storing temporary data in Crail, our concrete implementation of the NodeKernel architecture which uses RDMA networking with tiered DRAM/NVMe-Flash storage, improves NoSQL workload performance by up to 4.8× and Spark application performance by up to 3.4×. Furthermore, by storing data across NVMe Flash and DRAM storage tiers, Crail reduces storage cost by up to 8× compared to DRAM-only storage systems

Results of univariable logistic regression and receiver operating characteristics analysis of a cow being lame (numerical rating system according to Flower and Weary [38], NRS ≥ 2.5) using different RumiWatch noseband sensor and accelerometer (RumiWatch, ITIN+HOCH GmbH, Fütterungstechnik, Liestal, Switzerland) variables as predictors on the cutoff value with highest sensitivity + specificity.

<p>Results of univariable logistic regression and receiver operating characteristics analysis of a cow being lame (numerical rating system according to Flower and Weary [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155796#pone.0155796.ref038" target="_blank">38</a>], NRS ≥ 2.5) using different RumiWatch noseband sensor and accelerometer (RumiWatch, ITIN+HOCH GmbH, Fütterungstechnik, Liestal, Switzerland) variables as predictors on the cutoff value with highest sensitivity + specificity.</p