A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Selection of Temporal Lags When Modeling Economic and Financial Processes.

This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models.

Testing for nonlinear dependence in financial markets.

This article addresses the question of improving the detection of nonlinear dependence by means of recently developed nonparametric tests. To this end a generalized version of BDS test and a new test based on symbolic dynamics are used on realizations from a well-known artificial market for which the dynamic equation governing the market is known. Comparisons with other tests for detecting nonlinearity are also provided. We show that the test based on symbolic dynamics outperforms other tests with the advantage that it depends only on one free parameter, namely the embedding dimension. This does not hold for other tests for nonlinearity.

A single apheresis procedure in the donor may be enough to complete an allograft using the "Mexican method" of non-ablative allografting.

BACKGROUND: Since 1999, in Mexico we have been using a regimen to conduct allografts that involves non-myeloablative conditioning and peripheral blood stem cells (PBSC) and have introduced some changes with the main goal of decreasing the cost of the procedure. MATERIALS AND METHODS: We analysed the salient apheresis features of a group of 175 allogeneic peripheral blood stem cell transplants conducted in two institutions in a 7-year period. The grafts were conducted using the "Mexican" non-myelo ablative conditioning regimen employing oral busulphan, i.v. cyclophosphamide and i.v. fludarabine. In all instances, the apheresis machine employed was the Baxter CS3000 Plus and donors were mobilised with filgrastim. The apheresis procedures were performed on days 0, +1 and +2, the end-point of collection being 5,000 mL of blood/m2 in each procedure. Three apheresis sessions were planned but the number was adjusted according to the cell yield. RESULTS: The final number of allografted CD34 cells ranged between 0.5 and 25.4 x 10(6)/Kg of the recipient's body weight (median, 5.2 x 10(6)/Kg). One to three apheresis procedures were needed to obtain a product containing more than 0.5 x 10(6) CD34 cells/Kg of the recipient, the median being two procedures; in 72 cases (41%) a single apheresis procedure was sufficient to obtain the target number of CD34 cells. The volumes of apheresis ranged between 50 and 600 mL (median, 400 mL). CONCLUSIONS: Since the median cost of each apheresis procedure is 900 USD, the fact that two apheresis procedures was spared in 72 cases and one apheresis was spared in another 65 cases, led to a total saving of approximately 188,100 USD. It can be concluded that, in many cases, allogeneic transplants can be completed with a single apheresis session and that there are considerable financial benefits from this practice.

Exploring the impact of complications on length of stay in major surgery diagnosis-related groups.

STUDY OBJECTIVE: To analyze, in terms of the length of stay (LOS), the use of resources by patients classified under surgical diagnosis-related groups (DRGs) with complication and/or comorbidity (DRGCCs), divided into subgroups where complications were and were not detected, and to explore the repercussions on hospital reimbursement. DESIGN: The Complication Screening Program(Copyright ) (CSP) was used to divide the patients in 14 DRGCCs into subgroups with and without complications, and to compare the LOS between the subgroups and with the equivalent DRG without CC. The ratios between the LOS and the relative weight of the DRGs for reimbursement were also compared. SETTING: The population hospitalized for major surgery between 1995 and 1999 in the Hospital of Mataró, Catalonia, Spain. PARTICIPANTS: Patients (4227) hospitalized for major surgery. MAIN RESULT: The percentage of complications identified by the CSP ranged from 17.5% to 52.4%. The LOS of the DRGCCs was almost twice as long as in the DRGs without CC; 2.48 times greater if the DRGCC was flagged by the CSP and 70% greater if it was not. On average, the DRGCCs selected were reimbursed at a rate 93% higher than their counterpart DRGs without CC. In 11 of the 14 DRG pairs with or without CC, the reimbursement ratio was lower than the LOS ratio. CONCLUSIONS: DRGCCs can be classified into clearly differentiated groups based on the presence or absence of CSP-flagged complications. CSP-flagged complications produce an increase in LOS greater than the increase in the relative weights for reimbursement.