REAL-WORLD DATA OVER TIME FOR TRANSCATHETER AND SURGICAL AORTIC VALVE INTERVENTIONS IN QUÉBEC

2021 ◽  
Vol 37 (10) ◽  
pp. S79
Author(s):  
D de Verteuil ◽  
L Azzi ◽  
L Lambert ◽  
B Daneault ◽  
E Dumont ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Over Time

scholarly journals Concept Drift Detection in Dynamic Probabilistic Relational Models

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Nils Finke ◽  
Tanya Braun ◽  
Marcel Gehrke ◽  
Ralf Möller
Keyword(s):  
Real World ◽  
Joint Distribution ◽  
Concept Drift ◽  
Probability Distributions ◽  
Real World Data ◽  
Relational Models ◽  
World Data ◽  
Probabilistic Relational Models ◽  
Changes Over Time ◽  
Over Time

Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.

scholarly journals Health Economics Evidence in Peer-Reviewed Journals: Increase in Reporting of Real-World Data Over Time in Type 2 Diabetes

2017 ◽  
Vol 20 (9) ◽  
pp. A487
Author(s):  
Y Huang ◽  
TE Hartog ◽  
R Vaghjiani ◽  
N Patterson ◽  
H Van Lier ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Economics ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Over Time

scholarly journals A91 ECONOMIC EVALUATION OF A FECAL MICROBIOTA TRANSPLANTATION PROGRAM FOR RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN ALBERTA

2021 ◽  
Vol 4 (Supplement_1) ◽  
pp. 63-65
Author(s):  
D Y Yang ◽  
T Mullie ◽  
H Sun ◽  
L Russell ◽  
B Roach ◽  
...  
Keyword(s):  
Healthcare Cost ◽  
Real World ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
Control Group ◽  
Real World Data ◽  
World Data ◽  
The Cost ◽  
The Impact ◽  
Over Time

Abstract Background Fecal microbiota transplantation (FMT) is the most effective therapy for recurrent C. difficile infection. Although studies using statistical modeling have shown FMT to be cost-effective, real-world data is lacking. Aims To assess the impact of FMT program on the healthcare cost of recurrent C. difficile infections using real-world data from Alberta’s public healthcare system. Methods C. difficile infection patients were identified through provincial laboratory database with positive C. difficile results in Edmonton, Alberta between 2009–16. If an initial positive test was followed by ≧2 positive tests within 183 days, an individual was categorized as recurrent C. difficile infection (RCDI). Otherwise, non-recurrent C. difficile infection (non-RCDI) was assigned. Since the Edmonton FMT program was established in 2013, patients were further divided into pre-FMT (2009–12) and post-FMT (2013–16) eras. This divided patients into four study groups as outlined in Table 1. Administrative data, including inpatient stays, ambulatory or emergency room visits, outpatient prescriptions, and physician billings, were extracted. A cost of $389 was assigned to each FMT procedure to account for cost of donor screening and sample preparation. A difference in differences (DID) approach, a tool which estimates the effect of a treatment by comparing outcome difference between treatment group and control group over time, was used to analyze the impact of FMT program on the cost of RCDI. Non-RCDI patients were used as control group to account for changes in treatment costs over time. Ordinary least squares regression, with log-transformed healthcare cost as the dependent variable, was used for the analysis. Results 4717 non-RCDI and 548 RCDI patients were identified and divided into the 4 groups (Table 1). RCDI patients were significantly older than non-RCDI patients (71.13 vs 62.49; P < 0.001). After adjusting for differences in age, sex, and baseline healthcare utilization, cost for RCDI patients were significantly lower relative to costs for non-RCDI patients in the post-FMT era. Cost of non-RCDI increased by $5,300.08 between the pre- and post-FMT eras, while the cost of RCDI decreased by $7,654.92 in the same time frame (Table 2). FMT program was estimated to have saved $12,954 annually for RCDI patients at mean age, sex, and baseline cost of our overall sample. Conclusions Our data suggest that the healthcare cost of RCDI has decreased with the introduction of an FMT program. Funding Agencies Alberta Health Services, University of Alberta Hospital Foundation

scholarly journals Real-world data suggest antibody positivity to SARS-CoV-2 is associated with a decreased risk of future infection

2020 ◽  
Author(s):  
Raymond A. Harvey ◽  
Jeremy A. Rassen ◽  
Carly A. Kabelac ◽  
Wendy Turenne ◽  
Sandy Leonard ◽  
...  
Keyword(s):  
Real World ◽  
Antibody Test ◽  
The United States ◽  
Test Results ◽  
Real World Data ◽  
World Data ◽  
Positive Antibody ◽  
Laboratory Test Results ◽  
Over Time

AbstractImportanceThere is limited evidence regarding whether the presence of serum antibodies to SARS-CoV-2 is associated with a decreased risk of future infection. Understanding susceptibility to infection and the role of immune memory is important for identifying at-risk populations and could have implications for vaccine deployment.ObjectiveThe purpose of this study was to evaluate subsequent evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among individuals who are antibody-positive compared with those who are antibody-negative, using real-world data.DesignThis was an observational descriptive cohort study.ParticipantsThe study utilized a national sample to create cohorts from a de-identified dataset composed of commercial laboratory test results, open and closed medical and pharmacy claims, electronic health records, hospital billing (chargemaster) data, and payer enrollment files from the United States. Patients were indexed as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test recorded in the database. Patients with more than 1 antibody test on the index date where results were discordant were excluded.Main Outcomes/MeasuresPrimary endpoints were index antibody test results and post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, as measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, such as recorded signs and symptoms or prior evidence of COVID-19 (diagnoses or NAAT+) and recorded comorbidities.ResultsWe included 3,257,478 unique patients with an index antibody test. Of these, 2,876,773 (88.3%) had a negative index antibody result, 378,606 (11.6%) had a positive index antibody result, and 2,099 (0.1%) had an inconclusive index antibody result. Patients with a negative antibody test were somewhat older at index than those with a positive result (mean of 48 versus 44 years). A fraction (18.4%) of individuals who were initially seropositive converted to seronegative over the follow up period. During the follow-up periods, the ratio (CI) of positive NAAT results among individuals who had a positive antibody test at index versus those with a negative antibody test at index was 2.85 (2.73 - 2.97) at 0-30 days, 0.67 (0.6 - 0.74) at 31-60 days, 0.29 (0.24 - 0.35) at 61-90 days), and 0.10 (0.05 - 0.19) at >90 days.ConclusionsPatients who display positive antibody tests are initially more likely to have a positive NAAT, consistent with prolonged RNA shedding, but over time become markedly less likely to have a positive NAAT. This result suggests seropositivity using commercially available assays is associated with protection from infection. The duration of protection is unknown and may wane over time; this parameter will need to be addressed in a study with extended duration of follow up.Key PointsQuestionCan real-world data be used to evaluate the comparative risk of SARS-CoV-2 infection for individuals who are antibody-positive versus antibody-negative?FindingOf patients indexed on a positive antibody test, 10 of 3,226 with a NAAT (0.3%) had evidence of a positive NAAT > 90 days after index, compared with 491 of 16,157 (3.0%) indexed on a negative antibody test.MeaningIndividuals who are seropositive for SARS-CoV-2 based on commercial assays may be at decreased future risk of SARS-CoV-2 infection.

scholarly journals Learning Continuous Time Bayesian Networks in Non-stationary Domains

2018 ◽  
Author(s):  
Simone Villa ◽  
Fabio Stella
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Real World ◽  
Continuous Time ◽  
Real World Data ◽  
Change Over Time ◽  
World Data ◽  
Continuous Time Bayesian Networks ◽  
Continuous Time Bayesian Network ◽  
Over Time

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

Sign in / Sign up

Export Citation Format

Share Document