| Title |
Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients
|
|---|---|
| Published in |
Breast Cancer Research and Treatment, June 2016
|
| DOI | 10.1007/s10549-016-3850-8 |
| Pubmed ID | |
| Authors |
Alexander Hein, Paul Gass, Christina Barbara Walter, Florin-Andrei Taran, Andreas Hartkopf, Friedrich Overkamp, Hans-Christian Kolberg, Peyman Hadji, Hans Tesch, Johannes Ettl, Rachel Wuerstlein, Debra Lounsbury, Michael P. Lux, Diana Lüftner, Markus Wallwiener, Volkmar Müller, Erik Belleville, Wolfgang Janni, Tanja N. Fehm, Diethelm Wallwiener, Thomas Ganslandt, Matthias Ruebner, Matthias W. Beckmann, Andreas Schneeweiss, Peter A. Fasching, Sara Y. Brucker |
| Abstract |
As breast cancer is a diverse disease, clinical trials are becoming increasingly diversified and are consequently being conducted in very small subgroups of patients, making study recruitment increasingly difficult. The aim of this study was to assess the use of data from a remote data entry system that serves a large national registry for metastatic breast cancer. The PRAEGNANT network is a real-time registry with an integrated biomaterials bank that was designed as a scientific study and as a means of identifying patients who are eligible for clinical trials, based on clinical and molecular information. Here, we report on the automated use of the clinical data documented to identify patients for a clinical trial (EMBRACA) for patients with metastatic breast cancer. The patients' charts were assessed by two independent physicians involved in the clinical trial and also by a computer program that tested patients for eligibility using a structured query language script. In all, 326 patients from two study sites in the PRAEGNANT network were included in the analysis. Using expert assessment, 120 of the 326 patients (37 %) appeared to be eligible for inclusion in the EMBRACA study; with the computer algorithm assessment, a total of 129 appeared to be eligible. The sensitivity of the computer algorithm was 0.87 and its specificity was 0.88. Using computer-based identification of patients for clinical trials appears feasible. With the instrument's high specificity, its application in a large cohort of patients appears to be feasible, and the workload for reassessing the patients is limited. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 17% |
| Researcher | 4 | 17% |
| Other | 3 | 13% |
| Student > Bachelor | 3 | 13% |
| Professor | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 6 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 22% |
| Biochemistry, Genetics and Molecular Biology | 2 | 9% |
| Economics, Econometrics and Finance | 2 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 6 | 26% |
| Unknown | 6 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 June 2016.
All research outputs
#20,332,117
of 22,876,619 outputs
Outputs from Breast Cancer Research and Treatment
#4,110
of 4,659 outputs
Outputs of similar age
#295,530
of 343,019 outputs
Outputs of similar age from Breast Cancer Research and Treatment
#80
of 108 outputs
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