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Hein, Alexander; Gass, Paul; Walter, Christina Barbara; Taran, Florin-Andrei; Hartkopf, Andreas; Overkamp, Friedrich; Kolberg, Hans-Christian; Hadji, Peyman; Tesch, Hans; Ettl, Johannes; Wuerstlein, Rachel; Lounsbury, Debra; Lux, Michael P.; Lüftner, Diana; Wallwiener, Markus; Müller, Volkmar; Belleville, Erik; Janni, Wolfgang; Fehm, Tanja N.; Wallwiener, Diethelm; Ganslandt, Thomas; Ruebner, Matthias; Beckmann, Matthias W.; Schneeweiss, Andreas; Fasching, Peter A.; Brucker, Sara Y. (2016): Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients. In: Breast Cancer Research and Treatment, Vol. 158, No. 1: pp. 59-65
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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.