An electronic health record text mining tool to collect real-world drug treatment outcomes
| Autor: | Kim B. Gombert-Handoko, Henk-Jan Guchelaar, Sylvia A van Laar, Juliette Zwaveling |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Time Factors MEDLINE Antineoplastic Agents 030226 pharmacology & pharmacy Article 03 medical and health sciences Drug treatment 0302 clinical medicine Text mining Renal cell carcinoma Internal medicine medicine Data Mining Electronic Health Records Humans Pharmacology (medical) Carcinoma Renal Cell Aged Natural Language Processing Retrospective Studies Pharmacology Data collection business.industry Research Data Collection Reproducibility of Results Articles medicine.disease Confidence interval Kidney Neoplasms Progression-Free Survival Data extraction 030220 oncology & carcinogenesis Cohort Female business Software |
| Zdroj: | Clinical Pharmacology and Therapeutics Clinical Pharmacology & Therapeutics, 108(3), 644-652. WILEY |
| Popis: | Real-world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real-world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time-consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real-world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7-24.8) vs. 21.7 months (95% CI 18.6-24.8) and progression-free survival 8.9 months (95% CI 5.4-12.4) vs. 7.6 months (95% CI 5.7-9.4) for CDC vs. MR, respectively. Highest F1-score was found for cancer-related variables (88.1-100), followed by comorbidities (71.5-90.4) and adverse drug events (53.3-74.5), with most diverse scores on international metastatic RCC database criteria (51.4-100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression-free survival. |
| Databáze: | OpenAIRE |
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