Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records
Background: Real-world data (RWD) are routinely collected in clinical practice during therapeutic interventions. Data warehouses (DWHs) represent the primary source of RWD in which electronic health records (EHRs) can be rapidly analyzed via natural language processing. This study illustrates an ana...
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Elsevier
2025-03-01
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| Series: | ESMO Real World Data and Digital Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949820124000870 |
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| author | L. Mazzeo F. Corso P. Baili F. Scotti V. Torri M. Ganzinelli V. Mišković R. Leporati L. Provenzano A. Spagnoletti C. Silvestri C. Giani C. Cavalli R.M. di Mauro M. Meazza Prina C. Proto M. Brambilla M. Occhipinti S. Manglaviti T. Beninato D. Miliziano A.D. Dumitrascu G. Di Liberti T.S. Cassano F.G.M. de Braud Giuseppe Lo Russo A. Cappozzo A.M. Paganoni F. Ieva A. Prelaj |
| author_facet | L. Mazzeo F. Corso P. Baili F. Scotti V. Torri M. Ganzinelli V. Mišković R. Leporati L. Provenzano A. Spagnoletti C. Silvestri C. Giani C. Cavalli R.M. di Mauro M. Meazza Prina C. Proto M. Brambilla M. Occhipinti S. Manglaviti T. Beninato D. Miliziano A.D. Dumitrascu G. Di Liberti T.S. Cassano F.G.M. de Braud Giuseppe Lo Russo A. Cappozzo A.M. Paganoni F. Ieva A. Prelaj |
| author_sort | L. Mazzeo |
| collection | DOAJ |
| description | Background: Real-world data (RWD) are routinely collected in clinical practice during therapeutic interventions. Data warehouses (DWHs) represent the primary source of RWD in which electronic health records (EHRs) can be rapidly analyzed via natural language processing. This study illustrates an analytic framework that systematically exploits RWD and methods to generate real-world evidence (RWE) about innovative cancer drugs. The framework has been applied to investigate real-world treatment patterns and clinical outcomes of patients with advanced non-small-cell lung cancer (aNSCLC) treated with tyrosine kinase inhibitors (TKIs). Materials and methods: Data from a cohort of 190 epidermal growth factor receptor-positive mutation (EGFRm) patients with aNSCLC were retrospectively collected in an Italian cancer institute between 2014 and 2022. Patients were treated in first-line (1L) with osimertinib or other TKIs (non-osimertinib). A text-mining algorithm was implemented to retrieve RWD from EHRs. Survival endpoints were median time to treatment discontinuation (mTTD) and median overall survival (mOS) estimated with Kaplan–Meier curves. Time-dependent multivariate Cox analysis was carried out to overcome immortal time bias. Results: Approximately 38% of patients received 1L osimertinib, while the remaining 62% received previous-generation TKIs. Longer mTTD [15 months; 95% confidence interval (CI) 11.9-26.4 months] was found for patients treated with 1L osimertinib compared with non-osimertinib (10 months; 95% CI 7.9-13.1 months). In multivariate analysis, osimertinib was an independent protective factor regardless of bone and brain metastases and local radiotherapy. mOS was 27 months (95% CI 21.4-39.5 months) for osimertinib versus 20.2 months (95% CI 17.6-23.1 months) for non-osimertinib. Conclusions: Data analytics frameworks are useful tools to integrate RWE in cancer research and data-driven models are suitable to process large amounts of RWD. This study demonstrates that real-world treatment patterns and outcomes of TKIs are comparable with those found in both clinical trials and other real-world studies. RWE studies can support clinicians in investigating the best treatment strategy and decision makers to drive new health policies. |
| format | Article |
| id | doaj-art-4ea52fccad6a4834aa910f4072b6b247 |
| institution | OA Journals |
| issn | 2949-8201 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ESMO Real World Data and Digital Oncology |
| spelling | doaj-art-4ea52fccad6a4834aa910f4072b6b2472025-08-20T02:29:36ZengElsevierESMO Real World Data and Digital Oncology2949-82012025-03-01710010910.1016/j.esmorw.2024.100109Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health recordsL. Mazzeo0F. Corso1P. Baili2F. Scotti3V. Torri4M. Ganzinelli5V. Mišković6R. Leporati7L. Provenzano8A. Spagnoletti9C. Silvestri10C. Giani11C. Cavalli12R.M. di Mauro13M. Meazza Prina14C. Proto15M. Brambilla16M. Occhipinti17S. Manglaviti18T. Beninato19D. Miliziano20A.D. Dumitrascu21G. Di Liberti22T.S. Cassano23F.G.M. de Braud24Giuseppe Lo Russo25A. Cappozzo26A.M. Paganoni27F. Ieva28A. Prelaj29Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Department of Electronic, Information and Bioengineering, Politecnico di Milano, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Epidemiology and Data Science, Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, ItalyMOX – Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Department of Electronic, Information and Bioengineering, Politecnico di Milano, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyOncology Unit, ASST Ospedale Maggiore di Crema, Crema, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalyDepartment of Economics, Management and Quantitative Methods, Università degli Studi di Milano, Milan, ItalyMOX – Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, ItalyMOX – Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milan, Italy; Health Data Science Centre, Human Technopole, Milan, ItalyDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Department of Electronic, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Correspondence to: Dr Arsela Prelaj, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy. Tel: +02 2390 3647Background: Real-world data (RWD) are routinely collected in clinical practice during therapeutic interventions. Data warehouses (DWHs) represent the primary source of RWD in which electronic health records (EHRs) can be rapidly analyzed via natural language processing. This study illustrates an analytic framework that systematically exploits RWD and methods to generate real-world evidence (RWE) about innovative cancer drugs. The framework has been applied to investigate real-world treatment patterns and clinical outcomes of patients with advanced non-small-cell lung cancer (aNSCLC) treated with tyrosine kinase inhibitors (TKIs). Materials and methods: Data from a cohort of 190 epidermal growth factor receptor-positive mutation (EGFRm) patients with aNSCLC were retrospectively collected in an Italian cancer institute between 2014 and 2022. Patients were treated in first-line (1L) with osimertinib or other TKIs (non-osimertinib). A text-mining algorithm was implemented to retrieve RWD from EHRs. Survival endpoints were median time to treatment discontinuation (mTTD) and median overall survival (mOS) estimated with Kaplan–Meier curves. Time-dependent multivariate Cox analysis was carried out to overcome immortal time bias. Results: Approximately 38% of patients received 1L osimertinib, while the remaining 62% received previous-generation TKIs. Longer mTTD [15 months; 95% confidence interval (CI) 11.9-26.4 months] was found for patients treated with 1L osimertinib compared with non-osimertinib (10 months; 95% CI 7.9-13.1 months). In multivariate analysis, osimertinib was an independent protective factor regardless of bone and brain metastases and local radiotherapy. mOS was 27 months (95% CI 21.4-39.5 months) for osimertinib versus 20.2 months (95% CI 17.6-23.1 months) for non-osimertinib. Conclusions: Data analytics frameworks are useful tools to integrate RWE in cancer research and data-driven models are suitable to process large amounts of RWD. This study demonstrates that real-world treatment patterns and outcomes of TKIs are comparable with those found in both clinical trials and other real-world studies. RWE studies can support clinicians in investigating the best treatment strategy and decision makers to drive new health policies.http://www.sciencedirect.com/science/article/pii/S2949820124000870non-small cell lung cancertyrosine kinase inhibitorsosimertinibreal-world datatext-miningsurvival analysis |
| spellingShingle | L. Mazzeo F. Corso P. Baili F. Scotti V. Torri M. Ganzinelli V. Mišković R. Leporati L. Provenzano A. Spagnoletti C. Silvestri C. Giani C. Cavalli R.M. di Mauro M. Meazza Prina C. Proto M. Brambilla M. Occhipinti S. Manglaviti T. Beninato D. Miliziano A.D. Dumitrascu G. Di Liberti T.S. Cassano F.G.M. de Braud Giuseppe Lo Russo A. Cappozzo A.M. Paganoni F. Ieva A. Prelaj Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records ESMO Real World Data and Digital Oncology non-small cell lung cancer tyrosine kinase inhibitors osimertinib real-world data text-mining survival analysis |
| title | Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records |
| title_full | Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records |
| title_fullStr | Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records |
| title_full_unstemmed | Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records |
| title_short | Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records |
| title_sort | data analytics for real world data integration in tki treated nsclc patients using electronic health records |
| topic | non-small cell lung cancer tyrosine kinase inhibitors osimertinib real-world data text-mining survival analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2949820124000870 |
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