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Gandaglia G, Pellegrino F, Golozar A, De Meulder B, Abbott T, Achtman A, Imran Omar M, Alshammari T, Areia C, Asiimwe A, Beyer K, Bjartell A, Campi R, Cornford P, Falconer T, Feng Q, Gong M, Herrera R, Hughes N, Hulsen T, Kinnaird A, Lai LYH, Maresca G, Mottet N, Oja M, Prinsen P, Reich C, Remmers S, Roobol MJ, Sakalis V, Seager S, Smith EJ, Snijder R, Steinbeisser C, Thurin NH, Hijazy A, van Bochove K, Van den Bergh RCN, Van Hemelrijck M, Willemse PP, Williams AE, Zounemat Kermani N, Evans-Axelsson S, Briganti A, N'Dow J. Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data. Eur Urol 2024; 85:457-465. [PMID: 37414703 DOI: 10.1016/j.eururo.2023.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/18/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. OBJECTIVE To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. DESIGN, SETTING, AND PARTICIPANTS From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. RESULTS AND LIMITATIONS The most common comorbidities were hypertension (35-73%), obesity (9.2-54%), and type 2 diabetes (11-28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12-25%) and emergency department visits (10-14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. CONCLUSIONS Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. PATIENT SUMMARY Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.
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Affiliation(s)
- Giorgio Gandaglia
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy.
| | - Francesco Pellegrino
- Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy
| | - Asieh Golozar
- Odysseus Data Services, New York, NY, USA; OHDSI Center, Northeastern University, Boston, MA, USA
| | | | | | | | - Muhammad Imran Omar
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Academic Urology Unit, University of Aberdeen, Scotland, UK
| | | | | | | | - Katharina Beyer
- Translational Oncology and Urology Research, King's College London, London, UK
| | - Anders Bjartell
- Department of Translational Medicine, Lund University, Lund, Sweden
| | - Riccardo Campi
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | | | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Qi Feng
- Astellas Pharma, Inc., Northbrook, IL, USA
| | - Mengchun Gong
- Nanfang Hospital, Southern Medical University, Guangzhou, China; DHC Technologies, Beijing, China
| | | | | | - Tim Hulsen
- Philips Research, Department of Hospital Services & Informatics, Eindhoven, The Netherlands
| | | | | | | | - Nicolas Mottet
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands
| | - Marek Oja
- Institute of Computer Science, University of Tartu, Tartu, Estonia; STACC, Tartu, Estonia
| | - Peter Prinsen
- Netherlands Comprehensive Cancer Organization, Eindhoven, The Netherlands
| | | | - Sebastiaan Remmers
- Erasmus University Medical Centre, Cancer Institute, Rotterdam, The Netherlands
| | - Monique J Roobol
- Erasmus University Medical Centre, Cancer Institute, Rotterdam, The Netherlands
| | - Vasileios Sakalis
- Department of Urology, General Hospital of Thessaloniki Agios Pavlos, Thessaloniki, Greece
| | | | - Emma J Smith
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands
| | | | | | - Nicolas H Thurin
- INSERM CIC-P 1401, Bordeaux PharmacoEpi, Université de Bordeaux, Bordeaux, France
| | | | | | | | | | - Peter-Paul Willemse
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Department of Urology, Cancer Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Andrew E Williams
- The Institute for Clinical Research and Health Policy Studies at Tufts Medical Center, Boston, MA, USA
| | | | | | - Alberto Briganti
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Department of Urology and Division of Experimental Oncology, Urological Research Institute, IRCCS San Raffaele Hospital, Milan, Italy
| | - James N'Dow
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands; Academic Urology Unit, University of Aberdeen, Scotland, UK
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Gauffin O, Brand JS, Vidlin SH, Sartori D, Asikainen S, Català M, Chalabi E, Dedman D, Danilovic A, Duarte-Salles T, García Morales MT, Hiltunen S, Jödicke AM, Lazarevic M, Mayer MA, Miladinovic J, Mitchell J, Pistillo A, Ramírez-Anguita JM, Reyes C, Rudolph A, Sandberg L, Savage R, Schuemie M, Spasic D, Trinh NTH, Veljkovic N, Vujovic A, de Wilde M, Zekarias A, Rijnbeek P, Ryan P, Prieto-Alhambra D, Norén GN. Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study. Drug Saf 2023; 46:1335-1352. [PMID: 37804398 PMCID: PMC10684396 DOI: 10.1007/s40264-023-01353-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/09/2023]
Abstract
INTRODUCTION Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. OBJECTIVE The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. METHODS Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. RESULTS Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. CONCLUSIONS Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.
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Affiliation(s)
| | | | | | | | | | - Martí Català
- Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | | | - Daniel Dedman
- Clinical Practice Research Datalink (CPRD), The Medicines and Healthcare Products Regulatory Agency, London, UK
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Maria Teresa García Morales
- Instituto de Investigación Sanitaria Hospital 12 de Octubre, CIBER de Epidemiología y Salud Pública, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | | | - Annika M Jödicke
- Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Milan Lazarevic
- Clinic for cardiac and transplant surgery, University Clinical Center Nis, Nis, Serbia
| | - Miguel A Mayer
- Hospital del Mar Medical Research Institute, Parc de Salut Mar, Barcelona, Spain
| | - Jelena Miladinovic
- Clinic for infectious diseases, University Clinical Center Nis, University Clinical Center Nis, Nis, Serbia
| | | | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | | | - Ruth Savage
- Uppsala Monitoring Centre, Uppsala, Sweden
- Department of General Practice, University of Otago, Christchurch, New Zealand
| | - Martijn Schuemie
- Epidemiology Department, Johnson & Johnson, Titusville, NJ, USA
- Department of Biostatistics, UCLA, Los Angeles, CA, USA
| | - Dimitrije Spasic
- Clinic for cardiac and transplant surgery, University Clinical Center Nis, Nis, Serbia
| | - Nhung T H Trinh
- PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Nevena Veljkovic
- Heliant Ltd, Belgrade, Serbia
- Vinca Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Ankica Vujovic
- Clinic for Infectious and Tropical Diseases, University Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Marcel de Wilde
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Patrick Ryan
- Epidemiology Department, Johnson & Johnson, Titusville, NJ, USA
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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