Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data

Gandaglia, Giorgio; Pellegrino, Francesco; Golozar, Asieh; Meulder, Bertrand De; Abbott, Thomas; Achtman, Ariel H.; Omar, Muhammad Imran; Alshammari, Thamir M; Areia, Carlos; Asiimwe, Alex; Beyer, Katharina; Bjartell, Anders; Campi, Riccardo; Cornford, Philip; Falconer, Thomas; Feng, Qi; Gong, Maolian; Herrera, Ronald; Hughes, Nigel; Hulsen, Tim; Kinnaird, Adam; Lai, Lana Yin Hui; Maresca, Gianluca; Mottet, Nicolas; Oja, Marek; Prinsen, Peter; Reich, Christian; Remmers, Sebastiaan; Roobol, Monique J.; Sakalis, Vasileios; Seager, Sarah; Smith, Emma Jane; Snijder, Robert; Steinbeisser, Carl; Thurin, Nicolas; Hijazy, Ayman; Bochove, Kees van; Bergh, Roderick C.N. van den; Hemelrijck, Mieke Van; Willemse, Peter‐Paul M.; Williams, Andrew E.; Kermani, Nazanin Zounemat; Evans-Axelsson, Susan; Briganti, Alberto; N’Dow, James

doi:10.1016/j.eururo.2023.06.012

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…We have validated the data linkage and the above describe repeatable approach in the PIONEER study, where the cohort of patients with newly diagnosed prostate cancer had an inclusion criterion requiring both a diagnosis and biopsy to be recorded. 41 Using only EHR or prescription data would have yielded zero patients in Estonia while using only claims data would have yielded 235 patients. However, combining these 2 datasets resulted in a person count of 635 patients.…”

Section: Discussionmentioning

confidence: 99%

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

Oja,

Tamm,

Mooses

et al. 2023

JAMIA Open

Self Cite

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Objective To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. Materials and Methods We used Estonian national health databases that store almost all residents’ claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150 824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. Results In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. Discussion During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. Conclusion For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.

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Section: Discussionmentioning

confidence: 99%

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

Oja,

Tamm,

Mooses

et al. 2023

JAMIA Open

Self Cite

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“…The idea of the EHDEN network was taken up by the European Medicines Agency (EMA) and will also be continued within the framework of the DARWIN EU (deliver a sustainable platform to access and analyse health care data from across the EU) platform [ 15 ]. For prostate cancer, the idea of joint, cross-institutional data analyses has been adopted in Europe by the large multi-stakeholder consortium PIONEER [ 16 , 17 ]. Here too, data in the OMOP CDM standard are used to jointly answer questions about the routine care of prostate cancer internationally.…”

Section: Introductionmentioning

confidence: 99%

Transforming a Large-Scale Prostate Cancer Outcomes Dataset to the OMOP Common Data Model—Experiences from a Scientific Data Holder’s Perspective

Sibert,

Soff,

La Ferla

et al. 2024

Cancers

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To enhance international and joint research collaborations in prostate cancer research, data from different sources should use a common data model (CDM) that enables researchers to share their analysis scripts and merge results. The OMOP CDM maintained by OHDSI is such a data model developed for a federated data analysis with partners from different institutions that want to jointly investigate research questions using clinical care data. The German Cancer Society as the scientific lead of the Prostate Cancer Outcomes (PCO) study gathers data from prostate cancer care including routine oncological care data and survey data (incl. patient-reported outcomes) and uses a common data specification (called OncoBox Research Prostate) for this purpose. To further enhance research collaborations outside the PCO study, the purpose of this article is to describe the process of transferring the PCO study data to the internationally well-established OMOP CDM. This process was carried out together with an IT company that specialised in supporting research institutions to transfer their data to OMOP CDM. Of n = 49,692 prostate cancer cases with 318 data fields each, n = 392 had to be excluded during the OMOPing process, and n = 247 of the data fields could be mapped to OMOP CDM. The resulting PostgreSQL database with OMOPed PCO study data is now ready to use within larger research collaborations such as the EU-funded EHDEN and OPTIMA consortium.

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'Prehabilitation' checklist for men with prostate cancer starting androgen deprivation therapy

Payne,

Bahl,

Kirby

et al. 2024

Trends Urol & Men's Health

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Androgen deprivation therapy (ADT) plays an important, integral role in the treatment of prostate cancer; however, it is associated with multiple metabolic and other adverse effects. Making patients aware of these prior to starting ADT and assessing any specific risks during discussions is key to optimising outcomes. A brief checklist for health care professionals to use with patients, such as the one presented here, can aid discussions and facilitate signposting to appropriate specialist services without increasing workload.

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Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data

Cited by 4 publications

References 23 publications

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

Transforming a Large-Scale Prostate Cancer Outcomes Dataset to the OMOP Common Data Model—Experiences from a Scientific Data Holder’s Perspective

'Prehabilitation' checklist for men with prostate cancer starting androgen deprivation therapy

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