PHARMO Database Network

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1 Pros & cons in Oncology studies PHARMO Myrthe Database van Herk-Sukel, Network PhD 1 Disclosure Myrthe van Herk-Sukel is an employee of the PHARMO Institute for Drug Outcomes Research. This independent research institute performs financially supported studies for government and related healthcare authorities and several aceutical companies. The opinions and positions presented today are those of the presenter and do not necessarily reflect those of the PHARMO Institute. 2 1

2 Increasing need for longitudinal Real World Data Optimisation of the use of a cancer registry Retrospective database studies via data-linkage Support regulatory, market access and clinical decision making in The Netherlands Other (external) databases 3 Outpat Outpat Outpat Outpat GP GP GP GP LAB LAB LAB LAB In-pat In-pat In-pat In-pat Data Foundation Medication use by outpatients General Practitioner data (GP) Clinical laboratory (LAB) Medication use by inpatients TTP TTP TTP TTP Linkage on a patient level Data anonymization 4 2

3 Data Foundation GP data Clinical laboratory Medication use of inpatients TTP TTP TTP TTP Medication use of outpatients Hospitalisation Database TTP Cancer registry TTP Other TTP Data Archive Linkage on a patient level Data anonymization PHARMO Database Network Research Publications Reports PHARMO Database Research Network Institute 5 6 3

4 Procedures to obtain data Retrieve permission for obtaining data from all database separately - in-house databases vs partnership databases Other conditions are also important - security of the data - familiar with the contents and hurdles of the data - familiar with the healthcare system of The Netherlands 7 Healthcare database analysis in Oncology Data requirements in oncology studies are specific due to the nature of cancer as a group of diversified diseases and its complexity the need of data items not used in other therapeutic areas Virtually no single healthcare database may fulfil those needs Claims data Electronic medical records Registries Medical record linkage may be a promising approach to create data sources for oncology studies 8 4

5 General Practitioner (GP) Database > 3.1 million patient lives Catchment area: 2.4 million residents, continuously growing + - Diagnoses, symptoms - International Classification of Primary Care (ICPC) codes - Laboratory test results, referrals and prescriptions - Burden of illness, (co)morbidities - Biometric data may be biased - High level codes - Limited information from specialist visits Onco specific - No in-patient drugs (e.g. chemo) - No histological (e.g. cancer staging) information 9 PHARmaco-MOrbidity linkage 10 5

6 Out-patient Pharmacy linked to Hospitalisation Database > 6.3 million patient lives Catchment area: 3.8 million residents + - Onco specific Dispensing records (both GP and specialist prescribed) - Anatomical Therapeutic Chemical (ATC) codes - Hospitalisation records - Discharge diagnoses, procedures - International Classification of Diseases (ICD) codes - Only severe events - admission for more than 24 hours or for which a bed is required - No information from specialist visits - No in-patient drugs (e.g. chemo) - No histological (e.g. cancer staging) information 11 Linked to Pathology Registry (PALGA) Catchment area: 3.8 million residents + - Onco specific Excerpts of histological, cytological and autopsy examinations - immunohistochemistry and molecular pathology e.g. KRAS/EGFR/BRAF/ALK - Protocols to standardize the information registered (2014) - PALGA codes based on SNOMED (Systematized Nomenclature of Medicine) - Dispensing records (both GP and specialist prescribed) - Hospitalisation records - Only severe events - No information from specialist visits - No in-patient drugs (e.g. chemo) - No histological (e.g. cancer staging) information

7 Linked to Cancer Registry (IKNL) > soon to be expanded Catchment area: 1.5 million residents + - Onco specific Tumour staging (TNM-classification), topography, morphology - International Classification of Diseases for Oncology (ICD-O) - Tumour specific info - Receptor status (e.g. breast cancer: ER, PR, HER2) - PSA, Gleasonscore (prostate cancer) - Initial treatment (sequence and type) - chemotherapy, radiation therapy, surgery - Excerpts of histological, cytological and autopsy examinations - Dispensing records (both GP and specialist prescribed) - Hospitalisation records - Only severe events - No information from specialist visits - No in-patient drugs (e.g. chemo) 13 Linked to other databases > soon to be expanded Catchment area: 1.5 million residents + - In-patient drugs (e.g. chemo) - Anatomical Therapeutic Chemical (ATC) codes - Clinical test results - Tumour staging, histology, tumour specific info - Initial treatment(sequence and type) - Excerpts of histological, cytological and autopsy examinations - Dispensing records (both GP and specialist prescribed) - Hospitalisation records - Only severe events - No Limited information from specialist visits Onco specific - No in-patient drugs (e.g. chemo) 14 7

8 Study example 15 From cradle to grave: studies in oncology Before cancer diagnosis Cancer diagnosis and tumor characteristics Treatment of cancer Follow-up after cancer diagnosis and treatment Before cancer diagnosis Treatment of cancer Follow-up after cancer diagnosis and treatment Death Pre-existing morbidities and the risk of cancer Drug use and the risk of cancer Pattern of cancer care (e.g. continuation of hormonal treatment) chemotherapy regiments) Pharmacogenetics for personalized medicine Morbidities that influence the treatment of cancer Drug drug interactions Follow-up treatment, palliative care Patient reported outcomes Late effects of treatment Disease progression Survival Acute adverse events 16 8

9 for Oncology Cancer Registry (IKNL) PHARMO-PALGA-ECR Southeastern Netherlands (ECR) catchment area 2.4 million inhabitants 1.5 million inhabitants PHARMO-PALGA catchment area 3.8 million inhabitants Total linked PHARMO-PALGA-ECR cohort Included databases Out-patient Pharmacy Database PHARMO Hospitalisation Database Cancer Registry Pathology Database Linked PHARMO-PALGA-ECR sub cohorts 80% Clinical Laboratory data 60% In-patient Pharmacy data 50% GP data 17 Examples of international multi-database studies and consortia 18 9

10 Summary 25% of the residents in The Netherlands Follow-up for an average of 10 years Average lag time of the data is 1 year Medical Record Linkage No single database can fulfil all challenges in oncology studies Linkage is data enrichment Data collection period, catchment area and overlap between data sources differ 19 Thank you for your attention 20 10