The financial support of the European Commission is gratefully acknowledged. Material in this presentation reflects only the author’s views and the Commission is not liable for any use that may be made of the information contained herein. Patient Records, Datamining and Best Practice – How Much Information Is Enough, and How Do We Get It? P. Summers, J. Byrne, University of Oxford P. Bijlenga, University Hospital Geneva J. Macho, Hospital Clinic Provincial de Barcelona S. Coley, Sheffield Teaching Hospitals T. Sola, Hospital General de Catalunya T Doczi, Medical University Pecs A. van der Lugt, J. van der Lei, Erasmus Medical Centre D. Rufenacht, Clinic Hirslanden

Federated Federated Research Database HOSPITAL FIREWALL Research Patients Clinicians Clinical Database Data Genetics Imaging Complex Images Clinical Clinical Validate Literature Researchers paradigm of care A continuous data cycle of research ontology

Issue 1: 6 of 7 sites unable/unwillling to provide direct access to hospital data systems Local policies: legal and security concerns Lack of documentation or access (e.g. proprietary formats) Backlog in digitizing medical documents Response - software for collecting and hosting aneurysm care data

Recommendations 1: National health system demonstrations and testing of medical data-sharing systems. Uniform health-data access policies at (trans)national levels for care and for research Exchangeability of records – open standards Ensure adequately representation of neuro- related data in these standards (HL7).

Issue 2: Are clinical databases complete, consist and correct enough for research? Without curation and record correction – NO! Response 2: Feedback pathway to site of origin Research nurse / clinical team to correct ? Monitoring Recommendations 2: Automation & universality of record taking Clinical team should provide a “many eyes” approach to error detection & correction

@neuBrowser

Issue 3: Datamining needs data covering the whole patient – not just one aspect of care statin withdrawal; x2 SAH risk PHARMO Clinic databases : lack of controls to compare against tend to contain known factors only Recommendations 3 Global information systems within institutions Consistent beats exhaustive in data collection Risselada, Stroke (8):

Issue 4: What data is relevant to aneurysms? Literature search + practice in a combined neuro- interventional unit: > 950 possible items > 200 items in the average clinical report summary submitted Cochrane review of the systematic reviews: 24 risk factors for known IA to rupture* ~50 items considered important for controlling in genetics and transcriptomic studies Clinic records poor for consistency of risk and control factor assessment due to clinician filtering D28, Cochrane Literature Review Snapshot v3

Recommendations 4: Seek community consensus on standard of practice for patient data collection Decision making needs Known / suspected risk factors Procedural essentials

Federated Federated Research Database HOSPITAL FIREWALL Research Patients Clinicians Clinical Database Data Genetics Imaging Complex Images Clinical Clinical Validate Literature Researchers paradigm of care A continuous data cycle of research ontology

The Legacy Info and access software Ongoing assessment & adapted for use in the German Aneurysm Registry ESMINT working group on continued development and broadened

Thanks to Gulam Zidani, the participating partners and the over 1200 patients / controls

Pre-Discussion Challenge How many risk factors for rupture in known aneurysm patients can you name?

Cochrane systematic review Sex Age Ethnicity IA Site IA Size Sentinel Symptoms Previous SAH BP Cholesterol Diabetes BMI Polycystic kidney disease Smoking Alcohol Aspirin Thrombolysis Oral Contraceptives HRT Spinal manipulation Physical Activity Family History SAH Apolipoprotien E gene Other genetic factors Time, weekday, season D28, Cochrane Literature Review Snapshot v3