Overview of the Synthetic Derivative

1. Overview of the Synthetic Derivative April 16, 2010 Melissa Basford, MBA Program Manager – Synthetic Derivative

2. Synthetic Derivative resource overview • Rich, multi-source database of de-identified clinical and demographic data • Contains ~1.8 million records • ~1 million with detailed longitudinal data • averaging 100k bytes in size • an average of 27 codes per record • Records updated over time and are current through 7/31/09

3. SD Establishment Information collected during clinical care SD Database DE-IDENTIFICATION Data Parsing Restructuring for research Data Parsing Access through secured online application One way hash Data export

4. Data Types (so far) • Narratives, such as: • Clinical Notes • Discharge Summaries • History & Physicals • Problem Lists • Surgical Reports • Progress Notes • Letters & Clinical Communications • Diagnostic codes, procedural codes • Forms (intake, assessment) • Reports (pathology, ECGs, echocardiograms) • Lab values and vital signs • Medication orders • TraceMaster (ECGs) • ˜100 SNPs for 7000+ samples

5. Research use cases assumed in resource development (either alone, or with DNA samples)

6. Technology + policy • De-identification • Derivation of 128-character identifier (RUI) from the MRN generated by Secure Hash Algorithm (SHA-512) • RUI is unique to input, cannot be used to regenerate MRN • RUI links data through time and across data sources • HIPAA identifiers removed using combination of custom techniques and established de-identification software • Restricted access & continuous oversight • Access restricted to VU; not a public resource • IRB approval for study (non-human) • Data Use Agreement • Audit logs of all searches and data exports

7. Date shift feature • Our algorithm shifts the dates within a record by a time period that is consistent within each record, but differs across records • up to 364 days backwards • e.g. if the date in a particularrecord is April 1, 2005 and the randomly generated shift is 45 days in the past, then the date in the SD is February 15, 2005)

8. What the SD can’t do • Outbreaks and other date-specific studies (catastrophes, etc) • Find a specific patient (e.g. to contact) • Replace large scale epidemiology research (e.g. TennCare database) • Temporal search capabilities limited (but under development) • “First this, than that” study designs require significant manual effort • Expect “timeline” views and searching Q1-Q2

9. Demographic Characteristics *A significant number of SD records are of unknown race/ethnicity. Multiple efforts are underway to better classify these records including NLP on narratives.

10. Top diagnosis codes overall: • FEVER • CHEST PAIN • ABDOMINAL PAIN • COUGH • PAIN IN LIMB • HYPERTENSION • ROUTINE MEDICAL EXAM • ACUTE URI • MALAISE & FATIGUE • HEADACHE • URINARY TRACT INFECTION Examples of frequent diagnoses in total SD

11. Examples of frequent diagnoses among peds in SD • Top diagnosis codes overall: • ROUTIN CHILD HEALTH EXAM • FEVER • COUGH • ACUTE PHARYNGITIS • URIN TRACT INFECTION NOS • VOMITING ALONE • CARDIAC MURMURS NEC • ABDOMINAL PAIN-SITE NOS • OTITIS MEDIA NOS • ACUTE URI NOS • PAIN IN LIMB

12. Examples of ICD-9 codes for rare diseases

13. Statistical considerations and limitations Working with biostats (Schildcrout) on these issues. Some considerations: • Selection bias for inclusion in population; representativeness of cohort and generalizability • Bias in ICD-9 coding • Confounding by indication • Severity of disease • Medication prescribed/ordered vs received • Timing • For example, AE must come after medication (timecourse) • Timescale upon which events could be attributed to events • Dropout (Death vs. discharge vs. transfer) • Intervention based on in-hospital disease history

14. Using the SD resource

15. Requests IRB Exemption Enters StarBRITE to complete electronic application (IRB status is in StarBRITE) Researcher accesses SD SD staff verify/ access granted Researcher Signs DUA SD Access Protocol

16. Data Use Agreement Components

17. Phenotype Searching • Definition of phenotype for cases and controls is critical • May require consultation with experts • Basic understanding of data elements; uses and limitations of particular data points is important • List of ‘watch outs’ under development • Reviewing records manually to make case determination (or even to calculate PPV of search methodology) will be somewhat time consuming

18. The problem with ICD9 codes • ICD9 give both false negatives and false positives • False negatives: • Outpatient billing limited to 4 diagnoses/visit • Outpatient billing done by physicians (e.g., takes too long to find the unknown ICD9) • Inpatient billing done by professional coders: • omit codes that don’t pay well • can only code problems actually explicitly mentioned in documentation • False positives • Diagnoses evolve over time -- physicians may initially bill for suspected diagnoses that later are determined to be incorrect • Billing the wrong code (perhaps it is easier to find for a busier clinician) • Physicians may bill for a different condition if it pays for a given treatment • Example: Anti-TNF biologics (e.g., infliximab) originally not covered for psoriatic arthritis, so rheumatologists would code the patient as having rheumatoid arthritis

19. Lessons from preliminary phenotype development (can be corrected) • Eliminating negated and uncertain terms: • “I don’t think this is MS”, “uncertain if multiple sclerosis” • Delineating section tag of the note • “FAMILY MEDICAL HISTORY: Mother had multiple sclerosis.” • Adding requirements for further signs of “severity of disease” • For MS: an MRI with T2 enhancement, myelin basic protein or oligoclonal bands on lumbar puncture, etc. • This could potentially miss patients with outside work-ups, however

20. Other lessons (more difficult to correct via algorithms) • A number of incorrect ICD9 codes for RA and MS assigned to patients • Evolving disease • “Recently diagnosed with Susac’s syndrome - prior diagnosis of MS incorrect.” (Notes also included a thorough discussion of MS, ADEM, and Susac’s syndrome.) • Difference between two doctors: • Presurgical admission H&P includes “rheumatoid arthritis” in the past medical history • Rheumatology clinic visits notes say the diagnosis is “dermatomyositis” - never mention RA • Sometimes incorrect diagnoses are propagated through the record due to cutting-and-pasting / note reuse

21. Resources • StarPanel • Identified clinical data; designed for clinical use • Record Counter • De-identified clinical data; sophisticated phenotype searching • Returns a number – record counts and aggregate demographics • Synthetic Derivative • De-identified clinical data; sophisticated phenotype searching • Returns record counts AND de-identified narratives, test values, medications, etc., for review and creation of study data sets • BioVU • SNP data • De-identified clinical data; sophisticated phenotype searching • Able to link phenotype information to biological sample

22. Live Demo