Next-generation Phenotyping Using Interoperable Big Data

Transcription

1 Biomedical Informatics discovery and impact Next-generation Phenotyping Using Interoperable Big Data George Hripcsak, Chunhua Weng Columbia University Medical Center Collab with Mount Sinai Medical Center

2 Introducing OHDSI Observational Health Data Sciences and Informatics International network of researchers and observational health databases with a central coordinating center housed at Columbia University Mission: Large-scale analysis of observational health databases for population-level estimation and patient-level predictions Vision: Patients and clinicians use OHDSI tools every day to access evidence based on 1 billion patients Clinical researcher, provider, patient Tools and algorithms Data nodes Infrastructure, models, ontologies

3 OHDSI s global research community >120 collaborators from 11 different countries Experts in informatics, statistics, epidemiology, clinical sciences Active participation from academia, government, industry, providers

4 Global reach of ohdsi.org >4600 distinct users from 96 countries in 2015

5 Why large-scale analysis is needed in healthcare All health outcomes of interest All drugs

6 What is large-scale? Millions of observations Need for performance in handling relational structure with millions of patients and billions of clinical observations, focus on optimization to analytical use cases. Millions of covariates No analytics software in the world can fit a regression with >1m observations and >1m covariates on typical hardware but CYCLOPS can! Millions of questions Systematic solutions with massive parallelization should be designed to run efficiently for one-at-a-time AND all-by-all

7 Drug safety surveillance Device safety surveillance Vaccine safety surveillance Comparative effectiveness Health economics Quality of care Person Observation_period Specimen Death Standardized health system data Location Care_site Provider Standardized meta-data CDM_source Concept Standardized clinical data Visit_occurrence Procedure_occurrence Drug_exposure Device_exposure Condition_occurrence Measurement Note Observation Fact_relationship Payer_plan_period Procedure_cost Drug_era Visit_cost Drug_cost Device_cost Cohort Cohort_attribute Condition_era Dose_era Standardized health economics Standardized derived elements Vocabulary Domain Concept_class Concept_relationship Relationship Concept_synonym Concept_ancestor Source_to_concept_map Drug_strength Cohort_definition Attribute_definition Standardized vocabularies

8 Preparing your data for analysis Patient-level data in source system/ schema ETL design ETL implement Patient-level data in OMOP CDM ETL test OHDSI tools built to help WhiteRabbit: profile your source data RabbitInAHat: map your source structure to CDM tables and fields ATHENA: standardized vocabularies for all CDM domains Usagi: map your source codes to CDM CDM: DDL, index, constraints for Oracle, SQL Server, PostgresQL; Vocabulary tables with loading scripts ACHILLES: profile your CDM data; review data quality assessment; explore populationlevel summaries vocabulary OHDSI Forums: Public discussions for OMOP CDM Implementers/developers

9 Data Evidence sharing paradigms Single study Write Protocol Develop code Execute analysis Compile result Patient-level data in OMOP CDM Develop app Real-time query Design query Submit job Review result evidence Large-scale analytics Develop app Execute script Explore results One-time Repeated

10 Patient-level data in OMOP CDM Standardized large-scale analytics tools under development within OHDSI ACHILLES: Database profiling CIRCE: Cohort definition HERMES: Vocabulary exploration HERACLES: Cohort characterization CALYPSO: Feasibility assessment OHDSI Methods Library: CYCLOPS CohortMethod SelfControlledCaseSeries SelfControlledCohort TemporalPatternDiscovery Empirical Calibration LAERTES: Drug-AE evidence base PLATO: Patient-level predictive modeling HOMER: Population-level causality assessment

11 CIRCE for cohort definition CIRCE (Cohort Inclusion and Restriction Criteria Expression) User interface to define and review cohort definitions: COHORT is a set of persons satisfying one or more criteria for a duration of time Disease phenotype is a typical use case for cohort definition Interface translates a human-readable form into a standardized JSON representation for network-based analysis interoperabilities, and compiles the JSON into platform-specific SQL dialect for direct execution against any OMOP CDM-compliant dataset Open-source, freely available source code:

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14 One interface allows definition of criteria across all tables and all fields of the OMOP Common Data Model. The user interface translates this humanreadable form into JSON, which is compiled into SQL dialects for 5 platforms.

15 Each expression can be defined by one or more standard concept sets, using OHDSI s standardized vocabularies

16 HERMES for vocabulary exploration OHDSI standardized vocabularies allows consistent definitions to be applied across disparate source vocabularies: Select descendents for SNOMED concept of Attention deficit hyperactivity disorder maps all ICD9, ICD10, READ codes to execute analysis across OHDSI s international data network

17 Concept sets can define one or more entitities. Here, the PheKB list of ADHD inclusionary medications has been represented by 21 RxNorm ingredient concepts, all brands/dose/form are subsumed

18 The human-readable Expression form is translated into JSON in realtime. This JSON object can be shared across partners to materialize the definition consistently and reproducibly without any programming required

19 Each expression is compiled into SQL. OHDSI supports rendering SQL into platform-specific dialects for SQL Server, Oracle, Postgres, RedShift, MS APS. This code can be copied and executed in your favorite SQL UI tool, or.

20 Patient-level observational databases that are converted to the OMOP Common Data Model and exposed to the OHDSI webapi (either local install or any public network version) can have the cohort definition directly executed within the database to produce a COHORT. The COHORT is then available for all subsequent research within the OHDSI environment

21 Try it yourself

22 Proof of concept Treatment pathways around the world Diabetes, hypertension, depression (Submitted to PNAS)

23 Cohort

24 Databases (255M) and definitions

25 Diabetes

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28 Opportunities for collaboration Implement the PheKB library in CIRCE, so that all organizations with patient-level data (translated to OMOP common data model) can take the work from emerge and directly apply the logic to their own data and participate in emerge s research

29 Phenotyping hard challenges Quality of the data Ambiguous or unknown meaning Accuracy % accuracy [Hogan JAMIA 1997] Completeness mostly missing Complexity disease ontologies Bias

30 Truth observe & interpret Concept author Record read Concept Health status of the patient Error Clinician or patient s conception Error EHR/PHR Implicit 2 nd clinician s conception of the patient (or self, lawyer, compliance,...) Error process Model Computable representation

31 Biased Environment Patient state Therapy Care team Objective tests Electronic health record

32 Inpatient mortality for community acquired pneumonia Mortality (%) cohort 1935 cohort Fine Fine class Hripcsak... Comput Biol Med 2007;37: cohort +CXR +fdg -recent pneu -recent visit 1935 cohort above plus +DSUM exist +ICD9 (pneu not sepsis)

33 EHR-derived phenotype Clinically relevant feature derived from EHR Patient has (a diagnosis of) type II diabetes Recent rash and fever Drug-induced liver injury Then use the phenotype in correlation studies, etc. Query Raw data Phenotype Experiment

34 Physics of the medical record 1. Study EHR as if it were a natural object Use EHR to learn about EHR Not studying patient, but recording of patient 2. Aggregate across units and model 3. Borrow methods from non-linear time series

35 345 Glucose by Δt and tau Glucose MI delta-t (days) tau Albers... Translational Bioinformatics

36 Correlate lab tests and concepts 22 years of data on 3 million patients 21 laboratory tests sodium, potassium, bicarbonate, creatinine, urea nitrogen, glucose, and hemoglobin 60 concepts derived from signout notes residents caring for inpatients to facilitate the transfer of care for overnight coverage concepts likely to have an association + controls

37 Intentional and physiologic associations 0.15 potassium aldactone dialysis hyperkalemia hypokalemia hypomagnesemia

38 Timing of cause in disease vs. treatment 0.1 glucose hyperglycemia hypernatremia hypoglycemia insulin metformin pancreatitis

39 Specificity of the concept 0.14 creatinine aldactone dialysis diarrhea diuretic hctz hyperglycemia hypernatremia vomiting

40 Hripcsak... JAMIA 2013 Health care process model

41 Hripcsak... JAMIA 2013

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43 inpatient admit ambulatory surgery

44 Hripcsak JAMIA 2009 Interpreting time

45 Deviation by stated unit Stated time Now Number of occurrences day week month year Proportional deviation

46 Interpreting time Variable Definition Coefficient Significance value stated numeric value in the temporal assertion (1 to 30 in this sample) <0.001 round number true if value is a multiple of 5 (any unit) or 6 (with months) ln(duration) logarithm of stated duration in days, which equals the product of unit and value gt 18 years true if duration 18 years, so the event should not be in the database <0.001 intercept

47 Patient variability and sampling

48 Parameterizing Time

49 Parameterizing Time (Non-stationarity) 2.5 rate of change 2 coefficient of variation clock warped sequence creatinine glucose sodium potassium Hripcsak JAMIA 2015

50 Parameterizing Time

51 Vector autoregression to decipher associations

52 Noisy training sets with Nigam Shah; David Sontag

53 Summary OHDSI international collaboration could dovetail with emerge Next-generation phenotyping requires understanding the EHR