The burden of obesity inferred through high precision rules

An interesting presentation by Ted Pedersen on finding obesity morbidities in i2b2 records.

Major Depressive Disorder

German i2b2 implementations move forward

Thanks to Sebastian Mate for this update on the first German Academic i2b2-Workshop.

They had visitors from the universities of Goettingen, Ulm, Hannover, Giessen and Leipzig. Some of them had to travel hundreds of miles (see attached map) to visit them in Erlangen. Goettingen and Leipzig have i2b2 instances running, the others will try it out.

Topics covered included at the meeting:
(1) Clinical Data Warehouse in Erlangen(2) i2b2 Overview, motivation and work in Erlangen(3) i2b2 in 30 Minutes - how the Erlangen Package works, with live-installation ;-)(4) The simple SQL-based Erlangen ETL approach - how we load data into the hive(5) i2b2 HIS-mapping - how we map hundreds of attributes from our HIS into the i2b2 instance (ONT and CRC)(6) TMF PID-Generator and i2b2(7) TMF Pseudonymization Service

RA

Discussed the SNP's on the genotyped data and how the continent of origin and the PHS labels of ethnicity are highly concordant.

Open Source

A very insightful response from Fred Trotter. He lays down the options in a very nuanced and clear fashion. Much appreciated.

Major Depressive Disorder

Perlis, Smoller, et al.,

MInutes courtesy Patience Gallagher.

·       Longitudinal Classifier

o   Roy and Victor will finalize parameters of algorithm

o   Victor will then report (and provide a visualization of):

§ % depressed, % well, # of all notes, etc

o   To query Crimson: Victor will provide a list of medical record numbers within the two groups of interest (responsive and resistant) to Lynn Bry who can then report how many samples are currently available within Crimson

§ Parameters may need to be readjusted, depending on response from Crimson

·       Discussion of Validation

o   The issue with last week’s approach:

§ The algorithm is based on the text, so the first level of validation should not use information (i.e. clinician’s extensive knowledge of a patient) that is not in the text.

o   New validation plan:

§ STEP ONE:

·       GOAL: Determine if an expert clinician’s classification (based on notes only) is the same as the result of the algorithm

·       Pull successfully classified notes

o   All of a patient’s notes will be reviewed

o   Clinicians will be blinded to the results of the classifier

o   The sample of notes will reflect the results of the algorithm: “Random but representational”

o   To keep the validation clean, it will be a “case-control” model - Treatment resistant vs. Responsive

o   For now, only the electronic medical record will be used.

§ The consensus was that patient charts would be more annoying than beneficial, and the electronic records probably have sufficient information

§ Additionally, the information from the paper records is not integrated into the algorithm

§ May look at paper records down the road

§ STEP TWO: Compare list of patients that Roy knows are treatment resistant or responsive and run their notes through the classifier

·       (Tianxi says this will be beneficial as it will give more power to the classifier)

§ STEP THREE: Use the Quick Inventory of Depression Severity (QIDS) as an external source of validation

·       Many patients have QIDS scores in their charts – can determine if the algorithm classification is consistent with performance on the QIDS – this would be a cross-sectional measure

o   The output of this approach would be: “Among patients classified s depressed, the mean QIDS score is ____”

§ NEXT STEPS:

·       Do first level of the validation over the next few weeks.

o   Victor will pass the notes to Roy.

o   Roy will be the sole clinician reviewing the notes

·       Manuscript:

o   Victor and Tianxi have provided their input to Roy

o   Roy will integrate this information and then re-distribute the manuscript to the group

·       PV

o   Update from Victor on obesity:

§ Compared the BMIs of this data set with all other patients and the distribution of the MDD sample is very similar, but right shifted compared to majority’s BMI distribution

·       This makes sense! - Being depressed (and on antidepressants) leads to weight gain

Widening the Use of Electronic Health Records Data for Research

A symposium hosted by the National Center for Research Resources (NCRR). Louise Ramm, Deputy Director of NCRR provided framing challenges and welcome. Zak Kohane introduced use cases, sources of and reviewed the false dichotomy between health-record based research and clinical trials..

Gary Gibbons provided a perspective of disease in the African-American population as an exemplar of a complex orphan disease in the sense that like rare orphan diseases, it is understudied and insufficiently treated. He also pointed out how in many parts of the country underserved minorities are located away from the academic health centers that have made the most inroads in the use of electronic health records.. Therefore, institutions such as Morehouse School of Medicine have their work cut out for them (and not a lot of resources) to integrate data from a large number of only lightly affiliated practices. That same challenge presents an opportunity to be even more impactful in an orphan disease of epidemic quality. Professor Gibbons also urged a broadening of the captured context beyond what is conventionally captured in a standard (brief) healthcare visit. Environmental variables that are highly penetrant, much more so than many genomic markers are poorly captures. He concluded by reviewing the current compelling information about pharmacogenomic differences, and population genetic risks and also the wide holes in our knowledge of these as they pertain to various groups within the USA.

Andrew Auerbach from UCSF addressed comparative effectiness research and its translation into "Health system innovation research" Described how much can be done with charge data, and how additional codified data types (e.g. medications) can further improve the quality of that data. Closed with a discussion of how the various stakeholders in using EHR data for research (.e.g NIH, Payors, health systems leaders, physicians, and patients) might be well aligned or not. Put us on notice that IRB's are unfamiliar about distributed query systems and/or grids and this is becoming at last an obstacle for many CER studies. Summarized several use cases such as optimal length of treatment of pneumonia? Can a patient-focused discharge checklist reduce risk for readmission?

Robert Plenge described his use of i2b2 and electronic health records for genotypic research and discovery of endophenotypes.
John Brownstein reviewed non-traditional public health research using institutional data and non-traditional, non-institutional healthcare data extraction and analysis.