SMART integration with i2b2 reviewed

Discussed how additional SHRINE influences future directions of i2b2. Then demonstrated the latest i2b2/SMART integration (quite impressive!).

MDD

(minutes courtesy Caitlin)

Attendance: Jordan Smoller, Roy Perlis, Shawn Murphy, Susanne Churchill, Isaac Kohane, Tianxi Cai, Victor Castro, Alison Hoffnagle, Patience Gallagher, Sydney Weill, Caitlin Clements

Minutes:

-ICCBDà Refresh: Victor has performed a preliminary refresh of the data mart; however, this did not include the NLP cohort. Victor will finish NLP classification and will perform another refresh by the end of the following week that includes this data. The current updated numbers for the other cohorts may change a bit as well when he performs this second refresh; however, they will not change by much.

• The numbers for the cohort sizes increased after the refresh, particularly because the group added McLean patients to the data mart (McLean patients must come to MGH/BWH for blood draws)
• • While the cohort sizes are relatively large, the number of people the group has actually collected (through Crimson) is very lowàabout only 10% of the possible cohort size
  • Cohort MRP/MRP-SV numbers are abysmal. PPV for these cohorts are getting worse and performing poorly. Could the group possibly run a simple regression for these cohorts? àNo, because the numbers are too low
• Where does the group stand with projection?
• • The group is in the 4^th year of our 5 year grant, so the group needs to figure out where we stand quickly.

Next Stepsà 1. Final refresh of data mart

                        2. The group needs to decide what we are going to do with the MRP and MRP-SV cohorts

StudyGroup	Cohort	ClassificationType	N	TP	FP	PPV	PPV 95% CI Lower	PPV 95% CI Upper
Case	95NLP	NLP+EMR	45	38	7	0.844	0.712	0.923
Case	NoNLP	EMR	26	24	2	0.923	0.759	0.979
Case	MRP	EMR	25	14	11	0.560	0.371	0.733
Case	MRP-SV	EMR	5	1	4	0.200	0.036	0.624
StudyGroup	Cohort	ClassificationType	N	TN	FN	NPV	NPV 95% CI Lower	NPV 95% CI Upper
Control	MDD	Advertising	11	10	1	0.909	0.623	0.984
Control	SCZ	Advertising	15	13	2	0.867	0.621	0.963
Control	Control	EMR	13	13	0	1.000	0.772	1.000

All hands meetings

Topic: Local i2b2-initiated federally funded spinoffs.

1.Kat Liao presented the RA DBP and the spinoff RA-CVD project. What is the relationship with risk for CAD/CVD risk in the general population (and the risk alleles) and that in RA. Our of 4453 in RA cohort (i2b2), 335 have CAD.

2. Jordan Smoller presented the Bipolar Disease NIMH grant. Reviewed the challenges of a very specific case definition. Claims codes alone seem to give positive predictive values (PPV) in the 20-50% range and the PPV of NLP is about 85% (with specificity of 95%).

3. Roy Perlis spoke about his R01 derived from the MDD-resistant-to-SSRI DBP. He is studying a MDD clinical and genetic risk risk algorithm/classifier. He is on the way to a 1500 population which represented 1/2 of all the drug response GWAS currently published in this particular domain?

4. Robert Plenge described the spinoff of the i2b2 RA DBP to the PGRN project. The project looks at genetic informants of treatment response and treatment toxicities. Involves Vanderbilt, Northwestern and Partners. Early result shows the portability of the NLP RA classifier.

Review of drug exposure methodology

Given intermittent recording of drug exposure and clinical events, there is going to be a lot of censoring in the EHR data stream. This kind of data censoring is a methodological challenge and we reviewed various heuristics and comparisons that can be used to correct/account for this censoring. A lot of focus on modeling the frequency of observations.

Discussion of which pieces have to be locally vs centrally hosted

For the next generation of i2b2, we are trying to minimize how many cells have to be locally hosted. Even more crucially, how many cells have to be locally customized and supported and how many can be generic. In other words, can we or should we reposition any abstraction barriers?

Multiple sclerosis DBP

Xia et al,

Very extensive discussion of randomization procedures in selecting patient samples.

Back for Summer: CVD DBP

Shaw, Cai, Churchill, Liao, Murphy, Kohane, Sordo, Savova

Reviewed the subsamples to see if we are getting the right DM rate.