I've alluded to this project in other posts, but thought I would make it a more official call for network collaborators. This study would be coordinated out of Stanford, I'll be working on it with a colleague in the BMI program (Lichy Han), and Nigam Shah.
Perioperative medicine is an area where many prediction models are used in practice. Like others PMs in medicine, parsimony is generally the rule here, they can likely be outperformed statistical learning methods operating on a comprehensive representation of the health record like the CDM.
These are largely used to inform risk discussions with patients, but also to direct post operative monitoring and testing (in new Canadian guidelines, decisions around post op Troponin testing can depend in part on the 6 point Lee Cardiac Risk Index for example). They could also have a great use case in directing resources (not all folks need to see an internist pre or post operatively).
It's an attractive area for the OHDSI PLP framework: these are used clinically in current state, the outcomes are relatively proximate, and we don't need to try to handle granular data from the hospital stay when the event occurs. In fact, these are more typically used in a preceding outpatient preoperative evaluation.
There's a number of outcomes of interest which we will look to specify. The most impactful would be MACE/Death.
My current problem, is actually trying to wrap my head around some preliminary results that are too good (AUCs 90% or higher). I maintain a suspicion that this is the result of features from the inpatient stay where the outcome event occurs, making it into the model. Hoping to resolve that issue in the cohort definitions with this thread:
(In full disclosure: Lichy and I are in a class where the output is an R package - what better than to produce a network study! We'll make clear academic distinctions about our contributions vs. those of collaborators, our intent to do this has been proposed and approved by the prof teaching the class). There will still doubtlessly be work involved in packaging this up.