Thiopurine Metabolites Indexed Assay Calculation as a Grid-Enabled Rules Engine via the LIDDEx Consortium’s Grid Services Architecture in Support of Inflammatory Bowel Disease (IBD) Management Peter Higgins, MD, PhD, Msc (University of Michigan) Ji Zhu, PhD (University of Michigan) Akbar K. Waljee, MD, PhD (University of Wisconsin) Jeffrey Sica (University of Michigan) John Hamilton (University of Michigan) Shane Brown, PhD (PKS, Pty. Ltd.) Fede Lopez (PKS, Pty. Ltd.) Rob Manser, MSc (PKS, Pty. Ltd.) Ulysses J. Balis, MD (University of Michigan)

Overview Problem Statement Hypothesis A summary of the Thiopurine Metabolites Indexed Assay Calculation as a ultra-high complexity, rules-based test A Summary of the LIDDEx Initiative Leveraging the LIDDEx grid services architecture to realize a centrally validated, deployed and maintained rules platform for Thiopurine testing

Problem Statement Ultra-high complexity, rules-based indexed assays, such as the thiopurine metabolites test, are extremely difficult to implement in conventional LIS architectures. The likelihood of being able to initially implement and then maintain such tests across the vast plurality of LIS deployments, as single site-specific instantiations, is vanishingly low

Hypothesis Use of grid-based, web services architecture, in support of such complex testing, can provide an effective framework for the initial deployment and subsequent curation of such complex tests

Thiopurine Background Many IBD patients require thiopurines Experts have monitored with CBC & Chemistry ($60) – not reproducible Newer monitoring with metabolites (6-TGN and 6-MMP) ($300) – interpretation reproducible 62% sensitive 72% specific for clinical response “Before advocating the use of expensive testing, maybe we should look for simpler options.” Lloyd Mayer May IBD patients require thiopurines for treatment. Thioprines are the mainstay of therapy for these patients. These medications have a low therapeutic index. Most experts [PAUSE] use the complete blood and [PAUSE] and the comprehensive labs [PAUSE] Which are 60 dollars test are used in order to optimize dosing to produce clinical response without toxicity [PAUSE] These experts use their clinical experience to guide management as they are no standard algorithms. A new monitoring strategy using thiopurine metabolites(6TGN and 6MMP) are being used more frequently, As this is readily available and does not require expert interpretation. [PAUSE] They However cost $300 and take 2-5 days to come back. A recent meta-analysis of this approach [PAUSE] shows [PAUSE] that thiopurine metabolites [PAUSE] are only 62% sensitive [PAUSE] and 72 % specific [PAUSE] For clinical response. This raises the question: [PAUSE] Are we really getting $240 worth [PAUSE] of additional information? And as lloyd mayer said in his april 2006 editorial in gastro; before advocating the use of expenive tests maybe we should look for simpler options. But nobody has

Predicting Outcomes with Thiopurines Hypothesis: We can use CBC and Comp to predict clinical response more accurately than 6TG alone Use modeling to simulate expert assessment Aims Develop optimal model with machine learning Develop explanatory model:logistic regression Compare ROC curves Predict shunting and noncompliance

Development and Validation Developed algorithms on 70% of sample Using predictors CBC, Chemistries, and age Validated algorithms on remaining 30% Compared AuROC for clinical response between 6-TGN and Machine Learning Algorithm

Comparing MLA to 6-TGN Notices that the green lines shows us an ROC of 0.59 And the Orange line an area under the curve of 0.85 This is highly significant with 0.001

Variable Importance for Clinical Response Algorithm

Predicting Other Outcomes Shunting algorithm AuROC of 0.80 Noncompliance algorithm AuROC of 0.81

Random Forest Approach Advanced type of Machine Learning Constructs many (10K) short trees Each split based on random subset of predictors Each tree has a “vote” on outcome. Mathematically demanding rules-based algorithm Random Forest is a Type of Machine Learning that can address these problems. it……..

LIDDEX Clinical need as a motivation: “Out of the box” Laboratory Information system interoperability between un-initiated enterprises, such that clinical results can be exchanged on demand, in real time.

Results and Conclusions The LIDDEx grid can be deployed as an effective construct for thiopurine rules implementation A centrally-placed rules layer in a grid architecture is an ideal manner in which to deploy complex rules-based testing A vendor-driven approach for such consortial efforts is key to continued development of such initiatives