N. A. Loghmanpour 1 , M. K. Kanwar 2 , S. H. Bailey 3 , R. L. Benza 2 ,

1. Development of a novel predictive model for mortality post continuous flow LVAD implant using Bayesian Networks (BN) N. A. Loghmanpour1, M. K. Kanwar2, S. H. Bailey3, R. L. Benza2, J. F. Antaki1, S. Murali2 1Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 2Department of Cardiology, Allegheny General Hospital, Pittsburgh, PA, 3Department of Surgery, Allegheny General Hospital, Pittsburgh, PA  International Society of Heart and Lung Transplantation Annual Meeting April 12, 2014

2. Disclosures • N.A. Loghmanpour: None • M.K. Kanwar: None • S.H. Bailey: None • R.L. Benza: None • J.F. Antaki: None  • S. Murali: None

3. Case Study Patient B Caucasian female 70 years old INTERMACS level 3 NYHA class IV Chronic renal disease Patient A Caucasian male 60 years old INTERMACS level 1 NYHA class IV On ventilator and IABP

4. Motivation • (most) Risk score limitations: • Require a fixed set of data elements • May become outdated or irrelevant • Typically assume linear relationships between variables • Derived from previous VAD technology, and inaccurate when applied to newer VADs • Bayesian Network (BN) models provide robust predictions that correlate pre-operative clinical variables to each otherand final outcome. • Previously demonstrated feasibility of BN to predict 90-day mortality in 2 center study.

5. Infection WBC Count Traditional Statistics High: >11 Normal: 4-11 Low: <4 PRESENT ABSENT Bayesian Networks

6. So, what is the difference? vs. Traditional Bayesian Produce probability estimates grey-zone Consists of a graphical and a quantitative component Robust ability to handle uncertainty and missing data • Produce binary classifications • black and white • Consists of a numerical score • Incomputable if data is missing • Cannot compute HMRS if no Albumin recorded for pt

7. Existing Risk Score: HMRS & DTRS Low risk ≤ 1.58 Medium risk = 1.58 - 2.48 High risk ≥ 2.48 Low risk ≤ 8 Medium risk = 9 - 16 High risk = 17 - 19 Very high risk ≥ 19 (Cowgeret al. JACC, 2013) (Lietz et al. Circulation, 2007)

8. Study Design • INTERMACS: 8050 patients with continuous flow LVADs • Inclusion criteria: All adult patients who received CF LVADs as primary implant • Follow-up data censored for transplant and device explant • Dependent variable: mortality • 30 day • 90 day • 1 year • 2 year

9. Clinical Variable Summary • Inclusion criteria: • >50% completion • Recorded pre-implant

10. Patient Cohort

11. Cardiac Outcomes Risk Assessment (CORA)

12. CORA Model Performance

13. ROC Curve Sensitivity 1-Specificity

14. Case Study: Outcome Patient B HMRS: medium risk CORA: 99% chance of survival at 30 days 96% chance of survival at 90 days Outcome: still alive (implant October 2011) Patient A HMRS: low risk CORA: 66% chance of survival at 30 days 44% chance of survival at 90 days Outcome: died 5 days post-VAD

15. HMRS 2013 4th Quarterly INTERMACS report Survival versus INTERMACS level CORA

16. Limitations • Extensive missing data in many variables • Uneven distribution of outcome • Retrospective bias • Only FDA approved devices included in registry

17. Conclusion • First application of modern machine learning algorithms to a LVAD cohort. • CORA models predictive power exhibited excellent accuracy, sensitivity and specificity. • CORA models have the potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients... • Beta version currently live!

18. Cardiac Health Risk Stratification System (CHRiSS) Demo Site: http://chriss.blenderhouse.com/ Username:ishlt@blenderhouse.com Password: ishlt2014 Contact: nloghman@cmu.edu Thank you: Dr. Kirklin, Dr. Naftel and INTERMACS Funding: R41 HL120428-01 and R01HL086918 NIH grants