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

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

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

Motivation (most) Risk score limitations: – Require a fixed set of data elements May become outdated or irrelevant – Typically assume linear relationships between variables – Derived from previous VAD technology, and inaccurate when applied to newer VADs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 other and final outcome. Previously demonstrated feasibility of BN to predict 90-day mortality in 2 center study.

Infection % likelihood Present50 Absent50 WBCInfection PresentInfection Absent High90%4% Normal6% Low4%90% Bayesian Networks High: >11 Normal: 4-11 Low: <4 WBC Count PRESENT ABSENT Traditional Statistics

So, what is the difference? Traditional 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 Bayesian Produce probability estimates – grey-zone Consists of a graphical and a quantitative component Robust ability to handle uncertainty and missing data vs.

DTRSPoints Platelet ≤ 148*10 3 μl7 Albumin ≤ 3.3 g/dl5 INR > 1.14 Vasodilator therapy4 mPAP ≤ 25mmHg3 AST > 45 U/ml2 Hct ≤ 34%2 BUN > 51 U/dl2 No IV inotropes2 (Lietz et al. Circulation, 2007) Existing Risk Score: HMRS & DTRS Low risk ≤ 8 Medium risk = High risk = Very high risk ≥ 19 Low risk ≤ 1.58 Medium risk = High risk ≥ 2.48 (Cowger et al. JACC, 2013) HMRSWeight Age (in decades) Albumin Creatinine0.74 INR1.136 Center LVAD volume0.807

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

No. (%) N=111 Demographics: age interval, gender …21 (19) Co-morbidities: malnutrition, cancer…30 (27) Laboratory: blood type, sodium, INR, albumin… 16 (14) Hemodynamics: cardiac output, LVEF, RVEF, heart rate… 21 (19) Medication: ACEI, BB, warfarin, inotrope …13 (12) Quality of Life: EuroQoL mobility, pain, anxiety… 10 (9) Clinical Variable Summary Inclusion criteria: >50% completion Recorded pre- implant

TotalDeath 30 day (4.8%) 90 day (9.5%) 1 year (20.3%) 2 year (27.3%) Patient Cohort

Cardiac Outcomes Risk Assessment (CORA)

CORA Model Performance EndpointAccuracy (%)ROC (%)Kappa 30 day day year year

ROC Curve 1-Specificity Sensitivity

Case Study: Outcome 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 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)

HMRS CORA th Quarterly INTERMACS report Survival versus INTERMACS level

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

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!

Thank you: Dr. Kirklin, Dr. Naftel and INTERMACS Funding: R41 HL and R01HL NIH grants Username: Password: ishlt2014 Contact: Cardiac Health Risk Stratification System (CHRiSS) Demo Site: