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

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 StudyPatient ACaucasian male60 years oldINTERMACS level 1NYHA class IVOn ventilator and IABP

Patient BCaucasian female70 years oldINTERMACS level 3NYHA class IVChronic 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Motivation• (most) Risk score limitations:– Require a fixed set of data elements • May become outdated or irrelevant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

Infection % likelihood Present 50Absent 50

WBC Infection Present Infection Absent

High 90% 4%

Normal 6% 6%

Low 4% 90%

Bayesian Networks

High: >11Normal: 4-11Low: <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.

DTRS PointsPlatelet ≤ 148*103μl 7Albumin ≤ 3.3 g/dl 5INR > 1.1 4Vasodilator therapy 4mPAP ≤ 25mmHg 3AST > 45 U/ml 2Hct ≤ 34% 2BUN > 51 U/dl 2No IV inotropes 2

(Lietz et al. Circulation, 2007)

Existing Risk Score: HMRS & DTRS

Low risk ≤ 8 Medium risk = 9 - 16High risk = 17 - 19Very high risk ≥ 19

Low risk ≤ 1.58Medium risk = 1.58 - 2.48High risk ≥ 2.48

(Cowger et al. JACC, 2013)

HMRS WeightAge (in decades) 0.0274Albumin -0.723Creatinine 0.74INR 1.136Center LVAD volume 0.807

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

Total Death30 day 8007 387 (4.8%)90 day 7761 737 (9.5%)1 year 6575 1334 (20.3%)2 year 6099 1667 (27.3%)

Patient Cohort

Cardiac Outcomes Risk Assessment (CORA)

CORA Model Performance

Endpoint Accuracy (%) ROC (%) Kappa30 day 96.3 89.4 0.4490 day 91.4 81.2 0.381 year 84.0 79.4 0.462 year 78.4 80.0 0.43

ROC Curve

1-Specificity

Sens

itivi

ty

Case Study: OutcomePatient AHMRS: low risk

CORA: 66% chance of survival at 30 days44% chance of survival at 90 days

Outcome: died 5 days post-VAD

Patient BHMRS: 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

2013 4th 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 HL120428-01 and R01HL086918 NIH grants

http://chriss.blenderhouse.com/Username: ishlt@blenderhouse.comPassword: ishlt2014Contact: nloghman@cmu.edu

Cardiac Health Risk Stratification System (CHRiSS) Demo Site: