Predicting Patients at Risk for Poor Global Outcomes after DT-MCS Therapy

1. Predicting Patients at Risk for Poor Global Outcomes after DT-MCS Therapy Suzanne V. Arnold, MD, MHA Saint Luke’s Mid America Heart Institute/UMKC May 16, 2015

2. Background • LVADs improve survival and quality of life for most patients with end-stage heart failure who are not candidates for heart transplant • There are a group of patients who, despite LVAD, do not improve functionally or live longer following intervention

3. Background • Previous risk models have focused on mortality • Improved quality of life may be an equally or even more important treatment goal • Patients with severe heart failure symptoms were willing to trade over half of their remaining life years to achieve a better quality of life • We sought to define the incidence and predictors of poor global outcome Lewis et al-J Heart Lung Transplant. 2001 Sep;20(9):1016-24

4. Prior Work in TAVR - Similarities • Highly symptomatic patients with expected short survival without intervention • Both interventions have large effects on both of these factors • High residual mortality despite intervention • 1 year mortality 20-25% Kirklin et al-Heart Lung Transplant. 2014;33(6):555-564

5. Prior Work in TAVR - Differences • LVAD patients generally younger • Median age 60’s vs. 80’s • LVADs often placed in emergent settings • LVADs have many subsequent complications that impact outcomes

6. Definition of Combined Outcome Assessed at 1 year after LVAD • Death • Very poor quality of life • KCCQ <45 over the year following LVAD Arnold et al., Circ Cardiovasc Qual Outcomes. 2013 Sep 1;6(5):591-7. Arnold et al., Circulation. 2014 Jun 24;129(25):2682-90.

7. Preliminary Study – Single Center LVAD Patients N=168 –4 patients (under 18, intra-op death, transferred care) Analytic Cohort N=164 Acceptable Outcome N=106 (65%) Poor Outcome N=58 (35%) Severe CVA N=1 (1%) Dead N=37 (23%) Very Poor QoL N=17 (10%) Recurrent HF N=3 (2%) Fendler et al., Circ Cardiovasc Qual Outcomes. 2015;8(Suppl 2):A225

9. INTERMACS VAD Patients v3.0 May 2012-Sept 2013 N=3922 –2208 BTT/possible BTT –45 BTR, rescue, other –31 BiVAD –151 missing all comorbidity data Analytic Cohort N=1487 Died <1 year N=336 (23%) Survived N=1151 –177 No KCCQ follow-up Follow-up KCCQ data N=974 Very Poor QoL N=94 (7%) Acceptable QoL N=880 (67%)

10. Methods • Multivariable logistic model was built to predict a poor outcome at 1 year after DT-LVAD • 18 baseline demographic and clinical variables • Parameter estimates penalized to minimize the effect of over-fitting • Inverse propensity weighting was used to adjust for loss to follow-up

11. Model Non-significant factors: sex, diabetes, stroke, PAD, creatinine, lung disease, albumin, arrhythmias, tricuspid regurgitation, KCCQ missing 0 1 2 3

12. Model Performance Discrimination: C-index=0.64, Validated=0.62 Calibration: Slope=1.01, Intercept=0.01

13. Outcomes by Predicted Risk

15. Uncertainty • Poor outcomes after LVAD are difficult to predict • Coarse characterization at baseline • KCCQ may not fully encompass post-LVAD QoL • Post-LVAD complications play more of a role

16. Impact of Post-LVAD Events When added to the pre-LVAD model: C-index=0.70; validated=0.68

17. Summary • Nearly one-third of patients have poor global outcomes over the year after DT-LVAD • Identifying patients at such a high-risk of this poor outcome to deem the LVAD futile is unlikely • This model could be used to enable more realistic expectations of outcomes for individual patients

18. Thank you