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

MCS TherapyMCS Therapy

Suzanne V. Arnold, MD, MHASuzanne V. Arnold, MD, MHA

Saint Luke’s Mid America Heart Institute/UMKCSaint Luke’s Mid America Heart Institute/UMKCMay 16, 2015May 16, 2015

BackgroundBackground

• 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

BackgroundBackground

• 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

Prior Work in TAVR - SimilaritiesPrior 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

Prior Work in TAVR - DifferencesPrior 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

Definition of Combined OutcomeDefinition 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.

Preliminary Study – Single CenterPreliminary Study – Single Center

LVAD Patients

N=168

LVAD Patients

N=168

Analytic Cohort

N=164

Analytic Cohort

N=164

Acceptable Outcome

N=106 (65%)

Acceptable Outcome

N=106 (65%)Poor Outcome

N=58 (35%)

Poor Outcome

N=58 (35%)

Dead

N=37 (23%)

Dead

N=37 (23%)

Very Poor QoL

N=17 (10%)

Very Poor QoL

N=17 (10%)Recurrent HF

N=3 (2%)

Recurrent HF

N=3 (2%)

–4 patients (under 18, intra-op death, transferred care)

–4 patients (under 18, intra-op death, transferred care)

Severe CVA

N=1 (1%)

Severe CVA

N=1 (1%)

Fendler et al., Circ Cardiovasc Qual Outcomes. 2015;8(Suppl 2):A225

INTERMACSINTERMACSVAD Patients v3.0

May 2012-Sept 2013

N=3922

VAD Patients v3.0May 2012-Sept 2013

N=3922

Analytic Cohort

N=1487

Analytic Cohort

N=1487

Died <1 year

N=336 (23%)

Died <1 year

N=336 (23%)Survived

N=1151

Survived

N=1151

Very Poor QoL

N=94 (7%)

Very Poor QoL

N=94 (7%)

Acceptable QoL

N=880 (67%)

Acceptable QoL

N=880 (67%)

–2208 BTT/possible BTT –45 BTR, rescue, other –31 BiVAD –151 missing all comorbidity data

–2208 BTT/possible BTT –45 BTR, rescue, other –31 BiVAD –151 missing all comorbidity data

Follow-up KCCQ data

N=974

Follow-up KCCQ data

N=974

–177 No KCCQ follow-up –177 No KCCQ follow-up

MethodsMethods

• 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

OR (95% CI) P-value

Age (per +10 years) 1.23 (1.08-1.40) 0.002

KCCQ-12 Summary Score (per -10 pts) 1.10 (1.02-1.18) 0.010

BMI (per +5 kg/m2) 1.12 (1.02-1.24) 0.017

INTERMACS Patient Profile 1-2 1.27 (0.97-1.65) 0.078

History of solid organ cancer 1.39 (0.96-2.01) 0.079

Previous cardiac operation 1.23 (0.95-1.59) 0.119

Hemoglobin (per +1 g/dL) 0.95 (0.89-1.02) 0.170

History of illicit drug use/alcohol abuse 1.29 (0.88-1.89) 0.189

ModelModel

0 1 2 3

Non-significant factors: sex, diabetes, stroke, PAD, creatinine, lung disease, albumin, arrhythmias, tricuspid regurgitation, KCCQ missing

Discrimination:C-index=0.64, Validated=0.62Calibration: Slope=1.01, Intercept=0.01

Discrimination:C-index=0.64, Validated=0.62Calibration: Slope=1.01, Intercept=0.01

Model PerformanceModel Performance

Outcomes by Predicted RiskOutcomes by Predicted Risk

Characteristics by Risk GroupLow Risk

n=133Intermediate Risk

n=1040High Risk

n=137

Age Group

<50 53% 11% 2%

50-59 21% 17% 9%

60-69 17% 37% 34%

70+ 9% 36% 55%

BMI (kg/m2) 26.7±6.5 28.2±6.3 32.0±11.5

Pulmonary disease 11% 13% 21%

Atrial arrhythmia 12% 24% 32%

Severe diabetes 3% 12% 32%

Peripheral vascular disease 3% 8% 22%

Cancer 2% 10% 27%

Creatinine (mg/dL) 1.2±0.4 1.5±0.6 1.7±0.7

Previous cardiac surgery 14% 41% 74%

INTERMACS 1-2 26% 41% 77%

KCCQ  Pre-Implant 54.7±22.1 32.7±19.2 19.1±13.4

UncertaintyUncertainty• 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

Impact of Post-LVAD EventsImpact of Post-LVAD EventsPoor Global

OutcomeAcceptable Outcome

P-value

Major bleeding 47% 40% 0.010

Stroke 21% 5.5% <0.001

OR (95% CI) P-value

Major bleeding 1.19 (0.92-1.54) 0.187

Stroke 3.87 (2.61-5.73) <0.001

When added to the pre-LVAD model:

C-index=0.70; validated=0.68

SummarySummary

• 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

Thank youThank you