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RVAD Use Prediction in LVAD Recipients: Modeling and Predictors

Explore predictive modeling of RVAD use in LVAD recipients, including risk factors and model building techniques. Understand the complexity of predicting RVAD use and implications for clinical practice.

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RVAD Use Prediction in LVAD Recipients: Modeling and Predictors

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  1. Predictive Modeling of RVAD Use in LVAD Recipients Michael S Kiernan, MD, SM Assistant Professor, Tufts University Medical Director Ventricular Assist Device Program, Tufts Medical Center

  2. Presenter Disclosure Information • I will notdiscuss off label use or investigational use in my presentation. • I havefinancial relationships to disclose. • Employee of: None • Consultant for: None • Stockholder in: None • Research support: None • Honoraria from: ThoratecCorporation

  3. Timing and of RVAD Planned Early Durable Temporary Delayed Morgan Ann Thorac Surg 2004;77:859

  4. Rate RHF: 15-44% Kalogeropoulos JHLT 2015;34(12):1595

  5. Performance of risk prediction models tested in an independent cohort Kalogeropoulos JHLT 2015;34(12):1595

  6. Complex etiology of post-LVAD RH failure RV Dysfunction LVAD & Ventricular Inter-dependence RHF RAP Afterload (PVR) Lampert. JHLT 1025;34:1123 Goldstein. ATS 2003;75:S42

  7. Consort diagram of INTERMACS study cohort All Patients (N=11,162) • 1,186 Patients • 610 pulsatile LVAD • 323 pulsatile BiVAD • 253TAH Primary CF-LVAD (N=9976) Any RVAD (5.2%) (N=521) Early RVAD (≤ 14 d) (3.9%) (N=386) No RVAD (94.8%) (N=9455)

  8. Non-adjusted associations of continuous variables with outcomes: If straight line, then linear fit is ok • Assumes independent variables are linearly related to log odds of the outcome

  9. Model building: Handling of nonlinear relationships between predictors and outcomes Dichotomization median

  10. Model building: Handling of nonlinear relationships between predictors and outcomes Truncate beyond a ‘cut-point’

  11. Working with large databases:the problem of missing data

  12. Building a predictive model: handling missing data 1st pass at a multivariable model included all variables from Table (even those with >20% missing) • Stepwise selection with a p-value of 0.10 to enter the model and p=0.05 to stay in the model

  13. Preliminary model of RVAD predictors • Missing data is often not missing at random • Final model utilized multiple imputation which allowed us to utilize observations on all available subjects • Created 10 new datasets and re-estimated the effect size (ORs)

  14. Sensitive analysis: How well does model predicts the composite endpoint of death or RVAD within 2 weeks of LVAD surgery? [n=608/9976 with composite event (6.1%) vs. n=386 (3.8%) with RVAD]

  15. Conclusions • Predicting RVAD use is complicated due to complex physiology of RHF • Low prevalence of RVAD use will limits clinical utility of risk scores • Many previously identified risk factors from smaller studies were confirmed as risk factors in the INTERMACS model • It takes an expert to build a risk prediction model • (Thank you Robin Ruthazer!) • Next steps: • Create an INTERMACS validation cohort • Describe the late RVAD recipients (2%) • Evaluate less severe RHF not resulting in RVAD

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