Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Bayesian Techniques, and Clinical Trials

Slide Presentation from the AHRQ 2011 Annual Conference

On September 21, 2011, Gillian D. Sanders and Lurdes Y. Inoue made this presentation at the 2011 Annual Conference. Select to access the PowerPoint® presentation (1.49 MB). Plugin Software Help.

Slide 1

Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Bayesian Techniques, and Clinical Trial

Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Bayesian Techniques, and Clinical Trials

Gillian D Sanders Ph.D. 
Associate Professor of Medicine
Duke University

Lurdes Y Inoue Ph.D.
Associate Professor of Biostatistics
University of Washington

Funded by AHRQ R01-HS018505

Slide 2

Specific Aims

Specific Aims

  1. To develop a generalizable decision modeling framework for the prevention of SCD.
  2. To use Bayesian statistical techniques to devise a model for predicting patient and population health and economic outcomes.
  3. To use the framework and Bayesian model from Specific Aims 1-2, and patient level data from existing clinical trials, to explore timely clinical and policy questions.
  4. To develop a web-based dissemination system to allow providers and policy makers to interact with the decision modeling framework and to explore clinical and policy questions as evidence evolves.

Slide 3

Timing of ICD Clinical Trials

Timing of ICD Clinical Trials

Image: A t-table shows the major ICD RCTs and their timing. Each trial is listed in a row, and then years are shown in the columns with the shaded area showing when the trial was ongoing.

Those in green are considered “secondary prevention trials”—that is trials in patients who have previously experienced a sudden cardiac arrest and therefore are at high risk for a recurrent event

In blue are the “primary prevention” trials—those that look at prophylactically implanting an ICD in patients who are at increased risk compared to the general population—but who haven't had a previous ventricular event

Slide 4

Timing of ICD Clinical Trials

Timing of ICD Clinical Trials

An image showing when the findings from these different trials were published in the clinical literature from 1987-2005 is shown.

Slide 5

Clinical Characteristics of Trial Patients

Clinical Characteristics of Trial Patients

TrialTxNumAgeLVEF%
Ischemic
% NYHA Class
Mean%≥65yMean%≤30IIIIIIIV
AVIDCtrl50965.3357.7630.8258.2285.0761.4926.7211.790
ICD50764.8354.8332.1554.1785.8064.8928.406.710
CABGCtrl45464.9550.0027.0571.15100.056.9518.7617.886.4
ICD44664.0750.0027.1371.08100.055.8819.6816.527.92
CASHCtrl18957.8323.2845.1820.4788.8329.3557.6113.040
ICD9957.4627.2745.8924.2188.8924.4957.1418.370
DEFINITECtrl22958.1133.1921.8493.89017.9060.7021.400
ICD22958.4135.3720.8895.63025.3354.1520.520
MADIT-ICtrl10163.851.4924.5783.17100.032.6749.5017.820
ICD9562.1244.2126.6669.47100.037.8946.3215.790
MADIT-IICtrl49064.5753.4723.1699.59100.038.8034.2322.824.15
ICD74264.4553.5023.17100100.034.8325.3425.444.49
MUSTTCtrl35364.8754.1127.6564.87100.036.4138.4625.130
ICD16765.4256.8927.7265.27100.034.5539.0929.360
SCDHEFTCtrl84758.5833.5325.7160.5753.48070.1329.870
ICD82959.4135.4624.9661.4051.99068.2831.720

Slide 6

Clinical and Policy Questions

Clinical and Policy Questions

  • Controlling for EF, ischemia, age, and NYHA class—are the patients within the available trials similar?
  • Is there evidence that the devices used during the different trials differ in terms of their efficacies?
  • Is there evidence that the ICD is effective in patients:
    • Over 65 years of age? Over 75?
    • With EF > 30%.
    • With non-ischemic disease.
    • With NYHA class I? II? III? IV?
  • Are there specific patient subgroups for which:
    • The ICD is particularly ineffective or effective?
    • Decision makers might benefit from additional trial data?

Slide 7

Methods

Methods

  • Considered patient-level data from 8 trials (MADIT-I, -II, MUSTT, DEFINITE, SCDHeFT, AVID, CASH, CABG).
  • Primary outcome is overall survival.
  • Treatment: ICD versus control.
  • Prognostic variables:
    • Age (years).
    • Ejection fraction (%).
    • NYHA class (I, II, III, IV).
    • Presence of ischemic disease (yes/no).
  • Used Bayesian hierarchical model to explore data from trials and specific subgroups of interest.

Slide 8

Is the ICD Effective?

Does ICD Efficacy Differ Among Trials?

An image showing the different trials are shown. Going up the y axis—with the overall findings combining data from all trials at the top in black.

The x-axis indicates the treatment effect with the vertical dashed line at 0 indicating where no effect was demonstrated from ICD therapy.

Slide 9

Is the ICD Effective in Patients Over 65?

Is the ICD Effective?

TrialHazard RatioProbability
HR ≤ 0.80
2.5%50%97.5%
AVID0.500.640.860.92
CABG-PATCH0.821.041.410.02
CASH0.560.831.250.43
DEFINITE0.390.620.950.87
MADIT-I0.260.450.710.99
MADIT-II0.480.620.820.95
MUSTT0.280.430.641.00
SCD-HeFT0.600.730.910.82
Overall0.410.651.030.82

Slide 10

Is the ICD Effective in Patients With EF > 30%

Is the ICD Effective in Patients Over 65?

Image of a line chart which looks at the evidence of ICD efficacy in patients over age 65 and less than 75.

Slide 11

Is the ICD Effective in NYHA II Patients?

Is the ICD Effective in Patients With EF > 30%

Image of a line chart which looks at the evidence of ICD efficacy in patients with an ejection fraction greater than 30%.

Slide 12

Is the ICD Effective in NYHA III Patients?

Is the ICD Effective in NYHA II Patients?

Image of a line chart which looks at the evidence of ICD efficacy in NYHA Class II Patients.

Slide 13

Is the ICD Effective in NYHA IV Patients?

Is the ICD Effective in NYHA III Patients?

Image of a line chart which looks at the evidence of ICD efficacy in NYHA Class III Patients.

Slide 14

Is the ICD Effective in NYHA IV Patients?

Is the ICD Effective in NYHA IV Patients?

Image of a line chart which looks at the evidence of ICD efficacy in NYHA Class IV Patients.

Slide 15

Is the ICD Effective in NYHA IV Patients?

Is the ICD Effective in NYHA IV Patients?

TrialHazard RatioProbability
HR ≤ 0.80
2.5%50%97.5%
AVID0.030.8418.640.48
CABG-PATCH0.341.003.070.35
CASH0.030.5711.700.61
DEFINITE0.040.7514.640.53
MADIT-I0.030.6712.220.56
MADIT-II0.371.635.370.16
MUSTT0.040.8619.270.47
SCD-HeFT0.020.7713.960.51
Overall0.200.843.430.49

Slide 16

Is the ICD Effective in Ischemic Patients?

Is the ICD Effective in Ischemic Patients?

An image of a line graph showing the evidence supporting ICD effectiveness in patients with ischemic disease is shown.

Slide 17

Are Patients Within The Trials Similar?

Are Patients Within The Trials Similar?

An image of a line graph showing the estimated posterior baseline survival functions under each trial and over all trials is shown.

Slide 18

ICD Effect in Specific Patient Subgroups

ICD Effect in Specific Patient Subgroups

Age GroupEFNYHAIschemiaHazard RatioProbability
HR ≤ 0.80
LowerMedianUpper
< 65< 30%INo0.290.591.090.84
< 65< 30%IYes0.240.571.210.80
< 65< 30%IINo0.260.621.290.77
75+< 30%INo0.230.581.400.78
75+< 30%IYes0.200.561.520.77

Slide 19

Clinical and Policy Research Priorities

Clinical and Policy Research Priorities

  • Are there specific patient subgroups for which policy makers might benefit from additional clinical trial data and how best can such trials be designed given the available prior information from the existing clinical trials?
  • Are there clinical subgroups within the population at risk for SCD where the ICD appears to be particularly effective? Cost effective? Futile?
  • How best can we use current (and novel) risk stratification techniques to either rule in "low-risk" patients who are currently ineligible for ICD therapy, or rule out current "high-risk" patients who are currently recommended ICD implantation?
  • What are effects of comorbid diseases on the effectiveness and cost effectiveness of ICD use in the elderly population for primary or secondary prevention of SCD?
  • Are the outcomes observed in the community predicted by the available clinical trial evidence?
  • What is the cumulative survival benefit from SCD prevention therapies in the US?
  • How can the clinical trial data be best used to predict the prognosis of patients within the CMS ICD registry and as longitudinal data becomes available for the registry participants—how does our modeling framework predict the patients' outcomes?

Slide 20

Next Steps

Next Steps

  • Incorporate data from DINAMIT trial.
  • Combine full patient level data sets.
  • Further develop underlying Bayesian models.
  • Explore main prognostic variables of: sex, age, NYHA class, LVEF, prior MI, and QRS duration.
  • Look at additional endpoints of sudden cardiac death, rehospitalizations, quality of life, appropriate/ inappropriate shocks.
  • Explore other potential prognostic variables including race, time from MI /CABG, renal disease, and ICD programming and types.
  • Combine with decision analytic framework in a format that clinical providers and policymakers can use to explore the underlying evidence, our models, and the findings.
Page last reviewed March 2012
Internet Citation: Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Bayesian Techniques, and Clinical Trials. March 2012. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2011/sanders-inoue/index.html