Purpose: To develop an algorithm that extends survival probabilities based on the Seattle Heart Failure Model (SHFM) to generate estimates of survival time and mode of death for its integration in a customizable model designed to evaluate the cost-effectiveness of patient-centered interventions for heart failure (TEAM-HF).
Method: The SHFM is a multivariate risk model that has been shown to provide accurate 1-, 2-, and 3-year estimates for the survival of heart failure patients. These estimates are obtained by first calculating a SHFM score, which is based on various demographic, clinical and laboratory characteristics, and then using this score within an exponential hazard function. Since medical costs incurred from sudden cardiac death differ from other non-sudden modes of death, it is desirable to have the capability of accounting for different modes of death in the TEAM-HF model. To accomplish this, we made the immediate modification of declaring a cause-specific hazard function in a competing risks setting. Furthermore, in an effort to obtain more realistic long-term projections, we replaced the standard exponential hazard function with a Gompertz-based hazard function. Model parameters were then calibrated using the pooled data from several randomized trials and prospective cohort studies of heart failure patients.
Result: Our model suggests that the predicted mode of death changes across survival time and SHFM scores. We have integrated this procedure within the TEAM-HF cost-effectiveness model that generates virtual cohorts of patients by sampling sets of patient characteristics from a multivariate distribution, wherein each characteristic is defined in terms of its mean and standard deviation, and the global correlation structure is derived from a known target population. For a particular SHFM score, the model calculates the expected survival time, as well as the conditional and unconditional probabilities of death associated with each cause of death. For simulated patients with a particular SHFM score in the cost-effectiveness model, their mode of death is probabilistically sampled conditional on their randomly sampled survival time within a Monte Carlo framework.
Conclusion: The integration of this survival modeling procedure within the TEAM-HF cost-effectiveness model allows it to more accurately make cost and survival predictions for various heart failure interventions (e.g. implantable cardioverter defibrillators) that may differentially impact a patient’s mortality risk and their mode of death.
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