Cardiovascular (CV) disease cohort modeling has traditionally used either decision trees or Markov models. Decision trees are not ideal to accurately describe the nature of a chronic disease involving multiple CV events. Markov models can track multiple CV event history by including additional tunnel or composite health states, which increases complexity. Additionally, both types of models are limited to a maximum of one CV event (transition) per cycle. We present a novel simple approach for modeling multiple CV event history, based on the concept of partitioned survival (PS). PS involves modeling disease progression using survival functions. In our case, a cohort of patients with history of CV disease were at risk of “progression” to a first (subsequent) fatal or non-fatal CV event (in this case, considering proportional hazards survival functions) or non-CV death. Patients who “progressed” and were alive were at a higher risk of “progression” to a second (subsequent) CV event, or would die from non-CV causes. By nesting 4 instantaneous PS models, we allowed up to 4 CV events to occur within an annual cycle, while tracking multiple CV event history. Model predictions were then cross-validated against a recently published more complex Markov model used in an economic evaluation of evolocumab (Fonarow 2017). For patients in the comparator arm, predicted 10-year CV risk was 55% in Fonarow 2017 vs 55% in the simplified model; predicted lifetime (per-patient) event rate was 1.8 (1.2 non-fatal, 0.6 fatal) vs 1.7 (1.2 non-fatal, 0.5 fatal), respectively; predicted survival was 14.3 vs 14.4 years, respectively. Results from our simplified model are aligned with those from a more complex Markov model, allowing multiple CV event history tracking, the occurrence of multiple CV events within a cycle and the flexibility in the use of survival functions, whilst preserving some of the simplicity of decision trees.