Bayesian change point detection in monitoring cardiac surgery outcomes

Precise identification of the time when a clinical process has changed, a control chart's signal, enables clinicians to search for a potential special cause more effectively. In this article, we develop a change point estimation method for Bernoulli processes in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point model and Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters. The performance of the Bayesian estimator is investigated through applications on clinical data. We monitor outcomes of cardiac surgery and angioplasty procedures using Bernoulli exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts. We then identify the time of changes in prior signals obtained from charts. Study of the known potential causes of changes in the outcomes reveals that estimated change points and shifts in the known causes are coincident.