Optimized Clinical Decision-making: A Configurable Markov Model for Benign Prostatic Hyperplasia Treatment.

OBJECTIVE

To present a configurable mathematical method to optimize long-term clinical decision-making for benign prostatic hyperplasia.

METHODS

We designed a Markov chain model to simulate the different health states associated with benign prostatic hyperplasia and the transition between these states based on specific interventions: observation, pharmacotherapy, and 4 types of minimally invasive laser surgery. Transition probabilities, disutility scores, and costs for each health state were derived from the literature, expert opinion, and hospital administration data. Disutility was defined as the complement to one of the utility (1-utility), with utility representing the overall quality of life associated with a particular state. Linear programming was used to compute the Markov decision model. Primary outcomes include cost-effectiveness curves comparing the average treatment cost across permitted disutility levels while considering all modeled interventions.

RESULTS

To achieve optimal patient outcomes (low International Prostate Symptoms Score), the model favored surgical interventions and increased costs of treatment. Between different desired disutility values (breakpoints), the model recommends performing 2 recommend treatments in relative proportions to achieve the lowest cost and optimal outcome. The model is limited by its theoretical basis and reliance on literature for transition probabilities and quality of life assessment.

CONCLUSION

This model provides a tool for doctors, administrators, and patients to optimize cost-efficacy when considering multiple treatments and different severities of benign prostatic hyperplasia and may be configured to other disease states or clinical practices. Further studies are necessary to validate this model for real-life application.