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

Area of Science:

• Mathematical modeling in healthcare
• Decision analysis for clinical practice

Background:

• Benign prostatic hyperplasia (BPH) management involves multiple treatment options with varying costs and outcomes.
• Optimizing long-term clinical decision-making for BPH requires a systematic approach to compare interventions.

Purpose of the Study:

• To develop a configurable mathematical method for optimizing long-term clinical decision-making in benign prostatic hyperplasia (BPH).
• To evaluate the cost-effectiveness of different BPH interventions using a decision model.

Main Methods:

• A Markov chain model was developed to simulate BPH health states and transitions.
• Interventions included observation, pharmacotherapy, and four types of minimally invasive laser surgery.
• Linear programming computed the Markov decision model, analyzing transition probabilities, disutility, and costs.

Main Results:

• The model indicated that surgical interventions favored optimal patient outcomes (low International Prostate Symptoms Score) but incurred higher treatment costs.
• For specific disutility levels, the model recommended a combination of two treatments in relative proportions for the lowest cost and best outcome.
• Model limitations include its theoretical nature and reliance on literature data for probabilities and quality of life.

Conclusions:

• The developed model serves as a tool for optimizing cost-efficacy in BPH treatment selection for clinicians, administrators, and patients.
• The model's configurability allows adaptation to various disease severities and potentially other clinical scenarios.
• Further research is needed to validate the model's real-world applicability.