A personalized decision aid for prostate cancer shared decision making.

Area of Science:

• Urology
• Medical Informatics
• Decision Science

Background:

• Shared decision-making is crucial for prostate cancer treatment but lacks formal preference assessment.
• Patient values and expectations are challenging to quantify in treatment planning.

Purpose of the Study:

• To develop an automated decision aid for patient-centric prostate cancer treatment selection.
• To integrate patient preferences and values into decision analysis for optimal treatment compatibility.

Main Methods:

• Constructed a patient-centric decision-making template incorporating risk group, health state, and treatment alternatives.
• Utilized a linear additive value function to assess side effects (erectile dysfunction, incontinence) and treatment success.
• Derived toxicity probabilities from clinical trials and created toxicity matrices for treatment options.

Main Results:

• Developed a web-based decision aid that ranks treatment options based on individual patient preferences.
• Demonstrated that no single treatment universally dominates, highlighting the value of a preference-based approach.
• Preliminary use suggests improved patient compliance, side effect tolerance, and decision satisfaction.

Conclusions:

• Presents a novel patient-centric decision aid for prostate cancer, systematically incorporating patient values.
• Enables personalized medicine by ranking treatment options based on preferred outcomes.
• The model is expandable to include genomics and complex therapy scenarios.