Predictive Analytics in Spine Surgery: How Risk-Taking Behavior Shapes Surgical Decisions

Area of Science:

• Spine Surgery
• Medical Decision Making
• Psychosocial Factors

Background:

• Understanding patient risk-taking and tolerance for complications is crucial for tailoring spine surgery recommendations.
• Psychosocial factors, alongside clinical metrics, can inform surgical decision-making for spinal pathologies.

Purpose of the Study:

• To develop a predictive model integrating clinical and psychosocial factors for spine surgery decisions.
• To investigate if higher risk-taking tendencies correlate with choosing high-risk, high-reward spine surgeries.

Main Methods:

• A cross-sectional study of 797 participants using hypothetical spinal surgery scenarios.
• Participants completed surveys on demographics, disability (Oswestry Disability Index), and risk-taking (Domain-Specific Risk-Taking survey).
• An XGBoost model predicted surgery likelihood based on perceived risk, improvement probability, and psychosocial factors.

Main Results:

• The predictive model achieved an R-squared of 0.75.
• Lower complication risk, higher improvement probability, younger age, and higher BMI predicted surgery likelihood.
• Lower scores in financial and recreational risk-taking domains were associated with choosing surgery.

Conclusions:

• Integrating psychosocial dimensions into predictive models personalizes surgical risk discussions.
• Aligning treatment recommendations with patient values and risk perceptions enhances patient-centered care in spine surgery.