A Personalized Clinical Dynamic Prediction Model to Characterize Prognosis for Patients With Localized Prostate Cancer: Analysis of the CHHiP Phase 3 Trial.

Purpose: The CHHiP trial assessed moderately hypofractionated radiation therapy in localized prostate cancer. We utilized longitudinal prostate-specific antigen (PSA) measurements collected over time to evaluate and characterize patient prognosis.

Methods And Materials: We developed a clinical dynamic prediction joint model to predict the risk of biochemical or clinical recurrence. Modeling included repeated PSA values and adjusted for baseline prognostic risk factors of age, tumor characteristics, and treatment received. We included 3071 trial participants for model development using a mixed-effect submodel for the longitudinal PSAs and a time-to-event hazard submodel for predicting recurrence of prostate cancer. We evaluated how baseline prognostic factor subgroups affected the nonlinear PSA levels over time and quantified the association of PSA on time to recurrence. We assessed bootstrapped optimism-adjusted predictive performance on calibration and discrimination. Additionally, we performed comparative dynamic predictions on patients with contrasting prognostic factors and investigated PSA thresholds over landmark times to correlate with prognosis.

Results: Patients who developed recurrence had generally higher baseline and overall PSA values during follow-up and had an exponentially rising PSA in the 2 years before recurrence. Additionally, most baseline risk factors were significant in the mixed-effect and relative-risk submodels. PSA value and rate of change were predictive of recurrence. Predictive performance of the model was good across different prediction times over an 8-year period, with an overall mean area under the curve of 0.70, mean Brier score of 0.10, and mean integrated calibration index of 0.048; these were further improved for predictions after 5 years of accrued longitudinal posttreatment PSA assessments. PSA thresholds <0.23 ng/mL after 3 years were indicative of a minimal risk of recurrence by 8 years.

Conclusions: We successfully developed a joint statistical model to predict prostate cancer recurrence, evaluating prognostic factors and longitudinal PSA. We showed dynamically updated PSA information can improve prognostication, which can be used to guide follow-up and treatment management options.