Heterogeneity in resilience patterns and its prediction of 1-year quality of life outcomes among patients with newly diagnosed cancer: An exploratory piecewise growth mixture model analysis

Purpose: This study was designed to explore the impact of a new cancer diagnosis on resilience of patients and whether the resilience patterns could predict Quality of Life (QoL) in the first year.

Methods: An exploratory linear piecewise growth mixture modeling (PGMM) with one hypothetical dot (3 months since diagnosis, T1) was employed to identify different resilience patterns and growth in 289 patients with different cancer diagnoses at five assessment occasions (T0-T4). Logistic regression analysis was performed to select potential predictors and receiver operating characteristic (ROC) curve analysis was utilized to test PGMM's discriminative ability against 1-year QoL.

Results: Five discrete resilience trajectories with two growing trends were identified, including "Transcendence" (7.3%), "Resilient" (47.4%), "Recovery" (18.7%), "Damaged" (14.9%) and "Maladaption" (11.8%). Advanced stage, colorectal cancer, and receiving surgery therapy were significant predictors of negative resilience trajectories ("Damaged" or "Maladaption"). Discriminative ability was good for PGMM (AUC = 0.81, 95%CI, 0.76-0.85, P < 0.0001).

Conclusion: Heterogeneity is identified in resilience growth before and after 3 months since diagnosis. 26.7% newly diagnosed patients need additional attention especially for those with advanced colorectal cancer and receiving surgery therapy.