One way to provide effective healthcare services for patients with advanced cancer to improve their quality of life and survival: Prediction modeling using machine learning techniques and causal relationship between self-management strategy, health-related quality of life, and survival.

Abstract

e24005 Background: In healthcare services, self-management strategies can play a crucial role in enhancing the health-related quality of life (HRQoL) or survival of cancer patients, regardless of their stage or treatment plan. However, there needs to be more research addressing the relationship between the strategies, HRQoL, and survival. To address this gap, we investigated the relationship between the strategies, HRQoL, and survival using machine learning techniques (MLT) and path analysis with structural equation modeling (SEM). Methods: This study was a secondary analysis of a randomized controlled trial (RCT) to provide early palliative care to individuals with advanced cancer (ClinicalTrials.gov, ID NCT03181854). We employed dimensional multiple Cox proportional hazard regression analyses to identify the crucial predictors of 1-year survival. By integrating the critical predictors extracted from the Cox regression models, we established a final survival prediction model using the eXtreme Gradient Boost (XGBoost) and the BorutaSHAP in MLT. Furthermore, we used path analysis to investigate the causal relationship between the strategies, HRQoL, and survival. Results: The univariate dimensional Cox models indicated that factors such as worse ECOG performance status (hazard ratio (HR) = 2.18), living alone (HR = 1.74), male (HR = 1.65), low global QoL (HR = 1.81), high dyspnea (HR = 2.30), high pain (HR = 1.75), high appetite loss (HR = 1.94), high constipation (HR = 2.11), high depression (HR = 2.79) at baseline, and no significant positive changes in emotional functioning (HR = 1.88) negatively impacted survival. The final XGBoost model for survival prediction using repeated K-fold methods (K = 5, repeats = 20, area under the receiver operating characteristic curve (AUROC) = 0.76, area under the precision-recall curve (AUPRC) = 0.87) and bootstrapping (n = 300, AUROC = 0.81, AUPRC = 0.91) showed high survival prediction performance. Additionally, the path analysis revealed a causal relationship between self-management strategies, HRQoL, and survival in patients with advanced cancer ( p-value = 0.005). HRQoL was found to have a complete mediating relationship between the strategies and survival. Self-management strategies did not directly correlate with survival but showed an impact on survival through HRQoL. Conclusions: To the best of our knowledge, this is the first study to uncover the connection between self-management strategies, HRQoL, and survival in patients with advanced cancer using MLT and path analysis. These findings have the potential to deepen our understanding of self-management strategies and aid healthcare providers in providing improved cancer care for patients with advanced cancer. Clinical trial information: NCT03181854 .