Abstract Background Currently, risk stratification and effective management of heterogeneous patients with cancer based on patient-reported
Abstract Background Currently, risk stratification and effective management of heterogeneous patients with cancer based on patient-reported outcomes (PROs), used to evaluate clinical efficacy and outcomes, are relatively rare and urgently needed. We aimed to explore latent risk subgroups and delineate multidimensional networks of symptoms and functions based on PROs in this study. Methods Patients with cancer were recruited from eight hospitals in two Provinces in China. The PROs measure for patients with cancer (CA-PROM) was used to measure patients’ HRQoL, symptoms, and functions. Latent profile analysis (LPA) was used to explore latent risk subgroups using four fitting indicators on the patients’ HRQoL. Network model (NM) of multidimensional symptoms and functions was applied at the item level of the CA-PROM. The expected influence (EI), bridge EI, and predictability of each node were used to evaluate the centrality and predictability of NM. Network accuracy and stability were tested using a case-dropping bootstrap procedure. Finally, a network comparison test (NCT) was conducted to examine whether network characteristics differed among the various risk subgroups. Results In total, 1,404 valid questionnaires were collected. Three latent risk subgroups were determined based on the four fitting indicators. Considering the mean difference in HRQoL, subgroups 1, 2, and 3 were indicated as high-risk (n = 196), low-risk (n = 716), and medium-risk (n = 492) subgroups, respectively. There were statistically significant differences in most demographic data, disease conditions, and treatment among three latent risk subgroups. Network analysis revealed that some symptoms and functions (e.g., despair, gastrointestinal abnormalities, care and support from their families and friends, appetite, and so on) played more important roles in the heterogeneity of HRQoL for Chinese patients w ith cancer. But the performance of these symptoms and functions reported by patients varied among three subgroups. Network accuracy and stability basically met the preset criteria. NCT results showed that edge differences were observed in five nodes, and seven nodes with different EI values could be informative for targeted support for the patients of different clusters. Conclusion Different central and bridge symptoms or functions in multidimensional networks of PROs may serve as potential targets for personalized interventions among patients with cancer who are at different risk levels of HRQoL.
