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

Mu Zi Liang; Mei Ling Liu; Ying Tang; Alex Molassiotis; M Tish Knobf; Peng Chen; Guang Yun Hu; Zhe Sun; Yuan Liang Yu; Zeng Jie Ye

doi:10.1016/j.ejon.2023.102374

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

Eur J Oncol Nurs. 2023 Oct:66:102374. doi: 10.1016/j.ejon.2023.102374. Epub 2023 Jun 25.

Authors

Mu Zi Liang¹, Mei Ling Liu², Ying Tang³, Alex Molassiotis⁴, M Tish Knobf⁵, Peng Chen⁶, Guang Yun Hu⁷, Zhe Sun⁸, Yuan Liang Yu⁹, Zeng Jie Ye¹⁰

Affiliations

¹ Guangdong Academy of Population Development, Guangzhou, China.
² Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
³ Institute of Tumor, Guangzhou University of Chinese Medicine, Guangzhou, China.
⁴ College of Arts, Humanities and Education, University of Derby, Derby, United Kingdom.
⁵ School of Nursing, Yale University, Orange, CT, United States.
⁶ Basic Medical School, Guizhou University of Traditional Chinese Medicine, Guiyang, China.
⁷ Army Medical University, Chongqing Municipality, China.
⁸ The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
⁹ South China University of Technology, Guangzhou, China.
¹⁰ School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, China. Electronic address: zengjieye@qq.com.

PMID: 37499404
DOI: 10.1016/j.ejon.2023.102374

Abstract

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.

Keywords: Exploratory; Growth; Heterogeneity; Newly diagnosed cancer; Patterns; Piecewise growth mixture model analysis; Psycho-oncology; Quality of life; Resilience.