Measurement invariance of the 10-item resilience scale specific to cancer in Americans and Chinese: A propensity score-based multidimensional item response theory analysis

Sentinel and networked symptoms in patients with breast cancer undergoing chemotherapy

PURPOSE

It was designed to identify the symptom clusters and sentinel symptoms among patients with breast cancer receiving chemotherapy at the community level, and to explore core and bridge symptoms at the global level.

METHODS

A cross-sectional survey was conducted using the MD Anderson Symptom Inventory. Patients with breast cancer receiving chemotherapy, recruited from the "Be Resilient to Breast Cancer" project between January 2023 and December 2023, were included in the study. Symptom clusters and their sentinel symptoms were identified using exploratory factor analysis and Apriori algorithm. Core and bridge symptoms were identified using network analysis.

RESULTS

A total of 468 patients with breast cancer participated in the current study. At the community level, three symptom clusters and their corresponding sentinel symptoms were identified: a gastrointestinal symptom cluster (with nausea as the sentinel symptom), a psycho-sleep-related symptom cluster (with distress as the sentinel symptom), and a neurocognition symptom cluster (with dry mouth as the sentinel symptom). At the global level, fatigue emerged as the core symptom, while disturbed sleep and lack of appetite as bridge symptoms.

CONCLUSION

Addressing nausea, distress, and dry mouth are imperative for alleviating specific symptom clusters at the community level. Furthermore, targeting fatigue, disturbed sleep, and lack of appetite are crucial to break the interactions among diverse symptoms at the global level.

The interplay between stigma and sleep quality in breast cancer: A cross-sectional network analysis

PURPOSE

Stigma, a subjective internal shame, arises from the association of cancer with death. Sleep quality can be considered a product of stigma. However, the extent of overlap or difference between the two remains unclear.

METHODS

In total, 512 survivors with breast cancer were recruited from the "Be Resilient to Breast Cancer" project between May and August 2023. This study estimated the stigma, sleep quality, and their relationship by conducting a cross-sectional network analysis. The social impact scale and Pittsburgh Sleep Quality Index scale were employed in this study.

RESULTS

The core symptom for stigma from the network analysis was alienation by people (Strength = 1.213, Betweenness = 13, Closeness = 0.00211). The core symptom for sleep quality were the sleep quality (Str = 1.114, Bet = 17, Clo = 0.01586). Regarding the combination network, results showed that self-isolation and daytime dysfunction were the bridge nodes and that daytime dysfunction was positively associated with feeling less capable than before (according to self) (r = 0.15).

CONCLUSION

Our study demonstrates the core symptoms in different symptomatic networks, which can be targeted for treatment personalization and aid in the improvement of sleep quality and stigma in breast cancer patients.

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.

The Resilience Scale for Parents of Children with Cancer: Scale development and psychometric evaluation

OBJECTIVE

This study aimed to develop a Resilience Scale for Parents of Children with Cancer (RSP-CC) designed specifically for parents of children with cancer and to evaluate its psychometric properties.

METHODS

Based on the Resilience Model for Families of Children with Cancer, items were created after an extensive literature review and evaluated through expert consultation and a pilot study. Psychometric evaluation was conducted with the parents of 601 children with cancer in the pediatric hematology oncology services of different hospitals in the Ankara and Kayseri provinces in Turkey.

RESULTS

A 4-factor structure was illustrated by exploratory factor analysis and confirmed by confirmatory factor analysis, explaining 62.192% of the total variance. In the reliability analysis of the scale, Interclass correlation = 0.993 and Cronbach's alpha = 0.994 were found for the scale. Robust correlation coefficients were found between test-retest, and the correlation between the two measurements was statistically significant (r = 0.990; p ˂ 0.001). As a result, the validity of a 24-item scale structure consisting of 4 dimensions was validated.

CONCLUSIONS

It may be said that the RSP-CC meets the necessary criteria to examine the resilience in parents of children aged 0-18 with a cancer diagnosis, and its psychometric properties are reasonable sufficient.

Measuring resilience by cognitive diagnosis models and its prediction of 6-month quality of life in Be Resilient to Breast Cancer (BRBC)

Objective

The application of advanced Cognitive Diagnosis Models (CDMs) in the Patient Reported Outcome (PRO) is limited due to its complex statistics. This study was designed to measure resilience using CDMs and its prediction of 6-month Quality of Life (QoL) in breast cancer.

Methods

A total of 492 patients were longitudinally enrolled from Be Resilient to Breast Cancer (BRBC) and administered with 10-item Resilience Scale Specific to Cancer (RS-SC-10) and Functional Assessment of Cancer Therapy-Breast (FACT-B). Generalized Deterministic Input, Noisy "And" Gate (G-DINA) was performed to measure cognitive diagnostic probabilities (CDPs) of resilience. Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) were utilized to estimate the incremental prediction value of cognitive diagnostic probabilities over total score.

Results

CDPs of resilience improved prediction of 6-month QoL above conventional total score. AUC increased from 82.6-88.8% to 95.2-96.5% in four cohorts (all P < 0.001). The NRI ranged from 15.13 to 54.01% and IDI ranged from 24.69 to 47.55% (all P < 0.001).

Conclusion

CDPs of resilience contribute to a more accurate prediction of 6-month QoL above conventional total score. CDMs could help optimize Patient Reported Outcomes (PROs) measurement in breast cancer.