Validation and Application of the Chinese Version of the M. D. Anderson Symptom Inventory in Breast Cancer Patients

Patient-reported outcome based symptom management is a better option for early postoperative recovery after breast cancer surgery: a parallel controlled randomized clinical trial

BACKGROUND

We aimed to assess the efficacy and feasibility of applying patient-reported outcome (PRO) based symptom management in the early postoperative period after breast cancer surgery.

MATERIALS AND METHODS

Before surgery, patients diagnosed with breast cancer who met the inclusion criteria were randomly assigned in a 1:1 ratio to receive either postoperative PRO-based symptom management or usual care. All patients completed the MD Anderson Symptom Inventory-Chinese version (MDASI-C) via the electronic PRO system preoperatively, on a daily basis postoperatively, and twice weekly after discharge, for a duration up to 2 weeks. In the PRO-based care group, in addition to receiving usual care, patients whose symptoms reported by completing MDASI-C with a score of ≥4 will be managed symptomatically by the attending surgeon. Patients in the usual care group received routine care and their MDASI-C scores were unknown to their attending surgeon. The primary outcome was the MDASI-C score of patients at the time of discharge. Analyses were conducted in accordance with the established protocol.

RESULTS

Of the 134 participants, 67 were randomly assigned to each group. At discharge, the total score of MDASI-C scale was significantly higher in the usual care group comapred to the PRO-based care group (median [interquartile range], 22 [19] vs. 35 [36]; P = 0.002). The score of FACT-B scale (adjusted mean difference, 0.39; 95% confidence interval, 0.11-1.06; P = 0.009) was significantly lower in the usual care group than in the PRO-based care group during the 14 days after surgery. In the PRO-based care group, 87.2% of patients found the PRO-based symptom management approach helpful in their early postoperative recovery.

CONCLUSION

The implementation of a PRO-based symptom management system within 2 weeks after breast cancer surgery effectively alleviates symptom burden and improves quality of life compared to usual care.

Navigating specific targets of breast cancer symptoms: An innovative computer-simulated intervention analysis

PURPOSE

To pinpoint optimal interventions by dissecting the complex symptom interactions, encompassing both their static and temporal dimensions.

METHODS

The study incorporated a cross-sectional survey utilizing the MD Anderson Symptom Inventory. Participants with breast cancer undergoing chemotherapy were recruited from the "Be Resilient to Breast Cancer" from April 2023 to June 2024. Static symptom interrelationships were elucidated using undirected and Bayesian network models, complemented by an exploration of their dynamic counterparts through computer-simulated interventions.

RESULTS

The study included 602 patients with breast cancer. Both undirected networks and computer-simulated interventions concurred on the symptoms of distress and fatigue as optimal alleviation targets. The Bayesian network and computer-simulated interventions both emphasized "shortness of breath" as preventive care. Notably, Distress appeared to be the most effective target for interventions, and compared to fatigue (decreasing score = 1.84-2.20, decreasing prevalence = 14.2-16.7%). Conversely, disturbed sleep, despite its high position in Bayesian network, had no propelling effects on increasing the network's overall symptom activity levels (increasing score<1).

CONCLUSIONS

Computer-simulated intervention integrating with traditional network analysis can improve intervention precision and efficacy by prioritizing individual symptom impacts, both statically and dynamically.

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.