Nurse-led remote symptom management for chemotherapy toxicity and quality of life in lymphoma: a quasi-experimental study.

BACKGROUND

Chemotherapy for lymphoma often induces distressing toxicities that impair treatment adherence and quality of life (QoL). With increasing demand for decentralized cancer care, nurse-led telehealth interventions offer a promising approach to support symptom management.

OBJECTIVE

This study aimed to evaluate the effectiveness of a nurse-led remote symptom management model in reducing chemotherapy-related toxicities and improving QoL among patients with newly diagnosed lymphoma in China.

METHODS

A quasi-experimental study was conducted in a tertiary oncology center. Patients newly diagnosed with lymphoma and undergoing ≥ 6 cycles of chemotherapy were enrolled between July 2022 and December 2023. Participants receiving care between July 2022 and June 2023 formed the intervention group (N = 286), while those from July to December 2023 constituted the control group (N = 143). The intervention comprised structured telephone follow-ups by oncology nurses using CTCAE v5.0 criteria and a graded management protocol. Primary outcomes were incidences of grade ≥ 2 toxicities. Secondary outcomes included changes in QoL measured by the EORTC QLQ-C30. Generalized estimating equations (GEE) were used to analyze longitudinal data.

RESULTS

Compared to the control group, the intervention group demonstrated significantly lower odds of fatigue (OR = 0.62), nausea (OR = 0.56), vomiting (OR = 0.34), and constipation (OR = 0.30; all P < 0.01). No significant differences were observed for diarrhea, fever, or pain. QoL analysis showed improvements in global health status (β = 3.73), social functioning (β = 6.02), and financial difficulties (β = -6.07).

CONCLUSION

Nurse-led remote symptom management significantly reduced core chemotherapy-related toxicities and enhanced QoL in patients with lymphoma. These findings support the integration of structured remote symptom management into oncology care models in resource-constrained settings.