A qualitative research exploring the experiences of patients receiving immune checkpoint inhibitors for advanced lung cancer.

PURPOSES: In patients with advanced lung cancer, immune checkpoint inhibitors (ICIs) dramatically extended survival. We aimed to investigate the experiences of patients with advanced lung cancer who are receiving ICIs, with a focus on perceptions and sentiments on ICIs, to inform future research and clinical care. METHODS: Patients were recruited from January to July 2022 at Tianjin Medical University Hospital Cancer Institute & Hospital. The method of purposive sampling was used. Patients with stage IV lung cancer who were taking ICIs as a single therapy were recruited. Data were gathered using semi-structured and face-to-face interview. An inductive approach to analysis was used. RESULTS: Of the 42 eligible patients, 27 were invited, 20 agreed to participate, and ultimately 17 patients completed the interview. A total of 5 themes were extracted: suffered from adverse effects but generally tolerable; focus on survival, hope, and expectation; uncertainty about durability of response and the future; poor knowledge and attitude of ICIs; and financial stress and guilt over family. CONCLUSION: These findings make an important contribution to healthcare professionals' understanding of what it is like to be a patient with advanced lung cancer who is receiving ICIs. In general, the experience of immunotherapy is different from that of chemotherapy; financial and family pressures and uncertainty issues are likely to be heavier in this population. Oncology specialist nurses should provide tailored education to improve the patient's knowledge of immunotherapy, especially to improve risk awareness of immune-related adverse events and objective prognosis expectations.

The construction and validation of a frailty risk prediction model for older adults with lung cancer: A cross-sectional study.

OBJECTIVE: We aimed to construct and internally validate a frailty risk prediction model in older adults with lung cancer. METHOD: In total, 538 patients were recruited in a grade A tertiary cancer hospital in Tianjin, and patients were randomly divided into the training group (n = 377) and the testing group (n = 166) at a ratio of 7:3. The Frailty Phenotype scale was used to identify frailty and logistic regression analysis was used to identify the risk factors and establish a frailty risk prediction model. RESULTS: In the training group, logistic regression showed that age, fatigue-related symptom cluster, depression, nutritional status, D-dimer level, albumin level, presence of comorbidities, and disease course were independent risk factors for frailty. The areas under the curve (AUCs) of the training and testing groups were 0.921 and 0.872, respectively. A calibration curve of P = 0.447 validated model calibration. The decision curve analysis demonstrated greater clinical benefit when the threshold probability was >20%. CONCLUSION: The prediction model had a favorable prediction power for determining the risk of frailty, contributing to the prevention and screening of frailty. Patients with a frailty risk score of more than 0.374 should be regularly monitored for frailty and receive personalized preventive interventions.