Cachexia-related biomarkers predict shortened survival and treatment-related adverse outcomes in a population receiving palliative chemotherapy for lung cancer
Session type: Poster / e-Poster / Silent Theatre session
Optimal patient selection for palliative chemotherapy for lung cancer poses significant challenges. Several factors are associated with adverse outcomes in lung cancer, including poor performance status (measured by Eastern Cooperative Oncology Group, ECOG PS), low body mass index (BMI), weight loss and systemic inflammation. We sought to identify predictive variables for a range of adverse outcomes in a population receiving palliative chemotherapy for lung cancer.
A retrospective cohort study of patients who received first-line palliative chemotherapy for lung cancer during 2013-2015 in South East Scotland was undertaken. Demographic and clinical data was extracted from electronic health records. Body composition analysis was conducted using diagnostic computed tomography scans to evaluate muscularity (skeletal muscle index, SMI) and muscle density (muscle attenuation, MA). Established thresholds for variables were utilised where available. Where not available, optimal stratification was used to derive discriminatory thresholds for overall survival (OS). Outcome measures included OS and treatment-related adverse events. Only OS is reported here.
Time to event data were analysed using Kaplan-Meier methods and Cox Proportional Hazards regression.
397 patients were included; 259 with non-small cell lung cancer (NSCLC) and 138 small cell lung cancer (SCLC). 295 (80%) had an ECOG PS of 0/1 at diagnosis. Mean BMI was 25.9 (SD=5.3)
Median OS was 215 days (95%CI 191, 239). 191 patients (48%) received fewer than 4 cycles of chemotherapy; their median survival was 112 days (95%CI 97-127), p<0.001.
Independent predictors of reduced OS were baseline NLR ≥4, Albumin <35, SMI and MA. ECOG PS was not a significant predictor of OS. 4 composite models based on independent predictors were explored.
It is possible to identify patients at significant risk of reduced OS and other adverse outcomes at diagnosis. Our predictive models require further validation and could improve patient selection for palliative chemotherapy for lung cancer in the future.