Development and validation of a diagnostic nomogram for frailty in cancer patients

The presence of frailty decreases the overall survival of cancer patients. An accurate and operational diagnostic method is needed to help clinicians choose the most appropriate treatment to improve patient outcomes.

Data were collected from 10 649 cancer patients who were prospectively enrolled in the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers (INSCOC) project in China from July 2013 to August 2022. The training cohort and validation cohort were randomly divided at a ratio of 7:3. The multivariable logistic regression analysis, multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO) method were used to develop the nomogram. The concordance index and calibration curve were used to assess the diagnostic utility of the nomogram model.

The 10 risk factors associated with frailty in cancer patients were age, AJCC stage, liver cancer, hemoglobin, radiotherapy, surgery, hand grip strength (HGS), calf circumference (CC), PG-SGA score and QOL from the QLQ-C30. The diagnostic nomogram model achieved a good C index of 0.847 (95% CI, 0.832-0.862, P < 0.001) in the training cohort and 0.853 (95% CI, 0.83-0.876, P < 0.001) in the validation cohort. The prediction nomogram showed 1-, 3-, and 5-year mortality C indices in the training cohort of 0.708 (95% CI, 0.686-0.731), 0.655 (95% CI, 0.627-0.683), and 0.623 (95% CI, 0.568-0.678). The 1-, 3-, and 5-year C indices in the validation cohort were similarly 0.743 (95% CI, 0.711-0.777), 0.680 (95% CI, 0.639-0.722), and 0.629 (95% CI, 0.558-0.700). In addition, the calibration curves and decision curve analysis (DCA) were well-fitted for both the diagnostic model and prediction model.

The nomogram model provides an accurate method to diagnose frailty in cancer patients. Using this model could lead to the selection of more appropriate therapy and a better prognosis for cancer patients.