A Practical Nomogram For Predicting Early Death In Elderly Small Cell Lung Cancer Patients: A SEER-based Study

Objective:The aim of this study was to identify risk factors for early death in elderly patients with small cell lung cancer(SCLC)and to construct nomogram prediction models for all-cause early death and cancer-specific early death to help effectively manage this group of patients.Methods:Data of elderly SCLC patients were extracted from the Surveillance,Epidemiology,and End Results(SEER)database.These patients were randomly assigned to a training cohort and a validation cohort.In the training cohort,univariate and backward stepwise multivariable Logistic regression analyses were used to identify independent risk factors for early death in elderly SCLC patients.Nomograms were also constructed based on these factors to predict the overall risk of early death.The efficacy of the nomograms was validated using receiver operating characteristic curve(ROC),calibration curves,decision curve analysis(DCA),net reclassification index(NRI)and integrated discrimination improvement(IDI).Results:Among the 2077 elderly SCLC patients enrolled,773 died within three months of their initial diagnosis,713 of whom died of cancer-specific causes.The analysis showed that higher AJCC stage,brain metastasis,lung metastasis,and failure to receive surgery,chemotherapy,or radiotherapy were associated with an increased risk of all-cause early death and cancer-specific early death(P<0.05).These identified factors were used to construct two nomograms to predict the risk of all-cause and cancer-specific early death.The ROC indicated that the nomograms performed well in predicting both all-cause early death(AUC=0.820 in the training cohort and AUC=0.841 in the validation cohort)and cancer-specific early death(AUC=0.814 in the training cohort and AUC=0.839 in the validation cohort).The results of calibration curves,DCAs,NRI and IDI also showed that the two sets of nomograms had good predictive power and clinical utility and were superior to the commonly used TNM staging system.Conclusions:The nomogram prediction models constructed in this study can effectively assist clinicians in predicting the risk of early death in elderly SCLC patients,and can also help physicians screen patients at higher risk and develop personalized treatment plans for them.