Risk Factors and Predictive Nomograms for Early Death of Patients with Advanced Hepatocellular Carcinoma: A Large Retrospective Study Based on the SEER Database

Abstract Background Hepatocellular carcinoma (HCC) is a kind of tumor with insidious early symptoms and high invasiveness, and it is often in the advanced stage when clinically discovered. Patients with advanced HCC have a higher risk of early death (survival time ≤ 3 months). This study aims to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms. Methods Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database for model establishment and verification. The univariate and multivariate logistic regression analyses were used to identify the risk factors, which were used to construct nomograms. The concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the accuracy of the models. The calibration curves and the decision curves analysis (DCA) were used to verify the true consistency and clinical application value of the models. Internal validation was performed using bootstrapping (1000 re-samplings) and cross-validation (k = 10). Results A total of 6799 patients were selected from the SEER database. After strict screening conditions, 1392 patients with stage IVA HCC and 5,211 patients with stage IVB HCC were finally identified. 621 (44.6%) patients in stage IVA and 3271 (62.8%) patients in stage IVB experienced early death. 9 risk factors related to early death of patients with stage IVA and 10 risk factors related to early death of patients with stage IVB were finally identified. Reliable nomograms were constructed using the above risk factors. Internal verification showed that the nomograms had good accuracy for predicting early death. The DCA showed that the nomograms had good clinical applicability. Conclusion The nomograms are helpful for clinicians and oncologists to identify the risk factors for early death of patients with advanced HCC and predict the probability of early death, so as to accurately select individualized treatment plans.