Background: Primary malignant cardiac tumors (PMCTs) are correlated with an unfavourable prognosis. The aim of the current study was to establish and validate a nomogram model for 3-month mortality prediction for patients with PMCT.
Methods: A total of 638 PMCT patients diagnosed between 1975 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database were randomly enrolled and assigned into a training cohort (N=448) and validation cohort (N=190). Early mortality cases were analyzed, and related risk factors were identified by logistic regression models, and significant risk factors were used to establish a predictive nomogram model. The predictive capability of the model was validated by calibration analysis and receiver operating curve (ROC) in both training and validation cohorts.
Results: Multivariate logistic analysis revealed the independent risk factors for early mortality were old age, chemotherapy, surgery, and tumor stage, and these were used to construct the nomogram. In terms of calibration and discrimination, both the internal and external validation calibration curves revealed consistency between the nomogram prediction and the actual observation. The area under the curve (AUC) of the nomogram for 3-month mortality in the internal and external validation was 0.816 and 0.805, respectively.
Conclusions: Old age and advanced tumor stage are involved in higher odds of early mortality, while surgery and chemotherapy could reduce this. The nomogram model provides an accurate, user-friendly, and reproducible tool for predicting early mortality in PMCT patients.
Keywords: Nomogram; early mortality; primary malignant cardiac tumor (PMCT).
2021 Annals of Translational Medicine. All rights reserved.