Development and validation of nomograms to predict early death for elderly lung cancer patients

Jiafei Li; Qian Zou; Rubing Gu; Fang Wang; Xun Li

doi:10.3389/fsurg.2023.1113863

Development and validation of nomograms to predict early death for elderly lung cancer patients

Front Surg. 2023 Feb 13:10:1113863. doi: 10.3389/fsurg.2023.1113863. eCollection 2023.

Authors

Jiafei Li¹, Qian Zou¹, Rubing Gu¹, Fang Wang¹, Xun Li¹

Affiliation

¹ Department of Respiratory and Critical Care, Medicine, Chuzhou Hospital of Anhui Medical University, Anhui, China.

Abstract

Background: Due to the aging of society, the average age of LC (lung cancer) patients has increased in recent years. The purpose of this study was to determine the risk factors and develop nomograms to predict the probability of early death (dead in three months) for elderly (≥ 75 years old) LC patients.

Methods: Data of elderly LC patients were obtained from the SEER database by using the SEER stat software. All patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. The risk factors of all-cause early and cancer-specific early death were identified by univariate logistic regression and backward stepwise multivariable logistic regression in the training cohort. Then, risk factors were used to construct nomograms. The performance of nomograms was validated by receiver operating curves (ROC), calibration curves, and decision curve analysis (DCA) in the training cohort and validation cohort.

Results: A total of 15,057 elderly LC patients in the SEER database were included in this research and randomly divided into a training cohort (n = 10,541) and a validation cohort (n = 4516). The multivariable logistic regression models found that there were 12 independent risk factors for the all-cause early death and 11 independent risk factors for the cancer-specific early death of the elderly LC patients, which were then integrated into the nomograms. The ROC indicated that the nomograms exhibited high discriminative ability in predicting all-cause early (AUC in training cohort = 0.817, AUC in validation cohort = 0.821) and cancer-specific early death (AUC in training cohort = 0.824, AUC in validation cohort = 0.827). The calibration plots of the nomograms were close to the diagonal line revealing that there was good concordance between the predicted and practical early death probability in the training and validation cohort. Moreover, the results of DCA analysis indicated that the nomograms had good clinical utility in predicting early death probability.

Conclusion: The nomograms were constructed and validated to predict the early death probability of elderly LC patients based on the SEER database. The nomograms were expected to have high predictive ability and good clinical utility, which may help oncologists develop better treatment strategies.

Keywords: SEER database; early death; lung cancer; nomogram; risk factor.