Development of a nomogram for predicting the operative mortality of patients who underwent pneumonectomy for lung cancer: a population-based analysis.

BACKGROUND

Although many studies have reported that patients have undergone entire lung removal for lung cancer along with high operative mortality, the trends in the incidence and associated risk factors for operative death have not been explored in a national population-based study. In addition, a clinical decision-making nomogram for predicting postpneumonectomy mortality remains lacking.

METHODS

A total of 10,337 patients diagnosed with lung cancer who underwent pneumonectomy between 1998 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) cancer registry. Multivariate logistic regression analysis was used to identify risk factors for predicting operative mortality. Thereafter, these independent predictors were integrated into a nomogram, and bootstrap validation was applied to assess the discrimination and calibration. Additionally, decision curve analysis (DCA) was used to calculate the net benefit of this forecast model.

RESULTS

The overall postpneumonectomy mortality between 1998 and 2016 was 10.3%, including a 30-day mortality of 4.2%; however, there were statistically significant decreases in the operative death rates from 8.8% in 1998 to 6.7% in 2016 (P=0.009). Higher operative mortality was associated with advanced patients (P<0.001), male sex (P<0.001), right-sided pneumonectomy (P<0.001), squamous cell carcinoma (SCC) (P=0.008), number of positive lymph nodes (npLNs) 5 or greater (P=0.010), and distant metastasis (P<0.001). However, induction radiotherapy (RT) was a protective factor (P<0.001). The nomogram integrating all of the above independent predictors was well calibrated and had a relatively good discriminative ability, with a C-statistic of 0.687 and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.682; moreover, DCA demonstrated that our model was clinically useful.

CONCLUSIONS

If pneumonectomy was considered inevitable, clinical decision-making based on this simple but efficient predictive nomogram could help minimize the risk of operative death and maximize the survival benefit.