A new scoring system for predicting in‐hospital death after lung cancer surgery (the SABCIP score) using a Japanese nationwide administrative database

Abstract

Abstract Background We aimed to develop and validate a new risk scoring tool for predicting in‐hospital mortality after lung cancer surgery. Methods We retrospectively identified patients admitted for lung cancer surgery from a nationwide administrative database in Japan and randomly divided them into derivation and validation cohorts. In the derivation cohort, we performed logistic regression analysis to determine predictive variables and developed a risk scoring tool by proportionally weighting the regression coefficients and assigning points to each variable. In both cohorts, we evaluated the predictive performance of the score using the c‐index and showed the in‐hospital mortality at each risk score. Results In total, 64 175 patients (32 170 and 32 005 patients in the derivation and validation cohort, respectively) were enrolled, including 115 (0.4%) and 119 (0.4%) in‐hospital patient deaths in the derivation and validation cohorts, respectively. Following the multivariate regression analysis, we selected six variables to create the SABCIP score, a risk scoring tool named after the parameters on which it is based, namely male sex, age ≥ 75 years, body mass index <18.5, clinical stage ≥3, interstitial lung disease, and procedure type (sleeve resection, chest wall resection, or pneumonectomy). The c‐index of the score was 0.82 and 0.80 in the derivation and validation cohorts, respectively, which represents a better or equal discrimination performance compared with previous scoring tools. In‐hospital mortality increased as the score increased in both cohorts. Conclusion The SABCIP score is a simple and useful predictor of in‐hospital mortality in patients after lung cancer surgery.

Keywords

• in‐hospital mortality
• lung cancer
• lung resection
• risk modeling
• surgery