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

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.